|Version 8 (modified by nfrisby, 14 months ago) (diff)|
- Plan for my internship summary
- general core knowledge
- Late Lambda Float
This pages serves as a public log what I did for my GHC internship from 21 Jan 2013 to 12 April 2013.
Plan for my internship summary
Compared to 351a8c6bbd53ce07d687b5a96afff77c4c9910cc, we implemented OPTIMIZATIONS with a cumulative effect of EFFECT on the generated code as well as EFFECT on the compiler's code. The hope is for the optimizations to have beneficial non-trivial interactions and to simplify/tidy the GHC code base.
general core knowledge
- Max's page about code generation: really good!
- document ticky profiling
- Core -> STG -> CMM and _what you can learn by looking at each one_
Late Lambda Float
LLF = Late Lambda Float
As the GHC optimization papers explain, it is an early design decision to *not* perform lambda lifting. My initial project was to investigate the effects of aggressively floating lambdas to the top-level at the end of the core2core pipeline.
- The main reason to not perform lambda lifting is that abstracting over free variables loses information and thereby inhibits *downstream* optimization.
- Doing it *late* (ie just before CorePrep?) circumvents this issue.
- The original conjecture was that doing it would save allocation: a dynamically allocated closure becomes a static top-level function.
- Max Bolingbroke did a quick implementation of this idea some years ago (~mid 2000s), but it seems it was abandoned. I don't know why.
We decided to implement LLF by re-using most of the FloatOut? machinery.
FloatOut? is structured in three phases.
- Annotate all expressions with their free variables.
- Consume those annotations while annotating each binder with the target "level" (essentially a depth wrt value lambdas) to which we want to float it.
- Consume those annotations while actually relocating the bindings.
We wholesale re-use the third phase (compiler/simplCore/FloatOut) with no changes, add logic to the middle phase, and enrich the first phase with more analyses.
Most of my changes were
- Adding flags (compiler/main/DynFlags compiler/simplCore/CoreMonad compiler/simplCore/SimplCore)
- Implementing the LLF logic in the first two FloatOut? phases (compiler/simplCore/SetLevels)
- Adding LLF to the core2core pipeline (compiler/simplCore/SimplCore)
In order to minimize factors, I decided to float only lambdas during LLF. Thus there is no need to perform FloatIn? afterwards: all of our floats are to the top-level, so there will be nothing to FloatIn?.
We placed LLF as the last pass before CorePrep?. After experimentation, we decided to follow it with a simplifier pass.
The basic shape of things:
outer = CTX[let f x = RHS[x] in BODY[f]]
where outer is a top-level binding. LLF transforms this to:
poly_f FVS x = RHS[x] outer = CTX[BODY[f FVS]]
wbere FVS are the free variables of RHS[x]. We'll use a, b, c, ... for particular variables in FVS.
The poly prefix is vestigial: in the past, floated bindings could never cross lambdas, so the abstracted variables were only type variables. Hence the machinery that adds the new parameters was only ever adding type parameters; it was creating polymorphic functions. This scheme was not updated even when the machinery was enriched to also abstract over values.
- join points
- Note [join point abstraction]
Discovered Detriments of LLF
These are the various negative consequences that we discovered on the way. We discuss mitigation below.
- Unapplied occurrences of f in BODY results in the creation of PAPs, which increases allocation. For example: map f xs becomes map (poly_f a b c) xs. Max had identified this issue earlier.
- Abstracting over a known function might change a fast entry call in RHS to a slow entry call. For example, if CTX binds a to a lambda, that information is lost in the right-hand side of poly_f. This can increase runtime.
- Replacing a floated binder's occurrence (ie f becomes poly_f a b c) can add free variables to a thunk's closure, which increases allocation.
- TODO putStr (eg sphere)
Mitigating PAP Creation
This is the simplest to mitigate: we do not float f if it ever occurs unapplied.
Mitigating Thunk Growth
In nucleic2, we floated a binding with 11 free variables. But this binder occurred in about 60 thunks, so many closures grew by ~11 pointers, giving a +2.2% allocation change (as opposed to -0.9%).
We've considered three heuristics for avoiding this. In ascending complexity:
- (easy) Limit the number of free variables the binding is allowed.
- in-thunk: If f occurs inside of a thunk in BODY, then limit its free variables.
- thunk-growth: Approximate the maximum number of free variables that floating f would add to a thunk in BODY, and limit that.
We did not implement the first one, since in-thunk is not very complicated. thunk-growth is significantly more complicated.
- The question of whether f occurs in a thunk is not simple.
- We count non-trivial arguments as thunks; but not all non-trivial arguments end up as thunks.
- We do not count lambda-forms as thunks, since the lambda will hopefully be floated.
- Estimating the effect of floating f on such a thunk's (call it t) closure size is more complicated.
- Another floated function (say g) may also add some of f's free variables to t; we shouldn't penal both f and g for that.
- If f itself has a free variable, say h, which is a binder that gets floated, then floating f will also add h's free variables to t.
Therefore, these are rough approximations. Being more accurate would require changing the setLevels pass instead of just the simpler first pass (the one that only analyzes the original term).
We tried limits of 32, 16, 8, and 4 to differentiate between the last two. At a limit of 8, the allocation increase in ida and puzzle were 1.3 and 2.9 percentage points better with thunk-growth than with in-thunk. But there were no differences around 10 --- which is the lowest we can go while improving nucleic2, anyway --- so we're adopting in-thunk for now.
There might have been some potential benefits to run-time from thunk-growth versus in-thunk (with limit=8, 35 percentage points better on constraint, eg), but we're not confident in those measurements.
Preserving Fast Entries
Using -flate-float-in-thunk-limit=10, -fprotect-last-arg, and -O1, I tested the libraries+NoFib? for four variants.
- nn - do not float a binding that applies one of its free variables.
- yn - do not float a binding that applies one of its free variables saturated or oversaturated.
- ny - do not float a binding that applies one of its free variables undersaturated.
- yy - do not restrict application of bindings free variables
Roughly, we expect that more floating means (barely) less allocation but worse runtime (by how much?) because some known calls become unknown calls.
I discovered this while experimenting with the fast preservation variants above.
TODO Mitigate it.
In fish (1.6%), hpg (~4.5%), and sphere (10.4%), allocation gets worse for ny and yy compared to nn and yn. The nn and ny do not change the allocation compared to the baseline library (ie no LLF).
The nn -> ny comparison is counter to our rough idea: floating more bindings (those that saturate/oversaturate some free variables) worsens allocation. Thus, I investigate.
The sphere program hammers hPutStr. Its extra allocation is mostly due to a regression in GHC.IO.Encoding.UTF8. Here's the situation.
With the nn variant:
outer a b c ... = let-no-escape f x = CTX[let-no-escape $j y = ... (f ...) ... in CTX2[$j]] in ...
In this case, $j is not floated because it applies f. With the ny variant, $j gets floated.
poly_$j a b c ... f y = ... outer a b c ... = let f x = CTX[CTX2[poly_$j a b c ... f]] in ...
Thus f cannot be let-no-escape because it now occurs as an argument to poly_$j.
This contributes to sphere's 1 megabyte of extra allocation for two reasons:
- outer is entered about 60,000 times.
- The RHS of f has 13 free variables, so it's closure is rather large.
13*60,000 ~ 750,000. I suspect the rest of sphere's increase is due to a similar issue in GHC.IO.Handle.
In hpg, it's principally due to GHC.IO.Encoding.UTF8 again, with a second place contributor of GHC.IO.FD, where the function $wa17 is again like the outer example above, but with fewer free variables and thus less effect.
Discovered Benefits of LLF
We didn't see as much decrease in allocation as we would have liked.
- nice demonstration of the basic effect in puzzle
- #7663 - simulates having the inliner discount for free variables like it discounts for parameters
- Floating functions to the top-level creates more opportunities for the simplifier.
CTX[case a of [p1 -> let f x = ... in case a of ...]]
The let prevents the cases from being merged. Since LLF is so aggressive, it floats f when it otherwise wouldn't be.
Thunk Join Points
We discovered that the worker-wrapper was removing the void argument from join points (eg knights and mandel2). This ultimately resulted in LLF *increasing* allocation. A thunk was let-no-escape before LLF but not after, since it occurred free in the right-hand side of a floated binding and hence now occurred (escapingly) as an argument.
SPJ was expecting no such non-lambda join points to exist. We identified where it was happening (WwLib?.mkWorkerArgs) and switched it off. Here are the programs that with affected allocation.
protect-no = allow wwlib to remove the last value argument (ie the previous behavior) protect-yes = protect the last value argument from being removed (ie the experimental behavior) Both are applied to both the libraries and the program. Allocations ------------------------------------------------------------------------------- Program protect-no protect-yes ------------------------------------------------------------------------------- circsim 1326468688 -0.7% hidden 1165299720 -0.7% scs 1029909256 -0.1% transform 738757608 -0.1% cacheprof 478120432 +0.3% listcopy 334710912 -0.4% comp_lab_zift 330889440 -5.0% fulsom 321534872 -0.3% listcompr 304662896 -0.4% anna 70685104 +0.1% gamteb 59846096 -0.3% parser 32406448 +0.2% gg 8970344 -0.2% -1 s.d. ----- -0.6% +1 s.d. ----- +0.5% Average ----- -0.1%
In circsim, put gets 1,000,000 better and Text.Read.Lex gets a tiny bit better.
In hidden, it's Text.Read.Lex, Text.ParserCombinators?.ReadP, and GHC.Read.
In cacheprof, $wpreparseline gets a bit worse.
In comp_lab_zift: f_make_tree gets 2,060,000 better and f_union_br gets 1,500 better.
In parser, Main.leLex gets worse (5000).
In gg, Graph.$wprintFloat gets worse (12 -> 84).
Bigger swings in allocation (mostly good) took place in the programs themselves (eg transform.f_prmdef ~130,000 better, listcopy.f_se ~150,000 better).
Many of the Core differences were of the following form. For example, see circsim's put function. When protecting the last argument from being removed by WwLib.mkWorkerArgs, the Core looks like this:
let x :: RealWorld# -> ... x = \_void -> let d = ... in Ctor(... d ...) (... d ...) ... in CTX[x]
Without protection, it looks like:
let d = ... in CTX[Ctor(... d ...) (... d ...) ...]
Simon explained that it is probably the simplifier floating d out of the unprotected x binding *in order to reveal x as let-bound to a constructor*. Thus revealed, x is immediately inlined. Because of the \_void, this doesn't happen when the last argument is protected.
With protection, d isn't allocated unless x is entered, which might not always happen in CTX. This is a potential win because x might be let-no-escape.
A potential detriment of protection is that x is not exposed as a let-bound constructor. Simon conjectures that's not actually harmful because ... I CANNOT REMEMBER.
SPJ thought this may be another means of hoisting the let-no-escape functionality from the code generator into the core2core pipeline. LLF would handle let-no-escape lambdas, but it does not handle let-no-escape thunks (which we didn't initially realize were being identified).
Changing the let-no-escape thunks to \void -> ... closures upstream would then subject the binding to more optimisations. Formerly, it's non-lambda status meant that inlining it, eg, would lose sharing.