[lld] r288606 - Add comments about the use of threads in LLD.

[Sean Silva via llvm-commits]

On Sat, Dec 3, 2016 at 3:35 PM, Rui Ueyama via llvm-commits <
llvm-commits at lists.llvm.org> wrote:

> Author: ruiu
> Date: Sat Dec  3 17:35:22 2016
> New Revision: 288606
>
> URL: http://llvm.org/viewvc/llvm-project?rev=288606&view=rev
> Log:
> Add comments about the use of threads in LLD.
>
> Modified:
>     lld/trunk/ELF/Threads.h
>
> Modified: lld/trunk/ELF/Threads.h
> URL: http://llvm.org/viewvc/llvm-project/lld/trunk/ELF/Threads.
> h?rev=288606&r1=288605&r2=288606&view=diff
> ============================================================
> ==================
> --- lld/trunk/ELF/Threads.h (original)
> +++ lld/trunk/ELF/Threads.h Sat Dec  3 17:35:22 2016
> @@ -6,6 +6,54 @@
>  // License. See LICENSE.TXT for details.
>  //
>  //===-------------------------------------------------------
> ---------------===//
> +//
> +// LLD supports threads to distribute workloads to multiple cores. Using
> +// multicore is most effective when more than one core are idle. At the
> +// last step of a build, it is often the case that a linker is the only
> +// active process on a computer. So, we are naturally interested in using
> +// threads wisely to reduce latency to deliver results to users.
> +//
> +// That said, we don't want to do "too clever" things using threads.
> +// Complex multi-threaded algorithms are sometimes extremely hard to
> +// justify the correctness and can easily mess up the entire design.
> +//
> +// Fortunately, when a linker links large programs (when the link time is
> +// most critical), it spends most of the time to work on massive number of
> +// small pieces of data of the same kind. Here are examples:
> +//
> +//  - We have hundreds of thousands of input sections that need to be
> +//    copied to a result file at the last step of link. Once we fix a file
> +//    layout, each section can be copied to its destination and its
> +//    relocations can be applied independently.
> +//
> +//  - We have tens of millions of small strings when constructing a
> +//    mergeable string section.
> +//
> +// For the cases such as the former, we can just use parallel_for_each
> +// instead of std::for_each (or a plain for loop). Because tasks are
> +// completely independent from each other, we can run them in parallel
> +// without any coordination between them. That's very easy to understand
> +// and justify.
> +//
> +// For the cases such as the latter, we can use parallel algorithms to
> +// deal with massive data. We have to write code for a tailored algorithm
> +// for each problem, but the complexity of multi-threading is isolated in
> +// a single pass and doesn't affect the linker's overall design.
> +//
> +// The above approach seems to be working fairly well. As an example, when
> +// linking Chromium (output size 1.6 GB), using 4 cores reduces latency to
> +// 75% compared to single core (from 12.66 seconds to 9.55 seconds) on my
> +// machine. Using 40 cores reduces it to 63% (from 12.66 seconds to 7.95
> +// seconds). Because of the Amdahl's law, the speedup is not linear, but
> +// as you add more cores, it gets faster.
> +//
> +// On a final note, if you are trying to optimize, keep the axiom "don't
> +// guess, measure!" in mind. Some important passes of the linker are not
> +// that slow. For example, resolving all symbols is not a very heavy pass,
> +// although it would be very hard to parallelize it. You want to first
> +// identify a slow pass and then optimize it.
>

Actually, LLD's symbol resolution (the approach with Lazy symbols for
archives) is a perfect example of a MapReduce type problem, so it is
actually quite parallelizable.
You basically have a huge number of (SymbolName,SymbolValue) pairs and you
want to coalesce all values with the same SymbolName into pairs
(SymbolName, [SymbolValue1,SymbolValue2,...]) which you can then process
all the SymbolValueN's to see which is the real definition. This is
precisely the problem that MapReduce solves.

(note: I don't necessarily mean that it needs to be done in a distributed
fashion, just that the core problem is really one of coalescing values with
the same keys.
)

MapReduce's core abstraction is also a good tool for deduplicating strings.


Richard Smith and I were actually brainstorming at the latest llvm social a
distributed linker may be a good fit for the linking problem at Google (but
it was just brainstorming; obviously that would be a huge effort and we
would need very serious justification before embarking on that effort).

-- Sean Silva


> +//
> +//===------------------------------------------------------
> ----------------===//
>
>  #ifndef LLD_ELF_THREADS_H
>  #define LLD_ELF_THREADS_H
>
>
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