blob: c03e15253e15b467e91eef545eb81621db6e9fc3 [file] [log] [blame]
//===- Threads.h ------------------------------------------------*- C++ -*-===//
// The LLVM Linker
// This file is distributed under the University of Illinois Open Source
// 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, and there are opportunities for
// large parallelism there. 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
// Ivy Bridge Xeon 2.8 GHz 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.
#include "Config.h"
#include "lld/Core/Parallel.h"
#include <algorithm>
#include <functional>
namespace lld {
namespace elf {
template <class IterTy, class FuncTy>
void forEach(IterTy Begin, IterTy End, FuncTy Fn) {
if (Config->Threads)
parallel_for_each(Begin, End, Fn);
std::for_each(Begin, End, Fn);
inline void forLoop(size_t Begin, size_t End, std::function<void(size_t)> Fn) {
if (Config->Threads) {
parallel_for(Begin, End, Fn);
} else {
for (size_t I = Begin; I < End; ++I)