[llvm-dev] Register Allocation Graph Coloring algorithm and Others

Mon Dec 18 18:07:55 PST 2017


> On Dec 18, 2017, at 9:52 AM, Leslie Zhai via llvm-dev <llvm-dev at lists.llvm.org> wrote:
> 
> Hi David,
> 
> Thanks for your teaching!
> 
> I am a newbie in compiler area, I only learned Compiler Principle in 2002 https://www.leetcode.cn/2017/12/ilove-compiler-principle.html <https://www.leetcode.cn/2017/12/ilove-compiler-principle.html>
> 
> But I like to practice and learn :) https://github.com/xiangzhai/llvm/blob/avr/lib/CodeGen/RegAllocGraphColoring.cpp#L327 <https://github.com/xiangzhai/llvm/blob/avr/lib/CodeGen/RegAllocGraphColoring.cpp#L327> because theory is not always correct, or misunderstood by people, so I want to compare solutionByHEA, IRA, Greedy, PBQP and other algorithms.

Just as another tip:
- Indeed in my experience: Just implementing some algorithms yourself and comparing them against what existing compilers produce and then figuring out why is the best way to learn about allocators.
- Don't just put emphasis on all the different different coloring techniques. In practice it is usually the way you deal register constraints and other target specific coloring constraints, rematerialization and how you get coalescing into the picture. Most regalloc papers don't talk too much about that as it's highly finicky and often target specific. But they do have a huge impact on allocation quality and can make or break any of those algorithms...

- Matthias

> 
> Thanks for your lessons to correct my mistakes, such as memory=bad register=good, and I need to find the answer *when* memory is worse than a register, I am maintaining AVR target, there are 32 general registers, 32K flash, 2K sram http://www.atmel.com/Images/Atmel-42735-8-bit-AVR-Microcontroller-ATmega328-328P_Datasheet.pdf <http://www.atmel.com/Images/Atmel-42735-8-bit-AVR-Microcontroller-ATmega328-328P_Datasheet.pdf> so perhaps to MCU, memory might be expensive than register? but what about AMDGPU or VLIW processors? I don't have experienced on them, please teach me.
> 
> I am reading LLVM's code SpillXXX, LiveRangeXXX, RegisterCoalescer, etc. to get the whole view of CodeGen.
> 
> I am reading Dr. Rhydian Lewis's book: A Guide to Graph Colouring: Algorithms and Applications http://www.springer.com/us/book/9783319257280 <http://www.springer.com/us/book/9783319257280>   and other papers, even if HEA is not the best solution, I still want to practice and see the benchmark, I am not computing professionals, I am only 34 olds, perhaps I have enough time to waste :)
> 
> 
> 在 2017年12月19日 00:41, dag at cray.com <mailto:dag at cray.com> 写道:
>> Leslie Zhai <lesliezhai at llvm.org.cn> writes:
>> 
>>> * Memory (20 - 100 cycles) is expensive than Register (1 cycle), but
>>> it has to spill code when PhysReg is unavailable
>> As Vladimir said, the cache makes this kind of analysis much more
>> tricky.  It's not necessarily the case that memory=bad and
>> register=good.  Since there are tradeoffs involved, one needs to
>> evaluate different strategies to determine *when* memory is worse than a
>> register.  It may very well be the case that leaving something in memory
>> frees up a register for something much more important to use it.  All of
>> the register allocation algorithms try to determine this kind of thing
>> through various heuristics.  Which heuristic is most effective is highly
>> target-dependent.
>> 
>> In my experience, changing heuristics can swing performance 20% or more
>> in some cases.  Today's processors and memory systems are so complex
>> that 2nd- and even 3rd-order effects become important.
>> 
>> It is very, very wrong on today's machines to use # of spills as a
>> metric to determine the "goodness" of an allocation.  Determining *what*
>> to spill is much more important than the raw number of spills.  Many
>> times I have a seen codes generated with more spills perform much better
>> than the code generated with fewer spills.  Almost all of the papers
>> around the time of Chaiten-Briggs used # of spills as the metric.  That
>> may have been appropriate at that time but take those results with giant
>> grains of salt today.  Of course they are still very good papers for
>> understanding algorithms and heuristics.
>> 
>> The best way I know to determine what's best for your target is to run a
>> whole bunch of *real* codes on them, trying different allocation
>> algorithms and heuristics.  It is a lot of work, still worthy of a
>> Ph.D. even today.  Register allocation is not a "solved" problem and
>> probably never will be as architectures continue to change and become
>> ever more diverse.
>> 
>>> * Folding spill code into instructions, handling register coallescing,
>>> splitting live ranges, doing rematerialization, doing shrink wrapping
>>> are harder than RegAlloc
>> Again, as Vladimir said, they are all part of register allocation.
>> Sometimes they are implemented as separate passes but logically they all
>> contribute work to the task of assigning registers as efficiently as
>> possible.  And all of them use heuristics.  Choosing when and when not
>> to, for example, coalesce can be important.  Splitting live ranges is
>> logically the opposite of coalescing.  Theoretically one can "undo" a
>> bad coalescing decision by re-splitting the live range but it's usually
>> not that simple as other decisions in the interim can make that tricky
>> if not impossible.  It is a delicate balancing act.
>> 
>>> * The papers by Briggs and Chaiten contradict[2] themselves when
>>> examine the text of the paper vs. the pseudocode provided?
>> As others have said, I don't recall any inconsistencies but that doesn't
>> mean there aren't bugs and/or incomplete descriptions open to
>> interpretation.  One of my biggest issues with our computer academic
>> culture is that we do not value repeatability.  It is virtually
>> impossible to take a paper, implement the algorithm as described (or as
>> best as possible given ambiguity) and obtain the same results.  Heck,
>> half my Ph.D. dissertation was dissecting a published paper, examining
>> the sensitivity of the described algorithm to various parts of the
>> described heuristic that were ambiguous.  By interpreting the heuristic
>> description in different ways I observed very different results.  I read
>> papers for the algorithms, heuristics and ideas in them.  I pay no
>> attention to results because in the real world we have to implement the
>> ideas and test them ourselves to see if they will help us.
>> 
>> Peter is right to point you to Preston.  He is very accessible, friendly
>> and helpful.  I had the good fortune to work with him for a few years
>> and he taught me a lot.  He has much real-world experience on codes
>> actually used in production.  That experience is gold.
>> 
>> Good luck to you!  You're entering a world that some computing
>> professionals think is a waste of time because "we already know how to
>> do that."  Those of us in the trenches know better.  :)
>> 
>>                                -David
> 
> -- 
> Regards,
> Leslie Zhai - https://reviews.llvm.org/p/xiangzhai/ <https://reviews.llvm.org/p/xiangzhai/>
> 
> 
> 
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