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Good evening, hackers. Today's missive is more of a massive, in the sense that it's another presentation transcript-alike; these things always translate to many vertical pixels.

In my defense, I hardly ever give a presentation twice, so not only do I miss out on the usual per-presentation cost amortization and on the incremental improvements of repetition, the more dire error is that whatever message I might have can only ever reach a subset of those that it might interest; here at least I can be more or less sure that if the presentation would interest someone, that they will find it.

So for the time being I will try to share presentations here, in the spirit of, well, why the hell not.

CPS Soup

A functional intermediate language

10 May 2023 – Spritely

Andy Wingo

Igalia, S.L.

Last week I gave a training talk to Spritely Institute collaborators on the intermediate representation used by Guile's compiler.

CPS Soup

Compiler: Front-end to Middle-end to Back-end

Middle-end spans gap between high-level source code (AST) and low-level machine code

Programs in middle-end expressed in intermediate language

CPS Soup is the language of Guile’s middle-end

An intermediate representation (IR) (or intermediate language, IL) is just another way to express a computer program. Specifically it's the kind of language that is appropriate for the middle-end of a compiler, and by "appropriate" I meant that an IR serves a purpose: there has to be a straightforward transformation to the IR from high-level abstract syntax trees (ASTs) from the front-end, and there has to be a straightforward translation from IR to machine code.

There are also usually a set of necessary source-to-source transformations on IR to "lower" it, meaning to make it closer to the back-end than to the front-end. There are usually a set of optional transformations to the IR to make the program run faster or allocate less memory or be more simple: these are the optimizations.

"CPS soup" is Guile's IR. This talk presents the essentials of CPS soup in the context of more traditional IRs.

How to lower?

High-level:

(+ 1 (if x 42 69))

Low-level:

cmpi $x, #f je L1 movi $t, 42 j L2 L1: movi $t, 69 L2: addi $t, 1

How to get from here to there?

Before we dive in, consider what we might call the dynamic range of an intermediate representation: we start with what is usually an algebraic formulation of a program and we need to get down to a specific sequence of instructions operating on registers (unlimited in number, at this stage; allocating to a fixed set of registers is a back-end concern), with explicit control flow between them. What kind of a language might be good for this? Let's attempt to answer the question by looking into what the standard solutions are for this problem domain.

1970s

Control-flow graph (CFG)

graph := array<block> block := tuple<preds, succs, insts> inst := goto B | if x then BT else BF | z = const C | z = add x, y ... BB0: if x then BB1 else BB2 BB1: t = const 42; goto BB3 BB2: t = const 69; goto BB3 BB3: t2 = addi t, 1; ret t2

Assignment, not definition

Of course in the early days, there was no intermediate language; compilers translated ASTs directly to machine code. It's been a while since I dove into all this but the milestone I have in my head is that it's the 70s when compiler middle-ends come into their own right, with Fran Allen's work on flow analysis and optimization.

In those days the intermediate representation for a compiler was a graph of basic blocks, but unlike today the paradigm was assignment to locations rather than definition of values. By that I mean that in our example program, we get t assigned to in two places (BB1 and BB2); the actual definition of t is implicit, as a storage location, and our graph consists of assignments to the set of storage locations in the program.

1980s

Static single assignment (SSA) CFG

graph := array<block> block := tuple<preds, succs, phis, insts> phi := z := φ(x, y, ...) inst := z := const C | z := add x, y ... BB0: if x then BB1 else BB2 BB1: v0 := const 42; goto BB3 BB2: v1 := const 69; goto BB3 BB3: v2 := φ(v0,v1); v3:=addi t,1; ret v3

Phi is phony function: v2 is v0 if coming from first predecessor, or v1 from second predecessor

These days we still live in Fran Allen's world, but with a twist: we no longer model programs as graphs of assignments, but rather graphs of definitions. The introduction in the mid-80s of so-called "static single-assignment" (SSA) form graphs mean that instead of having two assignments to t, we would define two different values v0 and v1. Then later instead of reading the value of the storage location associated with t, we define v2 to be either v0 or v1: the former if we reach the use of t in BB3 from BB1, the latter if we are coming from BB2.

If you think on the machine level, in terms of what the resulting machine code will be, this either function isn't a real operation; probably register allocation will put v0, v1, and v2 in the same place, say $rax. The function linking the definition of v2 to the inputs v0 and v1 is purely notational; in a way, you could say that it is phony, or not real. But when the creators of SSA went to submit this notation for publication they knew that they would need something that sounded more rigorous than "phony function", so they instead called it a "phi" (φ) function. Really.

2003: MLton

Refinement: phi variables are basic block args

graph := array<block> block := tuple<preds, succs, args, insts>

Inputs of phis implicitly computed from preds

BB0(a0): if a0 then BB1() else BB2() BB1(): v0 := const 42; BB3(v0) BB2(): v1 := const 69; BB3(v1) BB3(v2): v3 := addi v2, 1; ret v3

SSA is still where it's at, as a conventional solution to the IR problem. There have been some refinements, though. I learned of one of them from MLton; I don't know if they were first but they had the idea of interpreting phi variables as arguments to basic blocks. In this formulation, you don't have explicit phi instructions; rather the "v2 is either v1 or v0" property is expressed by v2 being a parameter of a block which is "called" with either v0 or v1 as an argument. It's the same semantics, but an interesting notational change.

Refinement: Control tail

Often nice to know how a block ends (e.g. to compute phi input vars)

graph := array<block> block := tuple<preds, succs, args, insts, control> control := if v then L1 else L2 | L(v, ...) | switch(v, L1, L2, ...) | ret v

One other refinement to SSA is to note that basic blocks consist of some number of instructions that can define values or have side effects but which otherwise exhibit fall-through control flow, followed by a single instruction that transfers control to another block. We might as well store that control instruction separately; this would let us easily know how a block ends, and in the case of phi block arguments, easily say what values are the inputs of a phi variable. So let's do that.

Refinement: DRY

Block successors directly computable from control

Predecessors graph is inverse of successors graph

graph := array<block> block := tuple<args, insts, control>

Can we simplify further?

At this point we notice that we are repeating ourselves; the successors of a block can be computed directly from the block's terminal control instruction. Let's drop those as a distinct part of a block, because when you transform a program it's unpleasant to have to needlessly update something in two places.

While we're doing that, we note that the predecessors array is also redundant, as it can be computed from the graph of block successors. Here we start to wonder: am I simpliying or am I removing something that is fundamental to the algorithmic complexity of the various graph transformations that I need to do? We press on, though, hoping we will get somewhere interesting.

Basic blocks are annoying

Ceremony about managing insts; array or doubly-linked list?

Nonuniformity: “local” vs ‘`global’' transformations

Optimizations transform graph A to graph B; mutability complicates this task

• Desire to keep A in mind while making B
• Bugs because of spooky action at a distance

Recall that the context for this meander is Guile's compiler, which is written in Scheme. Scheme doesn't have expandable arrays built-in. You can build them, of course, but it is annoying. Also, in Scheme-land, functions with side-effects are conventionally suffixed with an exclamation mark; after too many of them, both the writer and the reader get fatigued. I know it's a silly argument but it's one of the things that made me grumpy about basic blocks.

If you permit me to continue with this introspection, I find there is an uneasy relationship between instructions and locations in an IR that is structured around basic blocks. Do instructions live in a function-level array and a basic block is an array of instruction indices? How do you get from instruction to basic block? How would you hoist an instruction to another basic block, might you need to reallocate the block itself?

And when you go to transform a graph of blocks... well how do you do that? Is it in-place? That would be efficient; but what if you need to refer to the original program during the transformation? Might you risk reading a stale graph?

It seems to me that there are too many concepts, that in the same way that SSA itself moved away from assignment to a more declarative language, that perhaps there is something else here that might be more appropriate to the task of a middle-end.

Basic blocks, phi vars redundant

Blocks: label with args sufficient; “containing” multiple instructions is superfluous

Unify the two ways of naming values: every var is a phi

graph := array<block> block := tuple<args, inst> inst := L(expr) | if v then L1() else L2() ... expr := const C | add x, y ...

I took a number of tacks here, but the one I ended up on was to declare that basic blocks themselves are redundant. Instead of containing an array of instructions with fallthrough control-flow, why not just make every instruction a control instruction? (Yes, there are arguments against this, but do come along for the ride, we get to a funny place.)

While you are doing that, you might as well unify the two ways in which values are named in a MLton-style compiler: instead of distinguishing between basic block arguments and values defined within a basic block, we might as well make all names into basic block arguments.

Arrays annoying

Array of blocks implicitly associates a label with each block

Optimizations add and remove blocks; annoying to have dead array entries

Keep labels as small integers, but use a map instead of an array

graph := map<label, block>

In the traditional SSA CFG IR, a graph transformation would often not touch the structure of the graph of blocks. But now having given each instruction its own basic block, we find that transformations of the program necessarily change the graph. Consider an instruction that we elide; before, we would just remove it from its basic block, or replace it with a no-op. Now, we have to find its predecessor(s), and forward them to the instruction's successor. It would be useful to have a more capable data structure to represent this graph. We might as well keep labels as being small integers, but allow for sparse maps and growth by using an integer-specialized map instead of an array.

This is CPS soup graph := map<label, cont> cont := tuple<args, term> term := continue to L with values from expr | if v then L1() else L2() ... expr := const C | add x, y ...

SSA is CPS

This is exactly what CPS soup is! We came at it "from below", so to speak; instead of the heady fumes of the lambda calculus, we get here from down-to-earth basic blocks. (If you prefer the other way around, you might enjoy this article from a long time ago.) The remainder of this presentation goes deeper into what it is like to work with CPS soup in practice.

Scope and dominators BB0(a0): if a0 then BB1() else BB2() BB1(): v0 := const 42; BB3(v0) BB2(): v1 := const 69; BB3(v1) BB3(v2): v3 := addi v2, 1; ret v3

What vars are “in scope” at BB3? a0 and v2.

Not v0; not all paths from BB0 to BB3 define v0.

a0 always defined: its definition dominates all uses.

BB0 dominates BB3: All paths to BB3 go through BB0.

Before moving on, though, we should discuss what it means in an SSA-style IR that variables are defined rather than assigned. If you consider variables as locations to which values can be assigned and which initially hold garbage, you can read them at any point in your program. You might get garbage, though, if the variable wasn't assigned something sensible on the path that led to reading the location's value. It sounds bonkers but it is still the C and C++ semantic model.

If we switch instead to a definition-oriented IR, then a variable never has garbage; the single definition always precedes any uses of the variable. That is to say that all paths from the function entry to the use of a variable must pass through the variable's definition, or, in the jargon, that definitions dominate uses. This is an invariant of an SSA-style IR, that all variable uses be dominated by their associated definition.

You can flip the question around to ask what variables are available for use at a given program point, which might be read equivalently as which variables are in scope; the answer is, all definitions from all program points that dominate the use site. The "CPS" in "CPS soup" stands for continuation-passing style, a dialect of the lambda calculus, which has also has a history of use as a compiler intermediate representation. But it turns out that if we use the lambda calculus in its conventional form, we end up needing to maintain a lexical scope nesting at the same time that we maintain the control-flow graph, and the lexical scope tree can fail to reflect the dominator tree. I go into this topic in more detail in an old article, and if it interests you, please do go deep.

CPS soup in Guile

Compilation unit is intmap of label to cont

cont := $kargs names vars term | ... term := $continue k src expr | ... expr := $const C | $primcall ’add #f (a b) | ...

Conventionally, entry point is lowest-numbered label

Anyway! In Guile, the concrete form that CPS soup takes is that a program is an intmap of label to cont. A cont is the smallest labellable unit of code. You can call them blocks if that makes you feel better. One kind of cont, $kargs, binds incoming values to variables. It has a list of variables, vars, and also has an associated list of human-readable names, names, for debugging purposes.

A $kargs contains a term, which is like a control instruction. One kind of term is $continue, which passes control to a continuation k. Using our earlier language, this is just goto *k*, with values, as in MLton. (The src is a source location for the term.) The values come from the term's expr, of which there are a dozen kinds or so, for example $const which passes a literal constant, or $primcall, which invokes some kind of primitive operation, which above is add. The primcall may have an immediate operand, in this case #f, and some variables that it uses, in this case a and b. The number and type of the produced values is a property of the primcall; some are just for effect, some produce one value, some more.

CPS soup term := $continue k src expr | $branch kf kt src op param args | $switch kf kt* src arg | $prompt k kh src escape? tag | $throw src op param args

Expressions can have effects, produce values

expr := $const val | $primcall name param args | $values args | $call proc args | ...

There are other kinds of terms besides $continue: there is $branch, which proceeds either to the false continuation kf or the true continuation kt depending on the result of performing op on the variables args, with immediate operand param. In our running example, we might have made the initial term via:

(build-term ($branch BB1 BB2 'false? #f (a0)))

The definition of build-term (and build-cont and build-exp) is in the (language cps) module.

There is also $switch, which takes an unboxed unsigned integer arg and performs an array dispatch to the continuations in the list kt, or kf otherwise.

There is $prompt which continues to its k, having pushed on a new continuation delimiter associated with the var tag; if code aborts to tag before the prompt exits via an unwind primcall, the stack will be unwound and control passed to the handler continuation kh. If escape? is true, the continuation is escape-only and aborting to the prompt doesn't need to capture the suspended continuation.

Finally there is $throw, which doesn't continue at all, because it causes a non-resumable exception to be thrown. And that's it; it's just a handful of kinds of term, determined by the different shapes of control-flow (how many continuations the term has).

When it comes to values, we have about a dozen expression kinds. We saw $const and $primcall, but I want to explicitly mention $values, which simply passes on some number of values. Often a $values expression corresponds to passing an input to a phi variable, though $kargs vars can get their definitions from any expression that produces the right number of values.

Kinds of continuations

Guile functions untyped, can multiple return values

Error if too few values, possibly truncate too many values, possibly cons as rest arg...

Calling convention: contract between val producer & consumer

• both on call and return side

Continuation of $call unlike that of $const

When a $continue term continues to a $kargs with a $const 42 expression, there are a number of invariants that the compiler can ensure: that the $kargs continuation is always passed the expected number of values, that the vars that it binds can be allocated to specific locations (e.g. registers), and that because all predecessors of the $kargs are known, that those predecessors can place their values directly into the variable's storage locations. Effectively, the compiler determines a custom calling convention between each $kargs and its predecessors.

Consider the $call expression, though; in general you don't know what the callee will do to produce its values. You don't even generally know that it will produce the right number of values. Therefore $call can't (in general) continue to $kargs; instead it continues to $kreceive, which expects the return values in well-known places. $kreceive will check that it is getting the right number of values and then continue to a $kargs, shuffling those values into place. A standard calling convention defines how functions return values to callers.

The conts cont := $kfun src meta self ktail kentry | $kclause arity kbody kalternate | $kargs names syms term | $kreceive arity kbody | $ktail

$kclause, $kreceive very similar

Continue to $ktail: return

$call and return (and $throw, $prompt) exit first-order flow graph

Of course, a $call expression could be a tail-call, in which case it would continue instead to $ktail, indicating an exit from the first-order function-local control-flow graph.

The calling convention also specifies how to pass arguments to callees, and likewise those continuations have a fixed calling convention; in Guile we start functions with $kfun, which has some metadata attached, and then proceed to $kclause which bridges the boundary between the standard calling convention and the specialized graph of $kargs continuations. (Many details of this could be tweaked, for example that the case-lambda dispatch built-in to $kclause could instead dispatch to distinct functions instead of to different places in the same function; historical accidents abound.)

As a detail, if a function is well-known, in that all its callers are known, then we can lighten the calling convention, moving the argument-count check to callees. In that case $kfun continues directly to $kargs. Similarly for return values, optimizations can make $call continue to $kargs, though there is still some value-shuffling to do.

High and low

CPS bridges AST (Tree-IL) and target code

High-level: vars in outer functions in scope

Closure conversion between high and low

Low-level: Explicit closure representations; access free vars through closure

CPS soup is the bridge between parsed Scheme and machine code. It starts out quite high-level, notably allowing for nested scope, in which expressions can directly refer to free variables. Variables are small integers, and for high-level CPS, variable indices have to be unique across all functions in a program. CPS gets lowered via closure conversion, which chooses specific representations for each closure that remains after optimization. After closure conversion, all variable access is local to the function; free variables are accessed via explicit loads from a function's closure.

Optimizations at all levels

Optimizations before and after lowering

Some exprs only present in one level

Some high-level optimizations can merge functions (higher-order to first-order)

Because of the broad remit of CPS, the language itself has two dialects, high and low. The high level dialect has cross-function variable references, first-class abstract functions (whose representation hasn't been chosen), and recursive function binding. The low-level dialect has only specific ways to refer to functions: labels and specific closure representations. It also includes calls to function labels instead of just function values. But these are minor variations; some optimization and transformation passes can work on either dialect.

Practicalities

Intmap, intset: Clojure-style persistent functional data structures

Program: intmap<label,cont>

Optimization: program→program

Identify functions: (program,label)→intset<label>

Edges: intmap<label,intset<label>>

Compute succs: (program,label)→edges

Compute preds: edges→edges

I mentioned that programs were intmaps, and specifically in Guile they are Clojure/Bagwell-style persistent functional data structures. By functional I mean that intmaps (and intsets) are values that can't be mutated in place (though we do have the transient optimization).

I find that immutability has the effect of deploying a sense of calm to the compiler hacker -- I don't need to worry about data structures changing out from under me; instead I just structure all the transformations that you need to do as functions. An optimization is just a function that takes an intmap and produces another intmap. An analysis associating some data with each program label is just a function that computes an intmap, given a program; that analysis will never be invalidated by subsequent transformations, because the program to which it applies will never be mutated.

This pervasive feeling of calm allows me to tackle problems that I wouldn't have otherwise been able to fit into my head. One example is the novel online CSE pass; one day I'll either wrap that up as a paper or just capitulate and blog it instead.

Flow analysis A[k] = meet(A[p] for p in preds[k]) - kill[k] + gen[k]

Compute available values at labels:

• A: intmap<label,intset<val>>
• meet: intmap-intersect<intset-intersect>
• -, +: intset-subtract, intset-union
• kill[k]: values invalidated by cont because of side effects
• gen[k]: values defined at k

But to keep it concrete, let's take the example of flow analysis. For example, you might want to compute "available values" at a given label: these are the values that are candidates for common subexpression elimination. For example if a term is dominated by a car x primcall whose value is bound to v, and there is no path from the definition of V to a subsequent car x primcall, we can replace that second duplicate operation with $values (v) instead.

There is a standard solution for this problem, which is to solve the flow equation above. I wrote about this at length ages ago, but looking back on it, the thing that pleases me is how easy it is to decompose the task of flow analysis into manageable parts, and how the types tell you exactly what you need to do. It's easy to compute an initial analysis A, easy to define your meet function when your maps and sets have built-in intersect and union operators, easy to define what addition and subtraction mean over sets, and so on.

Persistent data structures FTW

• meet: intmap-intersect<intset-intersect>
• -, +: intset-subtract, intset-union

Naïve: O(nconts * nvals)

Structure-sharing: O(nconts * log(nvals))

Computing an analysis isn't free, but it is manageable in cost: the structure-sharing means that meet is usually trivial (for fallthrough control flow) and the cost of + and - is proportional to the log of the problem size.

CPS soup: strengths

Relatively uniform, orthogonal

Facilitates functional transformations and analyses, lowering mental load: “I just have to write a function from foo to bar; I can do that”

Encourages global optimizations

Some kinds of bugs prevented by construction (unintended shared mutable state)

We get the SSA optimization literature

Well, we're getting to the end here, and I want to take a step back. Guile has used CPS soup as its middle-end IR for about 8 years now, enough time to appreciate its fine points while also understanding its weaknesses.

On the plus side, it has what to me is a kind of low cognitive overhead, and I say that not just because I came up with it: Guile's development team is small and not particularly well-resourced, and we can't afford complicated things. The simplicity of CPS soup works well for our development process (flawed though that process may be!).

I also like how by having every variable be potentially a phi, that any optimization that we implement will be global (i.e. not local to a basic block) by default.

Perhaps best of all, we get these benefits while also being able to use the existing SSA transformation literature. Because CPS is SSA, the lessons learned in SSA (e.g. loop peeling) apply directly.

CPS soup: weaknesses

Pointer-chasing, indirection through intmaps

Heavier than basic blocks: more control-flow edges

Names bound at continuation only; phi predecessors share a name

Over-linearizes control, relative to sea-of-nodes

Overhead of re-computation of analyses

CPS soup is not without its drawbacks, though. It's not suitable for JIT compilers, because it imposes some significant constant-factor (and sometimes algorithmic) overheads. You are always indirecting through intmaps and intsets, and these data structures involve significant pointer-chasing.

Also, there are some forms of lightweight flow analysis that can be performed naturally on a graph of basic blocks without looking too much at the contents of the blocks; for example in our available variables analysis you could run it over blocks instead of individual instructions. In these cases, basic blocks themselves are an optimization, as they can reduce the size of the problem space, with corresponding reductions in time and memory use for analyses and transformations. Of course you could overlay a basic block graph on top of CPS soup, but it's not a well-worn path.

There is a little detail that not all phi predecessor values have names, since names are bound at successors (continuations). But this is a detail; if these names are important, little $values trampolines can be inserted.

Probably the main drawback as an IR is that the graph of conts in CPS soup over-linearizes the program. There are other intermediate representations that don't encode ordering constraints where there are none; perhaps it would be useful to marry CPS soup with sea-of-nodes, at least during some transformations.

Finally, CPS soup does not encourage a style of programming where an analysis is incrementally kept up to date as a program is transformed in small ways. The result is that we end up performing much redundant computation within each individual optimization pass.

Recap

CPS soup is SSA, distilled

Labels and vars are small integers

Programs map labels to conts

Conts are the smallest labellable unit of code

Conts can have terms that continue to other conts

Compilation simplifies and lowers programs

Wasm vs VM backend: a question for another day :)

But all in all, CPS soup has been good for Guile. It's just SSA by another name, in a simpler form, with a functional flavor. Or, it's just CPS, but first-order only, without lambda.

In the near future, I am interested in seeing what a new GC will do for CPS soup; will bump-pointer allocation palliate some of the costs of pointer-chasing? We'll see. A tricky thing about CPS soup is that I don't think that anyone else has tried it in other languages, so it's hard to objectively understand its characteristics independent of Guile itself.

Finally, it would be nice to engage in the academic conversation by publishing a paper somewhere; I would like to see interesting criticism, and blog posts don't really participate in the citation graph. But in the limited time available to me, faced with the choice between hacking on something and writing a paper, it's always been hacking, so far :)

Speaking of limited time, I probably need to hit publish on this one and move on. Happy hacking to all, and until next time.

This is the latest installment of our Licensing and Compliance Lab's series on free software developers who choose GNU licenses for their works.

ADAV-Weimar (Afghan-German Doctors Association) and GNU Solidario have formalized a agreement to implement GNU Health for local and remote physicians to improve the medical care of the people in Afghanistan.

ADAV-Weimar (Afghan-German Doctors Association) is a registered voluntary association in Germany, founded in 2004 and counts with the support of over 150 health professionals from around the world. The organization provides scientific and practical help with establishing medical facilities and efficient healthcare in Afghanistan. It supports with building small but efficient Special Clinics and practices knowledge transfer by training Afghani doctors and medical staff and providing telemedicine. Furthermore, ADAV Weimar in co-operation with German E-Learning specialist Lecturio and partner Universities in Afghanistan has established an E-Learning program for medical students that provides free access to content prepared by awarded lectures from world class. Thus, ADAV-Weimar has become an inherent part of the international relations of all medical faculties in Afghanistan.

Dr. Luis Falcon, president of GNU Solidario, and Dr. Azim Mosafer, chairman of ADAV-Weimar e.V, signed this past April an initial three-year agreement to setup GNU Health Hospital Management systems, where physicians both from Afghanistan and abroad can work together to improve the healthcare and lives of the Afghan women, men and children.

Dr. Azim Mosafer – an Afghan-born German spine surgeon – is the head of the German-Afghan Doctors Association and currently involved in a European-Afghani telemedicine project. In a recent interview, Dr. Mosafer, who travels once a year to Afghanistan stressed the importance of telemedicine, at a national level and also within the country, to also provide medical care to people living in rural areas.

Since 2005 I have traveled to Afghanistan once a year for two weeks, to provide medical care. However, as a result of the high security risks in Afghanistan, fewer and fewer of my colleagues were willing to accompany me. We also began to think about doing something more effective. Even within Afghanistan, people have to travel great distances and overcome geographic obstacles in order to obtain medical care. With telemedicine such geographic hurdles can by bypassed internationally, but also inside the country.

Dr. Azim Mosafer, interview on AO spine. Source ADAV Weimar

The project has already started and the initial GNU Health Hospital Management training provided to the ADAV Weimar personnel from Germany. Initially, the implementation will provide the GNU Health Hospital Management System with telemedicine support and functionality such as:

• General Practice
• Family Medicine
• Surgery
• Laboratory
• Ophthalmology
• Medical Imaging
• Laboratory
• Odontology
• Gynecology and Obstetrics
• Pediatrics
• Health, Functioning and Disability

Source ADAV Weimar e.V.

In GNU Solidario, we are proud to cooperate with ADAV Weimar implementing the GNU Health ecosystem. We are excited to provide the latest technology in health informatics to the betterment of science and society. The European doctors will be able to cooperate with the local health professionals in providing the best clinical assessment and medical care possible to the Afghan children, women and men, specially in this difficult times. It is now when they need it most.

Dr. Luis Falcon

This is just the beginning of the journey. We are confident that other components from the Libre digital health ecosystem, such as the GNU Health Federation and MyGNUHealth Personal Health Record will further help health professionals in Afghanistan and Europe the best tools for cooperation, knowledge transfer and medical care to the people in Afghanistan.

Resources:

• Afghan-European Telemedicine: https://www.afghan-telemedicine.org/en/home
• GNU Solidario: https://www.gnusolidario.org
• GNU Health: https://www.gnuhealth.org

IRPF-Livre 2023 released

Governments come and go, but the oppression of imposed taxing software for taxation remains.

For a lot of people, using software they cannot control is like water for a fish: a part of the environment they're in. When the water is low on oxygen, they may even feel the discomfort, but they seldom trace it back to the root cause.

For us who love, live and breathe software freedom, any program that takes it away, that attempts to control our computing and ultimately ourselves, is painful like a sore toe in a tight shoe.

Uncomfortable and painful as the oxygen-deprived water and the tight shoe might be, being forced to breathe or wear them, prevented from seeking better waters or from taking the shoes out, is unbearable.

We struggle to correct an analogous injustice. We had a chance to relieve one case of imposed taxing software for taxation, so we took it, and held on to it:

Back in 2007, IRPF, the program that Brazilian taxpayers are required to run to prepare their income tax returns, was released without obfuscation, with debug information and surprisingly even under an acceptable license, which enabled us to reverse engineer it and from then on to update the rescued source code.

That relieved the primary oppression, but the government changes the software yearly, so every year brings a new threat to our freedom, and defending it requires duplicating the changes. That, too, is unjustly taxing!

Democratic governments ought to respect our freedom, not threaten it. The tax laws and regulations that the program implements are and must be public code. Nothing that the software is programmed to do should be a secret. The tax returns need to be and are verified after turning in. Nothing justifies making the program freedom depriving.

That it remains so is a manifestation of the bad habit of abusing power through software, of hijacking others' computers to serve one's purposes, without thinking much of it. Thus we draw the fish's attention to the toxic water, and to the root cause of its toxicity.

As we celebrate the 16th anniversary of the IRPF-Livre project, and take the too-tight shoes out by releasing its updates for 2023, we call upon the new Brazilian government, and indeed upon all democratic governments, to quit this bad habit, and to release, under freedom- and transparency-respecting terms, the source code for all government-mandated programs, so that they are not imposed taxing software.
https://www.fsfla.org/~lxoliva/fsfla/irpf-livre/2023/

About Imposed Taxing Software

Since 2006, we have been running a campaign against imposed taxing software: programs that are imposed in the sense that you cannot avoid them, and taxing in the sense that they burden you in a way that resembles a tax, but is exempt from social benefits and paid for with your freedom.

Nonfree programs are unjust and too onerous (even when they are nominally gratis), because they imply a loss of freedom, that is, of control over your digital life. When this burden (of suppressed freedom) is compounded with the imposition of use of such programs, they become profoundly oppressive: imposed taxing software.

Our initial focus was on oppressive software imposed by governments, such as mandatory tax-related programs and software required to interact with public banks.
https://www.fsfla.org/circular/2006-11#Editorial

While pressuring the government to liberate income tax software in Brazil, we have been updating and publishing a compatible and freedom-respecting version every year since 2007.
https://www.fsfla.org/anuncio/2012-10-Acesso-SoftImp
https://www.fsfla.org/~lxoliva/fsfla/irpf-livre/

In 2023, we extended the campaign to taxing software imposed by private providers: when freedom-depriving software is required to obtain or enjoy products or services.

To be clear, this campaign is not (solely) about software for taxation, but rather about software that is taxing (an unjust burden, because it taxes your freedom; the software is itself like a tax), and that, on top of that, is imposed, thus profoundly oppressive.

About IRPF-Livre

It's a software development project to prepare Natural Person's Income Tax returns compliant with the standards defined by the Brazilian Secretaria de Receita Federal (IRS), but without the technical and legal insecurity imposed by it.

IRPF-Livre is Free Software, that is, software that respects users' freedom to run it for any purpose, to study its source code and adapt it to their needs, and to distribute copies, modified or not.

The program is available both in source and Java object code forms:
http://www.fsfla.org/~lxoliva/fsfla/irpf-livre/

About FSFLA

Free Software Foundation Latin America joined in 2005 the international FSF network, previously formed by Free Software Foundations in the United States, in Europe and in India. These sister organizations work in their corresponding geographies towards promoting the same Free Software ideals and defending the same freedoms for software users and developers, working locally but cooperating globally.
https://www.fsfla.org/

Copyright 2023 FSFLA

Permission is granted to make and distribute verbatim copies of this entire document without royalty, provided the copyright notice, the document's official URL, and this permission notice are preserved.

Permission is also granted to make and distribute verbatim copies of individual sections of this document worldwide without royalty provided the copyright notice and the permission notice above are preserved, and the document's official URL is preserved or replaced by the individual section's official URL.

https://www.fsfla.org/anuncio/2023-05-IRPF-Livre-2023

Hello, dear readers! Today's article describes Ark, a new JavaScript-based mobile development platform. If you haven't read them yet, you might want to start by having a look at my past articles on Capacitor, React Native, NativeScript, and Flutter; having a common understanding of the design space will help us understand where Ark is similar and where it differs.

Ark, what it is

If I had to bet, I would guess that you have not heard of Ark. (I certainly hadn't either, when commissioned to do this research series.) To a first approximation, Ark—or rather, what I am calling Ark; I don't actually know the name for the whole architecture—is a loosely Flutter-like UI library implemented on top of a dialect of JavaScript, with build-time compilation to bytecode (like Hermes) but also with support for just-in-time and ahead-of-time compilation of bytecode to native code. It is made by Huawei.

At this point if you are already interested in this research series, I am sure this description raises more questions than it answers. Flutter-like? A dialect? Native compilation? Targetting what platforms? From Huawei? We'll get to all of these, but I think we need to start with the last question.

How did we get here?

In my last article on Flutter, I told a kind of just-so history of how Dart and Flutter came to their point in the design space. Thanks to corrections from a kind reader, it happened to also be more or less correct. In this article, though, I haven't talked with Ark developers at all; I don't have the benefit of a true claim on history. And yet, the only way I can understand Ark is by inventing a narrative, so here we go. It might even be true!

Recall that in 2018, Huawei was a dominant presence in the smartphone market. They were shipping excellent hardware at good prices both to the Chinese and to the global markets. Like most non-Apple, non-Google manufacturers, they shipped Android, and like most Android OEMs, they shipped Google's proprietary apps (mail, maps, etc.).

But then, over the next couple years, the US decided that allowing Huawei to continue on as before was, like, against national security interests or something. Huawei was barred from American markets, a number of suppliers were forbidden from selling hardware components to Huawei, and even Google was prohibited from shipping its mobile apps on Huawei devices. The effect on Huawei's market share for mobile devices was enormous: its revenue was cut in half over a period of a couple years.

In this position, as Huawei, what do you do? I can't even imagine, but specifically looking at smartphones, I think I would probably do about what they did. I'd fork Android, for starters, because that's what you already know and ship, and Android is mostly open source. I'd probably plan on continuing to use its lower-level operating system pieces indefinitely (kernel and so on) because that's not a value differentiator. I'd probably ship the same apps on top at first, because otherwise you slip all the release schedules and lose revenue entirely.

But, gosh, there is the risk that your product will be perceived as just a worse version of Android: that's not a good position to be in. You need to be different, and ideally better. That will take time. In the meantime, you claim that you're different, without actually being different yet. It's a somewhat ridiculous position to be in, but I can understand how you get here; Ars Technica published a scathing review poking fun at the situation. But, you are big enough to ride it out, knowing that somehow eventually you will be different.

Up to now, this part of the story is relatively well-known; the part that follows is more speculative on my part. Firstly, I would note that Huawei had been working for a while on a compiler and language run-time called Ark Compiler, with the goal of getting better performance out of Android applications. If I understand correctly, this compiler took the Java / Dalvik / Android Run Time bytecodes as its input, and outputted native binaries along with a new run-time implementation.

As I can attest from personal experience, having a compiler leads to hubris: you start to consider source languages like a hungry person looks at a restaurant menu. "Wouldn't it be nice to ingest that?" That's what we say at restaurants, right, fellow humans? So in 2019 and 2020 when the Android rug was pulled out from underneath Huawei, I think having in-house compiler expertise allowed them to consider whether they wanted to stick with Java at all, or whether it might be better to choose a more fashionable language.

Like black, JavaScript is always in fashion. What would it mean, then, to retool Huawei's operating system -- by then known by the name "HarmonyOS" -- to expose a JavaScript-based API as its primary app development framework? You could use your Ark compiler somehow to implement JavaScript (hubris!) and then you need a UI framework. Having ditched Java, it is now thinkable to ditch all the other Android standard libraries, including the UI toolkit: you start anew, in a way. So are you going to build a Capacitor, a React Native, a NativeScript, a Flutter? Surely not precisely any of these, but what will it be like, and how will it differ?

Incidentally, I don't know the origin story for the name Ark, but to me it brings to mind tragedy and rebuilding: in the midst of being cut off from your rich Android ecosystem, you launch a boat into the sea, holding a promise of a new future built differently. Hope and hubris in one vessel.

Two programming interfaces

In the end, Huawei builds two things: something web-like and something like Flutter. (I don't mean to suggest copying or degeneracy here; it's rather that I can only understand things in relation to other things, and these are my closest points of comparison for what they built.)

The web-like programming interface specifies UIs using an XML dialect, HML, and styles the resulting node tree with CSS. You augment these nodes with JavaScript behavior; the main app is a set of DOM-like event handlers. There is an API to dynamically create DOM nodes, but unlike the other systems we have examined, the HarmonyOS documentation doesn't really sell you on using a high-level framework like Angular.

If this were it, I think Ark would not be so compelling: the programming model is more like what was available back in the DHTML days. I wouldn't expect people to be able to make rich applications that delight users, given these primitives, though CSS animation and the HML loop and conditional rendering from the template system might be just expressive enough for simple applications.

The more interesting side is the so-called "declarative" UI programming model which exposes a Flutter/React-like interface. The programmer describes the "what" of the UI by providing a tree of UI nodes in its build function, and the framework takes care of calling build when necessary and of rendering that tree to the screen.

Here I need to show some example code, because it is... weird. Well, I find it weird, but it's not too far from SwiftUI in flavor. A small example from the fine manual:

@Entry @Component struct MyComponent { build() { Stack() { Image($rawfile('Tomato.png')) Text('Tomato') .fontSize(26) .fontWeight(500) } } }

The @Entry decorator (*) marks this struct (**) as being the main entry point for the app. @Component marks it as being a component, like a React functional component. Components conform to an interface (***) which defines them as having a build method which takes no arguments and returns no values: it creates the tree in a somewhat imperative way.

But as you see the flavor is somewhat declarative, so how does that work? Also, build() { ... } looks syntactically a lot like Stack() { ... }; what's the deal, are they the same?

Before going on to answer this, note my asterisks above: these are concepts that aren't in JavaScript. Indeed, programs written for HarmonyOS's declarative framework aren't JavaScript; they are in a dialect of TypeScript that Huawei calls ArkTS. In this case, an interface is a TypeScript concept. Decorators would appear to correspond to an experimental TypeScript feature, looking at the source code.

But struct is an ArkTS-specific extension, and Huawei has actually extended the TypeScript compiler to specifically recognize the @Component decorator, such that when you "call" a struct, for example as above in Stack() { ... }, TypeScript will parse that as a new expression type EtsComponentExpression, which may optionally be followed by a block. When Stack() is invoked, its children (instances of Image and Text, in this case) will be populated via running the block.

Now, though TypeScript isn't everyone's bag, it's quite normalized in the JavaScript community and not a hard sell. Language extensions like the handling of @Component pose a more challenging problem. Still, Facebook managed to sell people on JSX, so perhaps Huawei can do the same for their dialect. More on that later.

Under the hood, it would seem that we have a similar architecture to Flutter: invoking the components creates a corresponding tree of elements (as with React Native's shadow tree), which then are lowered to render nodes, which draw themselves onto layers using Skia, in a multi-threaded rendering pipeline. Underneath, the UI code actually re-uses some parts of Flutter, though from what I can tell HarmonyOS developers are replacing those over time.

Restrictions and extensions

So we see that the source language for the declarative UI framework is TypeScript, but with some extensions. It also has its restrictions, and to explain these, we have to talk about implementation.

Of the JavaScript mobile application development frameworks we discussed, Capacitor and NativeScript used "normal" JS engines from web browsers, while React Native built their own Hermes implementation. Hermes is also restricted, in a way, but mostly inasmuch as it lags the browser JS implementations; it relies on source-to-source transpilers to get access to new language features. ArkTS—that's the name of HarmonyOS's "extended TypeScript" implementation—has more fundamental restrictions.

Recall that the Ark compiler was originally built for Android apps. There you don't really have the ability to load new Java or Kotlin source code at run-time; in Java you have class loaders, but those load bytecode. On an Android device, you don't have to deal with the Java source language. If we use a similar architecture for JavaScript, though, what do we do about eval?

ArkTS's answer is: don't. As in, eval is not supported on HarmonyOS. In this way the implementation of ArkTS can be divided into two parts, a frontend that produces bytecode and a runtime that runs the bytecode, and you never have to deal with the source language on the device where the runtime is running. Like Hermes, the developer produces bytecode when building the application and ships it to the device for the runtime to handle.

Incidentally, before we move on to discuss the runtime, there are actually two front-ends that generate ArkTS bytecode: one written in C++ that seems to only handle standard TypeScript and JavaScript, and one written in TypeScript that also handles "extended TypeScript". The former has a test262 runner with about 10k skipped tests, and the latter doesn't appear to have a test262 runner. Note, I haven't actually built either one of these (or any of the other frameworks, for that matter).

The ArkTS runtime is itself built on a non-language-specific common Ark runtime, and the set of supported instructions is the union of the core ISA and the JavaScript-specific instructions. Bytecode can be interpreted, JIT-compiled, or AOT-compiled.

On the side of design documentation, it's somewhat sparse. There are some core design docs; readers may be interested in the rationale to use a bytecode interface for Ark as a whole, or the optimization overview.

Indeed ArkTS as a whole has a surfeit of optimizations, to an extent that makes me wonder which ones are actually needed. There are source-to-source optimizations on bytecode, which I expect are useful if you are generating ArkTS bytecode from JavaScript, where you probably don't have a full compiler implementation. There is a completely separate optimizer in the eTS part of the run-time, based on what would appear to be a novel "circuit-based" IR that bears some similarity to sea-of-nodes. Finally the whole thing appears to bottom out in LLVM, which of course has its own optimizer. I can only assume that this situation is somewhat transitory. Also, ArkTS does appear to generate its own native code sometimes, notably for inline cache stubs.

Of course, when it comes to JavaScript, there are some fundamental language semantics and there is also a large and growing standard library. In the case of ArkTS, this standard library is part of the run-time, like the interpreter, compilers, and the garbage collector (generational concurrent mark-sweep with optional compaction).

All in all, when I step back from it, it's a huge undertaking. Implementing JavaScript is no joke. It appears that ArkTS has done the first 90% though; the proverbial second 90% should only take a few more years :)

Evaluation

If you told a younger me that a major smartphone vendor switched from Java to JavaScript for their UI, you would probably hear me react in terms of the relative virtues of the programming languages in question. At this point in my career, though, the only thing that comes to mind is what an expensive proposition it is to change everything about an application development framework. 200 people over 5 years would be my estimate, though of course teams are variable. So what is it that we can imagine that Huawei bought with a thousand person-years of investment? Towards what other local maximum are we heading?

Startup latency

I didn't mention it before, but it would seem that one of the goals of HarmonyOS is in the name: Huawei wants to harmonize development across the different range of deployment targets. To the extent possible, it would be nice to be able to write the same kinds of programs for IoT devices as you do for feature-rich smartphones and tablets and the like. In that regard one can see through all the source code how there is a culture of doing work ahead-of-time and preventing work at run-time; for example see the design doc for the interpreter, or for the file format, or indeed the lack of JavaScript eval.

Of course, this wide range of targets also means that the HarmonyOS platform bears the burden of a high degree of abstraction; not only can you change the kernel, but also the JavaScript engine (using JerryScript on "lite" targets).

I mention this background because sometimes in news articles and indeed official communication from recent years there would seem to be some confusion that HarmonyOS is just for IoT, or aimed to be super-small, or something. In this evaluation I am mostly focussed on the feature-rich side of things, and there my understanding is that the developer will generate bytecode ahead-of-time. When an app is installed on-device, the AOT compiler will turn it into a single ELF image. This should generally lead to fast start-up.

However it would seem that the rendering library that paints UI nodes into layers and then composits those layers uses Skia in the way that Flutter did pre-Impeller, which to be fair is a quite recent change to Flutter. I expect therefore that Ark (ArkTS + ArkUI) applications also experience shader compilation jank at startup, and that they may be well-served by tesellating their shapes into primitives like Impeller does so that they can precompile a fixed, smaller set of shaders.

Jank

Maybe it's just that apparently I think Flutter is great, but ArkUI's fundamental architectural similarity to Flutter makes me think that jank will not be a big issue. There is a render thread that is separate from the ArkTS thread, so like with Flutter, async communication with main-thread interfaces is the main jank worry. And on the ArkTS side, ArkTS even has a number of extensions to be able to share objects between threads without copying, should that be needed. I am not sure how well-developed and well-supported these extensions are, though.

I am hedging my words, of course, because I am missing a bit of social proof; HarmonyOS is still in infant days, and it doesn't have much in the way of a user base outside China, from what I can tell, and my ability to read Chinese is limited to what Google Translate can do for me :) Unlike other frameworks, therefore, I haven't been as able to catch a feel of the pulse of the ArkUI user community: what people are happy about, what the pain points are.

It's also interesting that unlike iOS or Android, HarmonyOS is only exposing these "web-like" and "declarative" UI frameworks for app development. This makes it so that the same organization is responsible for the software from top to bottom, which can lead to interesting cross-cutting optimizations: functional reactive programming isn't just a developer-experience narrative, but it can directly affect the shape of the rendering pipeline. If there is jank, someone in the building is responsible for it and should be able to fix it, whether it is in the GPU driver, the kernel, the ArkTS compiler, or the application itself.

Peak performance

I don't know how to evaluate ArkTS for peak performance. Although there is a JIT compiler, I don't have the feeling that it is as tuned for adaptive optimization as V8 is.

At the same time, I find it interesting that HarmonyOS has chosen to modify JavaScript. While it is doing that, could they switch to a sound type system, to allow the kinds of AOT optimizations that Dart can do? It would be an interesting experiment.

As it is, though, if I had to guess, I would say that ArkTS is well-positioned for predictably good performance with AOT compilation, although I would be interested in seeing the results of actually running it.

Aside: On the importance of storytelling

In this series I have tried to be charitable towards the frameworks that I review, to give credit to what they are trying to do, even while noting where they aren't currently there yet. That's part of why I need a plausible narrative for how the frameworks got where they are, because that lets me have an idea of where they are going.

In that sense I think that Ark is at an interesting inflection point. When I started reading documentation about ArkUI and HarmonyOS and all that, I bounced out—there were too many architectural box diagrams, too many generic descriptions of components, too many promises with buzzwords. It felt to me like the project was trying to justify itself to a kind of clueless management chain. Was there actually anything here?

But now when I see the adoption of a modern rendering architecture and a bold new implementation of JavaScript, along with the willingness to experiment with the language, I think that there is an interesting story to be told, but this time not to management but to app developers.

Of course you wouldn't want to market to app developers when your system is still a mess because you haven't finished rebuilding an MVP yet. Retaking my charitable approach, then, I can only think that all the architectural box diagrams were a clever blind to avoid piquing outside interest while the app development kit wasn't ready yet :) As and when the system starts working well, presumably over the next year or so, I would expect HarmonyOS to invest much more heavily in marketing and developer advocacy; the story is interesting, but you have to actually tell it.

Aside: O platform, my platform

All of the previous app development frameworks that we looked at were cross-platform; Ark is not. It could be, of course: it does appear to be thoroughly open source. But HarmonyOS devices are the main target. What implications does this have?

A similar question arises in perhaps a more concrete way if we start with the mature Flutter framework: what would it mean to make a Flutter phone?

The first thought that comes to mind is that having a Flutter OS would allow for the potential for more cross-cutting optimizations that cross abstraction layers. But then I think, what does Flutter really need? It has the GPU drivers, and we aren't going to re-implement those. It has the bridge to the platform-native SDK, which is not such a large and important part of the app. You get input from the platform, but that's also not so specific. So maybe optimization is not the answer.

On the other hand, a Flutter OS would not have to solve the make-it-look-native problem; because there would be no other "native" toolkit, your apps won't look out of place. That's nice. It's not something that would make the platform compelling, though.

HarmonyOS does have this embryonic concept of app mobility, where like you could put an app from your phone on your fridge, or something. Clearly I am not doing it justice here, but let's assume it's a compelling use case. In that situation it would be nice for all devices to present similar abstractions, so you could somehow install the same app on two different kinds of devices, and they could communicate to transfer data. As you can see here though, I am straying far from my domain of expertise.

One reasonable way to "move" an app is to have it stay running on your phone and the phone just communicates pixels with your fridge (or whatever); this is the low-level solution. I think HarmonyOS appears to be going for the higher-level solution where the app actually runs logic on the device. In that case it would make sense to ship UI assets and JavaScript / extended TypeScript bytecode to the device, which would run the app with an interpreter (for low-powered devices) or use JIT/AOT compilation. The Ark runtime itself would live on all devices, specialized to their capabilities.

In a way this is the Apple WatchOS solution (as I understand it); developers publish their apps as LLVM bitcode, and Apple compiles it for the specific devices. A FlutterOS with a Flutter run-time on all devices could do something similar. As with WatchOS you wouldn't have to ship the framework itself in the app bundle; it would be on the device already.

Finally, publishing apps as some kind of intermediate representation also has security benefits: as the OS developer, you can ensure some invariants via the toolchain that you control. Of course, you would have to ensure that the Flutter API is sufficiently expressive for high-performance applications, while also not having arbitrary machine code execution vulnerabilities; there is a question of language and framework design as well as toolchain and runtime quality of implementation. HarmonyOS could be headed in this direction.

Conclusion

Ark is a fascinating effort that holds much promise. It's also still in motion; where will it be when it anneals to its own local optimum? It would appear that the system is approaching usability, but I expect a degree of churn in the near-term as Ark designers decide which language abstractions work for them and how to, well, harmonize them with the rest of JavaScript.

For me, the biggest open question is whether developers will love Ark in the way they love, say, React. In a market where Huawei is still a dominant vendor, I think the material conditions are there for a good developer experience: people tend to like Flutter and React, and Ark is similar. Huawei "just" needs to explain their framework well (and where it's hard to explain, to go back and change it so that it is explainable).

But in a more heterogeneous market, to succeed Ark would need to make a cross-platform runtime like the one Flutter has and engage in some serious marketing efforts, so that developers don't have to limit themselves to targetting the currently-marginal HarmonyOS. Selling extensions to JavaScript will be much more difficult in a context where the competition is already established, but perhaps Ark will be able to productively engage with TypeScript maintainers to move the language so it captures some of the benefits of Dart that facilitate ahead-of-time compilation.

Well, that's it for my review round-up; hope you have enjoyed the series. I have one more pending article, speculating about some future technologies. Until then, happy hacking, and see you next time.

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Bankrupt

Banking institutions have sought to automate customer service through websites and, more recently, through TRApps.
https://www.fsfla.org/anuncio/2023-03-TRApps

What these banks are saving in offices and staff, we customers are paying for with security and freedom. They are morally bankrupt.

Genuine security never depends on secret software. On the contrary, transparency strengthens security.

Nevertheless, these banks impose on us, in the name of security (their own, not ours), various harmful behaviors:

• the use of software that we cannot control and whose actions on our computers are hidden from us;

• the use of too-short passwords; and

• the use of devices and operating systems designed to run under someone else's control, and to collect and exfiltrate our data.

Running software controlled by others always implies a loss of freedom, and a threat to security and privacy.

The requirement to use these programs has become so common and persistent that it seems unavoidable. Thus, we have decided to expand our campaign against imposed taxing software beyond state-controlled institutions to also include private services and goods whose providers converge on such impositions.
https://www.fsfla.org/anuncio/2023-04-bancarrota#softimp

We share our board member Alexandre Oliva's recent account of his over 20 years of struggle against technological abuse by banks in his country. We highlight his recent legal victory: online banking services must be restored without requiring the installation of programs other than a standard browser. Read more:
https://www.fsfla.org/texto/bancarrota

About Imposed Taxing Software

Since 2006, we have been running a campaign against imposed taxing software: programs that are imposed in the sense that you cannot avoid them, and taxing in the sense that they burden you in a way that resembles a tax, but is exempt from social benefits and paid for with your freedom.

Nonfree programs are unjust and too onerous (even when they are nominally gratis), because they imply a loss of freedom, that is, of control over your digital life. When this burden (of suppressed freedom) is compounded with the imposition of use of such programs, they become profoundly oppressive: imposed taxing software.

Our initial focus was on oppressive software imposed by governments, such as mandatory tax-related programs and software required to interact with public banks.
https://www.fsfla.org/circular/2006-11#Editorial

While pressuring the government to liberate income tax software in Brazil, we have been updating and publishing a compatible and freedom-respecting version every year since 2007.
https://www.fsfla.org/anuncio/2012-10-Acesso-SoftImp
https://www.fsfla.org/~lxoliva/fsfla/irpf-livre/

In 2023, we extended the campaign to taxing software imposed by private providers: when freedom-depriving software is required to obtain or enjoy products or services.

To be clear, this campaign is not (solely) about software for taxation, but rather about software that is taxing (an unjust burden, because it taxes your freedom; the software is itself like a tax), and that, on top of that, is imposed, thus profoundly oppressive.

About FSFLA

Free Software Foundation Latin America joined in 2005 the international FSF network, previously formed by Free Software Foundations in the United States, in Europe and in India. These sister organizations work in their corresponding geographies towards promoting the same Free Software ideals and defending the same freedoms for software users and developers, working locally but cooperating globally.
https://www.fsfla.org/

Copyright 2023 FSFLA

Permission is granted to make and distribute verbatim copies of this entire document without royalty, provided the copyright notice, the document's official URL, and this permission notice are preserved.

Permission is also granted to make and distribute verbatim copies of individual sections of this document worldwide without royalty provided the copyright notice and the permission notice above are preserved, and the document's official URL is preserved or replaced by the individual section's official URL.

https://www.fsfla.org/anuncio/2023-04-bancarrota

Let’s reflect on some of my recent work that started with understanding Trisquel GNU/Linux, improving transparency into apt-archives, working on reproducible builds of Trisquel, strengthening verification of apt-archives with Sigstore, and finally thinking about security device threat models. A theme in all this is improving methods to have trust in machines, or generally any external entity. While I believe that everything starts by trusting something, usually something familiar and well-known, we need to deal with misuse of that trust that leads to failure to delivery what is desired and expected from the trusted entity. How can an entity behave to invite trust? Let’s argue for some properties that can be quantitatively measured, with a focus on computer software and hardware:

• Deterministic Behaviour – given a set of circumstances, it should behave the same.
• Verifiability and Transparency – the method for decision making should be accessible for understanding (the scientific method) available as free software and its binaries verifiable, i.e., it should be possible to verify that the entity actually follows the intended deterministic method (implying efforts like reproducible builds and bootstrappable builds).
• Accountable – the entity should behave the same for everyone, and deviation should be possible prove in a way that is hard to deny, implying efforts such as Certificate Transparency and more generic checksum logs like Sigstore and Sigsum.

Essentially, this boils down to: Trust, Verify and Hold Accountable. To put this dogma in perspective, it helps to understand that this approach may be harmful to human relationships (which could explain the social awkwardness of hackers), but it remains useful as a method to improve the design of computer systems, and a useful method to evaluate safety of computer systems. When a system fails some of the criteria above, we know we have more work to do to improve it.

How far have we come on this journey? Through earlier efforts, we are in a fairly good situation. Richard Stallman through GNU/FSF made us aware of the importance of free software, the Reproducible/Bootstrappable build projects made us aware of the importance of verifiability, and Certificate Transparency highlighted the need for accountable signature logs leading to efforts like Sigstore for software. None of these efforts would have seen the light of day unless people wrote free software and packaged them into distributions that we can use, and built hardware that we can run it on. While there certainly exists more work to be done on the software side, with the recent amazing full-source build of Guix based on a 357-byte hand-written seed, I believe that we are closing that loop on the software engineering side.

So what remains? Some inspiration for further work:

• Accountable binary software distribution remains unresolved in practice, although we have some software components around (e.g., apt-sigstore and guix git authenticate). What is missing is using them for verification by default and/or to improve the signature process to use trustworthy hardware devices, and committing the signatures to transparency logs.
• Trustworthy hardware to run trustworthy software on remains a challenge, and we owe FSF’s Respect Your Freedom credit for raising awareness of this. Many modern devices requires non-free software to work which fails most of the criteria above and are thus inherently untrustworthy.
• Verifying rebuilds of currently published binaries on trustworthy hardware is unresolved.
• Completing a full-source rebuild from a small seed on trustworthy hardware remains, preferably on a platform wildly different than X86 such as Raptor’s Talos II.
• We need improved security hardware devices and improved established practices on how to use them. For example, while Gnuk on the FST enable a trustworthy software and hardware solution, the best process for using it that I can think of generate the cryptographic keys on a more complex device. Efforts like Tillitis are inspiring here.

Onwards and upwards, happy hacking!

We are delighted and somewhat relieved to announce that the third reduction of the Guix bootstrap binaries has now been merged in the main branch of Guix! If you run guix pull today, you get a package graph of more than 22,000 nodes rooted in a 357-byte program—something that had never been achieved, to our knowledge, since the birth of Unix.

We refer to this as the Full-Source Bootstrap. In this post, we explain what this means concretely. This is a major milestone—if not the major milestone—in our quest for building everything from source, all the way down.

How did we get there, and why? In two previous blog posts, we elaborated on why this reduction and bootstrappability in general is so important.

One reason is to properly address supply chain security concerns. The Bitcoin community was one of the first to recognize its importance well enough to put the idea into practice. At the Breaking Bitcoin conference 2020, Carl Dong gave a fun and remarkably gentle introduction. At the end of the talk, Carl states:

The holy grail for bootstrappability will be connecting hex0 to mes.

Two years ago, at FOSDEM 2021, I (Janneke) gave a short talk about how we were planning to continue this quest.

If you think one should always be able to build software from source, then it follows that the “trusting trust” attack is only a symptom of an incomplete or missing bootstrap story.

The Road to Full-Source Bootstrap

Three years ago, the bootstrap binaries were reduced to just GNU Mes and MesCC-Tools (and the driver to build Guix packages: a static build of GNU Guile 2.0.9).

The new Full-Source Bootstrap, merged in Guix master yesterday, removes the binaries for Mes and MesCC-Tools and replaces them by bootstrap-seeds. For x86-linux (which is also used by the x86_64-linux build), this means this program hex0-seed, with ASCII-equivalent hex0_x86.hex0. Hex0 is self-hosting and its source looks like this:

; Where the ELF Header is going to hit ; Simply jump to _start ; Our main function # :_start ; (0x8048054) 58 # POP_EAX ; Get the number of arguments

you can spot two types of line-comment: hex0 (;) and assembly (#). The only program-code in this snippet is 58: two hexidecimal digits that are taken as two nibbles and compiled into the corresponding byte with binary value 58.

Starting from this 357-byte hex0-seed binary provided by the bootstrap-seeds, the stage0-posix package created by Jeremiah Orians first builds hex0 and then all the way up: hex1, catm, hex2, M0, cc_x86, M1, M2, get_machine (that's all of MesCC-Tools), and finally M2-Planet.

The new GNU Mes v0.24 release can be built with M2-Planet. This time with only a remarkably small change, the bottom of the package graph now looks like this (woohoo!):

gcc-mesboot (4.9.4) ^ | (...) ^ | binutils-mesboot (2.20.1a), glibc-mesboot (2.2.5), gcc-core-mesboot (2.95.3) ^ | patch-mesboot (2.5.9) ^ | bootstrappable-tcc (0.9.26+31 patches) ^ | gnu-make-mesboot0 (3.80) ^ | gzip-mesboot (1.2.4) ^ | tcc-boot (0.9.27) ^ | mes-boot (0.24.2) ^ | stage0-posix (hex0..M2-Planet) ^ | gash-boot, gash-utils-boot ^ | * bootstrap-seeds (357-bytes for x86) ~~~ [bootstrap-guile-2.0.9 driver (~25 MiB)]

full graph

We are excited that the NLnet Foundation has been sponsoring this work!

However, we aren't done yet; far from it.

Lost Paths

The idea of reproducible builds and bootstrappable software is not very new. Much of that was implemented for the GNU tools in the early 1990s. Working to recreate it in present time shows us much of that practice was forgotten.

Most bootstrap problems or loops are not so easy to solve and sometimes there are no obvious answers, for example:

• In 2013, the year that Reproducible Builds started to gain some traction, the GNU Compiler Collection released version 4.8.0, making C++ a build requirement, and

• Even more recently (2018), the GNU C Library glibc-2.28 adds Python as a build requirement,

While these examples make for a delightful puzzle from a bootstrappability perspective, we would love to see the maintainers of GNU packages consider bootstrappability and start taking more responsibility for the bootstrap story of their packages.

Next Steps

Despite this major achievement, there is still work ahead.

First, while the package graph is rooted in a 357-byte program, the set of binaries from which packages are built includes a 25 MiB statically-linked Guile, guile-bootstrap, that Guix uses as its driver to build the initial packages. 25 MiB is a tenth of what the initial bootstrap binaries use to weigh, but it is a lot compared to those 357 bytes. Can we get rid of this driver, and how?

A development effort with Timothy Sample addresses the dependency on guile-bootstrap of Gash and Gash-Utils, the pure-Scheme POSIX shell implementation central to our second milestone. On the one hand, Mes is gaining a higher level of Guile compatibility: hash table interface, record interface, variables and variable-lookup, and Guile (source) module loading support. On the other hand, Gash and Gash-Utils are getting Mes compatibility for features that Mes is lacking (notably syntax-case macros). If we pull this off, guile-bootstrap will only be used as a dependency of bootar and as the driver for Guix.

Second, the full-source bootstrap that just landed in Guix master is limited to x86_64-linux and i686-linux, but ARM and RISC-V will be joining soon. We are most grateful and excited that the NLnet Foundation has decided to continue sponsoring this work!

Some time ago, Wladimir van der Laan contributed initial RISC-V support for Mes but a major obstacle for the RISC-V bootstrap is that the “vintage” GCC-2.95.3 that was such a helpful stepping stone does not support RISC-V. Worse, the RISC-V port of GCC was introduced only in GCC 7.5.0—a version that requires C++ and cannot be bootstrapped! To this end, we have been improving MesCC, the C compiler that comes with Mes, so it is able to build GCC 4.6.5; meanwhile, Ekaitz Zarraga backported RISC-V support to GCC 4.6.5, and backported RISC-V support from the latest tcc to our bootstrappable-tcc.

Outlook

The full-source bootstrap was once deemed impossible. Yet, here we are, building the foundations of a GNU/Linux distro entirely from source, a long way towards the ideal that the Guix project has been aiming for from the start.

There are still some daunting tasks ahead. For example, what about the Linux kernel? The good news is that the bootstrappable community has grown a lot, from two people six years ago there are now around 100 people in the #bootstrappable IRC channel. Interesting times ahead!

About Bootstrappable Builds and GNU Mes

Software is bootstrappable when it does not depend on a binary seed that cannot be built from source. Software that is not bootstrappable---even if it is free software---is a serious security risk (supply chain security) for a variety of reasons. The Bootstrappable Builds project aims to reduce the number and size of binary seeds to a bare minimum.

GNU Mes is closely related to the Bootstrappable Builds project. Mes is used in the full-source bootstrap path for the Guix System.

Currently, Mes consists of a mutual self-hosting scheme interpreter and C compiler. It also implements a C library. Mes, the scheme interpreter, is written in about 5,000 lines of code of simple C and can be built with M2-Planet. MesCC, the C compiler, is written in scheme. Together, Mes and MesCC can compile bootstrappable TinyCC that is self-hosting. Using this TinyCC and the Mes C library, the entire Guix System for i686-linux and x86_64-linux is bootstrapped.

About GNU Guix

GNU Guix is a transactional package manager and an advanced distribution of the GNU system that respects user freedom. Guix can be used on top of any system running the Hurd or the Linux kernel, or it can be used as a standalone operating system distribution for i686, x86_64, ARMv7, AArch64 and POWER9 machines.

In addition to standard package management features, Guix supports transactional upgrades and roll-backs, unprivileged package management, per-user profiles, and garbage collection. When used as a standalone GNU/Linux distribution, Guix offers a declarative, stateless approach to operating system configuration management. Guix is highly customizable and hackable through Guile programming interfaces and extensions to the Scheme language.

Good day, gentle hackfolk. Like an old-time fiddler I would appear to be deep in the groove, playing endless variations on a theme, in this case mobile application frameworks. But one can only recognize novelty in relation to the familiar, and today's note is a departure: we are going to look at Flutter, a UI toolkit based not on JavaScript but on the Dart language.

The present, from the past

Where to start, even? The problem is big enough that I'll approach it from three different angles: from the past, from the top, and from the bottom.

With the other frameworks we looked at, we didn't have to say much about their use of JavaScript. JavaScript is an obvious choice, in 2023 at least: it is ubiquitous, has high quality implementations, and as a language it is quite OK and progressively getting better. Up to now, "always bet on JS" has had an uninterrupted winning streak.

But winning is not the same as unanimity, and Flutter and Dart represent an interesting pole of contestation. To understand how we got here, we have to go back in time. Ten years ago, JavaScript just wasn't a great language: there were no modules, no async functions, no destructuring, no classes, no extensible iteration, no optional arguments to functions. In addition it was hobbled with a significant degree of what can only be called accidental sloppiness: with which can dynamically alter a lexical scope, direct eval that can define new local variables, Function.caller, and so on. Finally, larger teams were starting to feel the need for more rigorous language tooling that could use types to prohibit some classes of invalid programs.

All of these problems in JavaScript have been addressed over the last decade, mostly successfully. But in 2010 or so if you were a virtual machine engineer, you might look at JavaScript and think that in some other world, things could be a lot better. That's effectively what happened: the team that originally built V8 broke off and started to work on what became Dart.

Initially, Dart was targetted for inclusion in the Chrome web browser as an alternate "native" browser language. This didn't work, for various reasons, but since then Dart grew the Flutter UI toolkit, which has breathed new life into the language. And this is a review of Flutter, not a review of Dart, not really anyway; to my eyes, Dart is spiritually another JavaScript, different but in the same family. Dart's implementation has many interesting aspects as well that we'll get into later on, but all of these differences are incidental: they could just as well be implemented on top of JavaScript, TypeScript, or another source language in that family. Even if Flutter isn't strictly part of the JavaScript-based mobile application development frameworks that we are comparing, it is valuable to the extent that it shows what is possible, and in that regard there is much to say.

Flutter, from the top

At its most basic, Flutter is a UI toolkit for Dart. In many ways it is like React. Like React, its interface follows the functional-reactive paradigm: programmers describe the "what", and Flutter takes care of the "how". Also, like the phenomenon in which new developers can learn React without really knowing JavaScript, Flutter is the killer app for Dart: Flutter developers mostly learn Dart at the same time that they pick up Flutter.

In some other ways, Flutter is the logical progression of React, going in the same direction but farther along. Whereas React-on-the-web takes the user's declarative specifications of what the UI should look like and lowers them into DOM trees, and React Native lowers them to platform-native UI widgets, Flutter has its own built-in layout, rasterization, and compositing engine: Flutter draws all the pixels.

This has the predictable challenge that Flutter has to make significant investments so that its applications don't feel out-of-place on their platform, but on the other hand it opens up a huge space for experimentation and potential optimization: Flutter has the potential to beat native at its own game. Recall that with React Native, the result of the render-commit-mount process is a tree of native widgets. The native platform will surely then perform a kind of layout on those widgets, divide them into layers that correspond to GPU textures, paint those layers, then composite them to the screen -- basically, what a web engine will do.

What if we could instead skip the native tree and go directly to the lower GPU layer? That is the promise of Flutter. Flutter has the potential to create much more smooth and complex animations than the other application development frameworks we have mentioned, with lower overhead and energy consumption.

In practice... that's always the question, isn't it? Again, please accept my usual caveat that I am a compilers guy moonlighting in the user interface domain, but my understanding is that Flutter mostly lives up to its promise, but with one significant qualification which we'll get to in a minute. But before that, let's traverse Flutter from the other direction, coming up from Dart.

Dart, from the bottom

To explain some aspects of Dart I'm going to tell a just-so story that may or may not be true. I know and like many of the Dart developers, and we have similar instincts, so it's probably not too far from the truth.

Let's say you are the team that originally developed V8, and you decide to create a new language. You write a new virtual machine that looks like V8, taking Dart source code as input and applying advanced adaptive compilation techniques to get good performance. You can even be faster than JS because your language is just a bit more rigid than JavaScript is: you have traded off expressivity for performance. (Recall from our discussion of NativeScript that expressivity isn't a value judgment: there can be reasons to pay for more "mathematically appealing operational equivalences", in Felleisen's language, in exchange for applying more constraints on a language.)

But, you fail to ship the VM in a browser; what do you do? The project could die; that would be annoying, but you work for Google, so it happens all the time. However, a few interesting things happen around the same time that will cause you to pivot. One is a concurrent experiment by Chrome developers to pare the web platform down to its foundations and rebuild it. This effort will eventually become Flutter; while it was originally based on JS, eventually they will choose to switch to Dart.

The second thing that happens is that recently-invented smart phones become ubiquitous. Most people have one, and the two platforms are iOS and Android. Flutter wants to target them. You are looking for your niche, and you see that mobile application development might be it. As the Flutter people continue to experiment, you start to think about what it would mean to target mobile devices with Dart.

The initial Dart VM was made to JIT, but as we know, Apple doesn't let people do this on iOS. So instead you look to write a quick-and-dirty ahead-of-time compiler, based on your JIT compiler that takes your program as input, parses and baseline-compiles it, and generates an image that can be loaded at runtime. It ships on iOS. Funnily enough, it ships on Android too, because AOT compilation allows you to avoid some startup costs; forced to choose between peak performance via JIT and fast startup via AOT, you choose fast startup.

It's a success, you hit your product objectives, and you start to look further to a proper ahead-of-time compiler native code that can stand alone without the full Dart run-time. After all, if you have to compile at build-time, you might as well take the time to do some proper optimizations. You actually change the language to have a sound typing system so that the compiler can make program transformations that are valid as long as it can rely on the program's types.

Fun fact: I am told that the shift to a sound type system actually started before Flutter and thus before AOT, because of a Dart-to-JavaScript compiler that you inherited from the time in which you thought the web would be the main target. The Dart-to-JS compiler used to be a whole-program compiler; this enabled it to do flow-sensitive type inference, resulting in faster and smaller emitted JavaScript. But whole-program compilation doesn't scale well in terms of compilation time, so Dart-to-JS switched to separate per-module compilation. But then you lose lots of types! The way to recover the fast-and-small-emitted-JS property was through a stronger, sound type system for Dart.

At this point, you still have your virtual machine, plus your ahead-of-time compiler, plus your Dart-to-JS compiler. Such riches, such bounty! It is not a bad situation to be in, in 2023: you can offer a good development experience via the just-in-time compiled virtual machine. Apparently you can even use the JIT on iOS in developer mode, because attaching ptrace to a binary allows for native code generation. Then when you go to deploy, you make a native binary that includes everything.

For the web, you also have your nice story, even nicer than with JavaScript in some ways: because the type checker and ahead-of-time compiler are integrated in Dart, you don't have to worry about WebPack or Vite or minifiers or uglifiers or TypeScript or JSX or Babel or any of the other things that JavaScript people are used to. Granted, the tradeoff is that innovation is mostly centralized with the Dart maintainers, but currently Google seems to be investing enough so that's OK.

Stepping back, this story is not unique to Dart; many of its scenes also played out in the world of JavaScript over the last 5 or 10 years as well. Hermes (and QuickJS, for that matter) does ahead-of-time compilation, albeit only to bytecode, and V8's snapshot facility is a form of native AOT compilation. But the tooling in the JavaScript world is more diffuse than with Dart. With the perspective of developing a new JavaScript-based mobile operating system in mind, the advantages that Dart (and thus Flutter) has won over the years are also on the table for JavaScript to win. Perhaps even TypeScript could eventually migrate to have a sound type system, over time; it would take a significant investment but the JS ecosystem does evolve, if slowly.

(Full disclosure: while the other articles in this series were written without input from the authors of the frameworks under review, through what I can only think was good URL guesswork, a draft copy of this article leaked to Flutter developers. Dart hacker Slava Egorov kindly sent me a mail correcting a number of misconceptions I had about Dart's history. Fair play on whoever guessed the URL, and many thanks to Slava for the corrections; any remaining errors are wholly mine, of course!)

Evaluation

So how do we expect Flutter applications to perform? If we were writing a new mobile OS based on JavaScript, what would it mean in terms of performance to adopt a Flutter-like architecture?

Startup latency

Flutter applications are well-positioned to start fast, with ahead-of-time compilation. However they have had problems realizing this potential, with many users seeing a big stutter when they launch a Flutter app.

To explain this situation, consider the structure of a typical low-end Android mobile device: you have a small number of not-terribly-powerful CPU cores, but attached to the same memory you also have a decent GPU with many cores. For example, the SoC in the low-end Moto E7 Plus has 8 CPU cores and 128 GPU cores (texture shader units). You could paint widget pixels into memory from either the CPU or the GPU, but you'd rather do it in the GPU because it has so many more cores: in the time it takes to compute the color of a single pixel on the CPU, on the GPU you could do, like, 128 times as many, given that the comparison is often between multi-threaded rasterization on the GPU versus single-threaded rasterization on the CPU.

Flutter has always tried to paint on the GPU. Historically it has done so via a GPU back-end to the Skia graphics library, notably used by Chrome among other projects. But, Skia's API is a drawing API, not a GPU API; Skia is the one responsible for configuring the GPU to draw what we want. And here's the problem: configuring the GPU takes time. Skia generates shader code at run-time for rasterizing the specific widgets used by the Flutter programmer. That shader code then needs to be compiled to the language the GPU driver wants, which looks more like Vulkan or Metal. The process of compilation and linking takes time, potentially seconds, even.

The solution to "too much startup shader compilation" is much like the solution to "too much startup JavaScript compilation": move this phase to build time. The new Impeller rendering library does just that. However to do that, it had to change the way that Flutter renders: instead of having Skia generate specialized shaders at run-time, Impeller instead lowers the shapes that it draws to a fixed set of primitives, and then renders those primitives using a smaller, fixed set of shaders. These primitive shaders are pre-compiled at build time and included in the binary. By switching to this new renderer, Flutter should be able to avoid startup jank.

Jank

Of all the application development frameworks we have considered, to my mind Flutter is the best positioned to avoid jank. It has the React-like asynchronous functional layout model, but "closer to the metal"; by skipping the tree of native UI widgets, it can potentially spend less time for each frame render.

When you start up a Flutter app on iOS, the shell of the application is actually written in Objective C++. On Android it's the same, except that it's Java. That shell then creates a FlutterView widget and spawns a new thread to actually run Flutter (and the user's Dart code). Mostly, Flutter runs on its own, rendering frames to the GPU resources backing the FlutterView directly.

If a Flutter app needs to communicate with the platform, it passes messages across an asynchronous channel back to the main thread. Although these messages are asynchronous, this is probably the largest potential source of jank in a Flutter app, outside the initial frame paint: any graphical update which depends on the answer to an asynchronous call may lag.

Peak performance

Dart's type system and ahead-of-time compiler optimize for predictable good performance rather than the more variable but potentially higher peak performance that could be provided by just-in-time compilation.

This story should probably serve as a lesson to any future platform. The people that developed the original Dart virtual machine had a built-in bias towards just-in-time compilation, because it allows the VM to generate code that is specialized not just to the program but also to the problem at hand. A given system with ahead-of-time compilation can always be made to perform better via the addition of a just-in-time compiler, so the initial focus was on JIT compilation. On iOS of course this was not possible, but on Android and other platforms where this was available it was the default deployment model.

However, even Android switched to ahead-of-time compilation instead of the JIT model in order to reduce startup latency: doing any machine code generation at all at program startup was more work than was needed to get to the first frame. One could add JIT back again on top of AOT but it does not appear to be a high priority.

I would expect that Capacitor could beat Dart in some raw throughput benchmarks, given that Capacitor's JavaScript implementation can take advantage of the platform's native JIT capability. Does it matter, though, as long as you are hitting your frame budget? I do not know.

Aside: An escape hatch to the platform

What happens if you want to embed a web view into a Flutter app?

If you think on the problem for a moment I suspect you will arrive at the unsatisfactory answer, which is that for better or for worse, at this point it is too expensive even for Google to make a new web engine. Therefore Flutter will have to embed the native WebView. However Flutter runs on its own threads; the native WebView has its own process and threads but its interface to the app is tied to the main UI thread.

Therefore either you need to make the native WebView (or indeed any other native widget) render itself to (a region of) Flutter's GPU backing buffer, or you need to copy the native widget's pixels into their own texture and then composite them in Flutter-land. It's not so nice! The Android and iOS platform view documentation discuss some of the tradeoffs and mitigations.

Aside: For want of a canvas

There is a very funny situation in the React Native world in which, if the application programmer wants to draw to a canvas, they have to embed a whole WebView into the React Native app and then proxy the canvas calls into the WebView. Flutter is happily able to avoid this problem, because it includes its own drawing library with a canvas-like API. Of course, Flutter also has the luxury of defining its own set of standard libraries instead of necessarily inheriting them from the web, so when and if they want to provide equivalent but differently-shaped interfaces, they can do so.

Flutter manages to be more expressive than React Native in this case, without losing much in the way of understandability. Few people will have to reach to the canvas layer, but it is nice to know it is there.

Conclusion

Dart and Flutter are terribly attractive from an engineering perspective. They offer a delightful API and a high-performance, flexible runtime with a built-in toolchain. Could this experience be brought to a new mobile operating system as its primary programming interface, based on JavaScript? React Native is giving it a try, but I think there may be room to take things further to own the application from the program all the way down to the pixels.

Well, that's all from me on Flutter and Dart for the time being. Next up, a mystery guest; see you then!