88 lines
3.6 KiB
C++
88 lines
3.6 KiB
C++
//===- Threads.h ------------------------------------------------*- C++ -*-===//
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//
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// The LLVM Linker
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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//
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// LLD supports threads to distribute workloads to multiple cores. Using
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// multicore is most effective when more than one core are idle. At the
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// last step of a build, it is often the case that a linker is the only
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// active process on a computer. So, we are naturally interested in using
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// threads wisely to reduce latency to deliver results to users.
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//
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// That said, we don't want to do "too clever" things using threads.
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// Complex multi-threaded algorithms are sometimes extremely hard to
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// reason about and can easily mess up the entire design.
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//
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// Fortunately, when a linker links large programs (when the link time is
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// most critical), it spends most of the time to work on massive number of
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// small pieces of data of the same kind, and there are opportunities for
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// large parallelism there. Here are examples:
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//
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// - We have hundreds of thousands of input sections that need to be
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// copied to a result file at the last step of link. Once we fix a file
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// layout, each section can be copied to its destination and its
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// relocations can be applied independently.
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//
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// - We have tens of millions of small strings when constructing a
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// mergeable string section.
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//
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// For the cases such as the former, we can just use parallel_for_each
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// instead of std::for_each (or a plain for loop). Because tasks are
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// completely independent from each other, we can run them in parallel
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// without any coordination between them. That's very easy to understand
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// and reason about.
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//
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// For the cases such as the latter, we can use parallel algorithms to
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// deal with massive data. We have to write code for a tailored algorithm
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// for each problem, but the complexity of multi-threading is isolated in
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// a single pass and doesn't affect the linker's overall design.
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//
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// The above approach seems to be working fairly well. As an example, when
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// linking Chromium (output size 1.6 GB), using 4 cores reduces latency to
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// 75% compared to single core (from 12.66 seconds to 9.55 seconds) on my
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// Ivy Bridge Xeon 2.8 GHz machine. Using 40 cores reduces it to 63% (from
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// 12.66 seconds to 7.95 seconds). Because of the Amdahl's law, the
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// speedup is not linear, but as you add more cores, it gets faster.
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//
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// On a final note, if you are trying to optimize, keep the axiom "don't
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// guess, measure!" in mind. Some important passes of the linker are not
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// that slow. For example, resolving all symbols is not a very heavy pass,
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// although it would be very hard to parallelize it. You want to first
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// identify a slow pass and then optimize it.
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//
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//===----------------------------------------------------------------------===//
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#ifndef LLD_ELF_THREADS_H
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#define LLD_ELF_THREADS_H
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#include "Config.h"
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#include "llvm/Support/Parallel.h"
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#include <functional>
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namespace lld {
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namespace elf {
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template <typename R, class FuncTy> void parallelForEach(R &&Range, FuncTy Fn) {
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if (Config->Threads)
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for_each(llvm::parallel::par, std::begin(Range), std::end(Range), Fn);
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else
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for_each(llvm::parallel::seq, std::begin(Range), std::end(Range), Fn);
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}
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inline void parallelForEachN(size_t Begin, size_t End,
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std::function<void(size_t)> Fn) {
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if (Config->Threads)
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for_each_n(llvm::parallel::par, Begin, End, Fn);
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else
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for_each_n(llvm::parallel::seq, Begin, End, Fn);
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}
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} // namespace elf
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} // namespace lld
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#endif
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