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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#if defined(EIGEN_USE_THREADS) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H)
#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
// Runs an arbitrary function and then calls Notify() on the passed in
// Notification.
template <typename Function, typename... Args> struct FunctionWrapperWithNotification
{
static void run(Notification* n, Function f, Args... args) {
f(args...);
if (n) {
n->Notify();
}
}
};
template <typename Function, typename... Args> struct FunctionWrapperWithBarrier
{
static void run(Barrier* b, Function f, Args... args) {
f(args...);
if (b) {
b->Notify();
}
}
};
template <typename SyncType>
static EIGEN_STRONG_INLINE void wait_until_ready(SyncType* n) {
if (n) {
n->Wait();
}
}
// An abstract interface to a device specific memory allocator.
class Allocator {
public:
virtual ~Allocator() {}
virtual void* allocate(size_t num_bytes) const = 0;
virtual void deallocate(void* buffer) const = 0;
};
// Build a thread pool device on top the an existing pool of threads.
struct ThreadPoolDevice {
// The ownership of the thread pool remains with the caller.
ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores, Allocator* allocator = nullptr)
: pool_(pool), num_threads_(num_cores), allocator_(allocator) { }
EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
return allocator_ ? allocator_->allocate(num_bytes)
: internal::aligned_malloc(num_bytes);
}
EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
if (allocator_) {
allocator_->deallocate(buffer);
} else {
internal::aligned_free(buffer);
}
}
EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
return allocate(num_bytes);
}
EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
deallocate(buffer);
}
template<typename Type>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const {
return data;
}
EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
#ifdef __ANDROID__
::memcpy(dst, src, n);
#else
// TODO(rmlarsen): Align blocks on cache lines.
// We have observed that going beyond 4 threads usually just wastes
// CPU cycles due to the threads competing for memory bandwidth, so we
// statically schedule at most 4 block copies here.
const size_t kMinBlockSize = 32768;
const size_t num_threads = CostModel::numThreads(n, TensorOpCost(1.0, 1.0, 0), 4);
if (n <= kMinBlockSize || num_threads < 2) {
::memcpy(dst, src, n);
} else {
const char* src_ptr = static_cast<const char*>(src);
char* dst_ptr = static_cast<char*>(dst);
const size_t blocksize = (n + (num_threads - 1)) / num_threads;
Barrier barrier(static_cast<int>(num_threads - 1));
// Launch the last 3 blocks on worker threads.
for (size_t i = 1; i < num_threads; ++i) {
enqueue_with_barrier(&barrier, [n, i, src_ptr, dst_ptr, blocksize] {
::memcpy(dst_ptr + i * blocksize, src_ptr + i * blocksize,
numext::mini(blocksize, n - (i * blocksize)));
});
}
// Launch the first block on the main thread.
::memcpy(dst_ptr, src_ptr, blocksize);
barrier.Wait();
}
#endif
}
EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
memcpy(dst, src, n);
}
EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
memcpy(dst, src, n);
}
EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
::memset(buffer, c, n);
}
template<typename T>
EIGEN_STRONG_INLINE void fill(T* begin, T* end, const T& value) const {
std::fill(begin, end, value);
}
EIGEN_STRONG_INLINE int numThreads() const {
return num_threads_;
}
// Number of theads available in the underlying thread pool. This number can
// be different from the value returned by numThreads().
EIGEN_STRONG_INLINE int numThreadsInPool() const {
return pool_->NumThreads();
}
EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
return l1CacheSize();
}
EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
// The l3 cache size is shared between all the cores.
return l3CacheSize() / num_threads_;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
// Should return an enum that encodes the ISA supported by the CPU
return 1;
}
template <class Function, class... Args>
EIGEN_STRONG_INLINE Notification* enqueue(Function&& f,
Args&&... args) const {
Notification* n = new Notification();
pool_->Schedule(
std::bind(&FunctionWrapperWithNotification<Function, Args...>::run, n,
std::move(f), args...));
return n;
}
template <class Function, class... Args>
EIGEN_STRONG_INLINE void enqueue_with_barrier(Barrier* b, Function&& f,
Args&&... args) const {
pool_->Schedule(
std::bind(&FunctionWrapperWithBarrier<Function, Args...>::run, b,
std::move(f), args...));
}
template <class Function, class... Args>
EIGEN_STRONG_INLINE void enqueueNoNotification(Function&& f,
Args&&... args) const {
if (sizeof...(args) > 0) {
pool_->Schedule(std::bind(std::move(f), args...));
} else {
pool_->Schedule(std::move(f));
}
}
// Returns a logical thread index between 0 and pool_->NumThreads() - 1 if
// called from one of the threads in pool_. Returns -1 otherwise.
EIGEN_STRONG_INLINE int currentThreadId() const {
return pool_->CurrentThreadId();
}
// WARNING: This function is synchronous and will block the calling thread.
//
// Synchronous parallelFor executes f with [0, n) arguments in parallel and
// waits for completion. F accepts a half-open interval [first, last). Block
// size is chosen based on the iteration cost and resulting parallel
// efficiency. If block_align is not nullptr, it is called to round up the
// block size.
void parallelFor(Index n, const TensorOpCost& cost,
std::function<Index(Index)> block_align,
std::function<void(Index, Index)> f) const {
if (EIGEN_PREDICT_FALSE(n <= 0)){
return;
// Compute small problems directly in the caller thread.
} else if (n == 1 || numThreads() == 1 ||
CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
f(0, n);
return;
}
// Compute block size and total count of blocks.
ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
// Recursively divide size into halves until we reach block_size.
// Division code rounds mid to block_size, so we are guaranteed to get
// block_count leaves that do actual computations.
Barrier barrier(static_cast<unsigned int>(block.count));
std::function<void(Index, Index)> handleRange;
handleRange = [=, &handleRange, &barrier, &f](Index firstIdx,
Index lastIdx) {
while (lastIdx - firstIdx > block.size) {
// Split into halves and schedule the second half on a different thread.
const Index midIdx = firstIdx + divup((lastIdx - firstIdx) / 2, block.size) * block.size;
pool_->Schedule([=, &handleRange]() { handleRange(midIdx, lastIdx); });
lastIdx = midIdx;
}
// Single block or less, execute directly.
f(firstIdx, lastIdx);
barrier.Notify();
};
if (block.count <= numThreads()) {
// Avoid a thread hop by running the root of the tree and one block on the
// main thread.
handleRange(0, n);
} else {
// Execute the root in the thread pool to avoid running work on more than
// numThreads() threads.
pool_->Schedule([=, &handleRange]() { handleRange(0, n); });
}
barrier.Wait();
}
// Convenience wrapper for parallelFor that does not align blocks.
void parallelFor(Index n, const TensorOpCost& cost,
std::function<void(Index, Index)> f) const {
parallelFor(n, cost, nullptr, std::move(f));
}
// WARNING: This function is asynchronous and will not block the calling thread.
//
// Asynchronous parallelFor executes f with [0, n) arguments in parallel
// without waiting for completion. When the last block finished, it will call
// 'done' callback. F accepts a half-open interval [first, last). Block size
// is chosen based on the iteration cost and resulting parallel efficiency. If
// block_align is not nullptr, it is called to round up the block size.
void parallelForAsync(Index n, const TensorOpCost& cost,
std::function<Index(Index)> block_align,
std::function<void(Index, Index)> f,
std::function<void()> done) const {
// Compute small problems directly in the caller thread.
if (n <= 1 || numThreads() == 1 ||
CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
f(0, n);
done();
return;
}
// Compute block size and total count of blocks.
ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
ParallelForAsyncContext* const ctx =
new ParallelForAsyncContext(block.count, std::move(f), std::move(done));
// Recursively divide size into halves until we reach block_size.
// Division code rounds mid to block_size, so we are guaranteed to get
// block_count leaves that do actual computations.
ctx->handle_range = [this, ctx, block](Index firstIdx, Index lastIdx) {
while (lastIdx - firstIdx > block.size) {
// Split into halves and schedule the second half on a different thread.
const Index midIdx = firstIdx + divup((lastIdx - firstIdx) / 2, block.size) * block.size;
pool_->Schedule(
[ctx, midIdx, lastIdx]() { ctx->handle_range(midIdx, lastIdx); });
lastIdx = midIdx;
}
// Single block or less, execute directly.
ctx->f(firstIdx, lastIdx);
// Delete async context if it was the last block.
if (ctx->count.fetch_sub(1) == 1) delete ctx;
};
if (block.count <= numThreads()) {
// Avoid a thread hop by running the root of the tree and one block on the
// main thread.
ctx->handle_range(0, n);
} else {
// Execute the root in the thread pool to avoid running work on more than
// numThreads() threads.
pool_->Schedule([ctx, n]() { ctx->handle_range(0, n); });
}
}
// Convenience wrapper for parallelForAsync that does not align blocks.
void parallelForAsync(Index n, const TensorOpCost& cost,
std::function<void(Index, Index)> f,
std::function<void()> done) const {
parallelForAsync(n, cost, nullptr, std::move(f), std::move(done));
}
// Thread pool accessor.
ThreadPoolInterface* getPool() const { return pool_; }
// Allocator accessor.
Allocator* allocator() const { return allocator_; }
private:
typedef TensorCostModel<ThreadPoolDevice> CostModel;
// For parallelForAsync we must keep passed in closures on the heap, and
// delete them only after `done` callback finished.
struct ParallelForAsyncContext {
ParallelForAsyncContext(Index block_count,
std::function<void(Index, Index)> block_f,
std::function<void()> done_callback)
: count(block_count),
f(std::move(block_f)),
done(std::move(done_callback)) {}
~ParallelForAsyncContext() { done(); }
std::atomic<Index> count;
std::function<void(Index, Index)> f;
std::function<void()> done;
std::function<void(Index, Index)> handle_range;
};
struct ParallelForBlock {
Index size; // block size
Index count; // number of blocks
};
// Calculates block size based on (1) the iteration cost and (2) parallel
// efficiency. We want blocks to be not too small to mitigate parallelization
// overheads; not too large to mitigate tail effect and potential load
// imbalance and we also want number of blocks to be evenly dividable across
// threads.
ParallelForBlock CalculateParallelForBlock(
const Index n, const TensorOpCost& cost,
std::function<Index(Index)> block_align) const {
const double block_size_f = 1.0 / CostModel::taskSize(1, cost);
const Index max_oversharding_factor = 4;
Index block_size = numext::mini(
n, numext::maxi<Index>(
divup<Index>(n, max_oversharding_factor * numThreads()),
block_size_f));
const Index max_block_size = numext::mini(n, 2 * block_size);
if (block_align) {
Index new_block_size = block_align(block_size);
eigen_assert(new_block_size >= block_size);
block_size = numext::mini(n, new_block_size);
}
Index block_count = divup(n, block_size);
// Calculate parallel efficiency as fraction of total CPU time used for
// computations:
double max_efficiency =
static_cast<double>(block_count) /
(divup<int>(block_count, numThreads()) * numThreads());
// Now try to increase block size up to max_block_size as long as it
// doesn't decrease parallel efficiency.
for (Index prev_block_count = block_count;
max_efficiency < 1.0 && prev_block_count > 1;) {
// This is the next block size that divides size into a smaller number
// of blocks than the current block_size.
Index coarser_block_size = divup(n, prev_block_count - 1);
if (block_align) {
Index new_block_size = block_align(coarser_block_size);
eigen_assert(new_block_size >= coarser_block_size);
coarser_block_size = numext::mini(n, new_block_size);
}
if (coarser_block_size > max_block_size) {
break; // Reached max block size. Stop.
}
// Recalculate parallel efficiency.
const Index coarser_block_count = divup(n, coarser_block_size);
eigen_assert(coarser_block_count < prev_block_count);
prev_block_count = coarser_block_count;
const double coarser_efficiency =
static_cast<double>(coarser_block_count) /
(divup<int>(coarser_block_count, numThreads()) * numThreads());
if (coarser_efficiency + 0.01 >= max_efficiency) {
// Taking it.
block_size = coarser_block_size;
block_count = coarser_block_count;
if (max_efficiency < coarser_efficiency) {
max_efficiency = coarser_efficiency;
}
}
}
return {block_size, block_count};
}
ThreadPoolInterface* pool_;
int num_threads_;
Allocator* allocator_;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H