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Improved the performance of full reductions.

Browse Source 
AFTER:
BM_fullReduction/10        4541       4543     154017  21.0M items/s
BM_fullReduction/64        5191       5193     100000  752.5M items/s
BM_fullReduction/512       9588       9588      71361  25.5G items/s
BM_fullReduction/4k      244314     244281       2863  64.0G items/s
BM_fullReduction/5k      359382     359363       1946  64.8G items/s

BEFORE:
BM_fullReduction/10        9085       9087      74395  10.5M items/s
BM_fullReduction/64        9478       9478      72014  412.1M items/s
BM_fullReduction/512      14643      14646      46902  16.7G items/s
BM_fullReduction/4k      260338     260384       2678  60.0G items/s
BM_fullReduction/5k      385076     385178       1818  60.5G items/s
This commit is contained in:
Benoit Steiner 2016-06-03 17:27:08 -07:00
parent 8d97ba6b22
commit c2a102345f
3 changed files with 76 additions and 17 deletions
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37
unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
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@ -12,6 +12,8 @@
namespace Eigen {
static const int kCudaScratchSize = 1024;
// This defines an interface that GPUDevice can take to use
// CUDA streams underneath.
class StreamInterface {
@ -27,6 +29,12 @@ class StreamInterface {
// Return a scratchpad buffer of size 1k
virtual void* scratchpad() const = 0;
// Return a semaphore. The semaphore is initially initialized to 0, and
// each kernel using it is responsible for resetting to 0 upon completion
// to maintain the invariant that the semaphore is always equal to 0 upon
// each kernel start.
virtual unsigned int* semaphore() const = 0;
};
static cudaDeviceProp* m_deviceProperties;
@ -65,12 +73,12 @@ static const cudaStream_t default_stream = cudaStreamDefault;
class CudaStreamDevice : public StreamInterface {
public:
// Use the default stream on the current device
CudaStreamDevice() : stream_(&default_stream), scratch_(NULL) {
CudaStreamDevice() : stream_(&default_stream), scratch_(NULL), semaphore_(NULL) {
cudaGetDevice(&device_);
initializeDeviceProp();
}
// Use the default stream on the specified device
CudaStreamDevice(int device) : stream_(&default_stream), device_(device), scratch_(NULL) {
CudaStreamDevice(int device) : stream_(&default_stream), device_(device), scratch_(NULL), semaphore_(NULL) {
initializeDeviceProp();
}
// Use the specified stream. Note that it's the
@ -78,7 +86,7 @@ class CudaStreamDevice : public StreamInterface {
// the specified device. If no device is specified the code
// assumes that the stream is associated to the current gpu device.
CudaStreamDevice(const cudaStream_t* stream, int device = -1)
: stream_(stream), device_(device), scratch_(NULL) {
: stream_(stream), device_(device), scratch_(NULL), semaphore_(NULL) {
if (device < 0) {
cudaGetDevice(&device_);
} else {
@ -123,15 +131,27 @@ class CudaStreamDevice : public StreamInterface {
virtual void* scratchpad() const {
if (scratch_ == NULL) {
scratch_ = allocate(1024);
scratch_ = allocate(kCudaScratchSize + sizeof(unsigned int));
}
return scratch_;
}
virtual unsigned int* semaphore() const {
if (semaphore_ == NULL) {
char* scratch = static_cast<char*>(scratchpad()) + kCudaScratchSize;
semaphore_ = reinterpret_cast<unsigned int*>(scratch);
cudaError_t err = cudaMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_);
EIGEN_UNUSED_VARIABLE(err)
assert(err == cudaSuccess);
}
return semaphore_;
}
private:
const cudaStream_t* stream_;
int device_;
mutable void* scratch_;
mutable unsigned int* semaphore_;
};
struct GpuDevice {
@ -174,6 +194,15 @@ struct GpuDevice {
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE unsigned int* semaphore() const {
#ifndef __CUDA_ARCH__
return stream_->semaphore();
#else
eigen_assert(false && "The default device should be used instead to generate kernel code");
return NULL;
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
#ifndef __CUDA_ARCH__
cudaError_t err = cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToDevice,

4
unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
View File

@ -316,7 +316,7 @@ struct OuterReducer {
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
template <int B, int N, typename S, typename R, typename I>
__global__ void FullReductionKernel(R, const S, I, typename S::CoeffReturnType*);
__global__ void FullReductionKernel(R, const S, I, typename S::CoeffReturnType*, unsigned int*);
#ifdef EIGEN_HAS_CUDA_FP16
@ -616,7 +616,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
template <typename S, typename O, bool V> friend struct internal::FullReducerShard;
#endif
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernel(R, const S, I, typename S::CoeffReturnType*);
template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernel(R, const S, I, typename S::CoeffReturnType*, unsigned int*);
#ifdef EIGEN_HAS_CUDA_FP16
template <typename S, typename R, typename I> friend void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I, half2*);
template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernelHalfFloat(R, const S, I, half*, half2*);

52
unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
View File

@ -112,17 +112,40 @@ __global__ void ReductionInitKernel(const CoeffType val, Index num_preserved_coe
}
}
template <int BlockSize, int NumPerThread, typename Self,
typename Reducer, typename Index>
__global__ void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs,
typename Self::CoeffReturnType* output) {
typename Self::CoeffReturnType* output, unsigned int* semaphore) {
// Initialize the output value
const Index first_index = blockIdx.x * BlockSize * NumPerThread + threadIdx.x;
// Initialize the output value if it wasn't initialized by the ReductionInitKernel
if (gridDim.x == 1 && first_index == 0) {
*output = reducer.initialize();
__syncthreads();
if (gridDim.x == 1) {
if (first_index == 0) {
*output = reducer.initialize();
}
}
else {
if (threadIdx.x == 0) {
unsigned int block = atomicCAS(semaphore, 0u, 1u);
if (block == 0) {
// We're the first block to run, initialize the output value
atomicExch(output, reducer.initialize());
unsigned int old = atomicExch(semaphore, 2u);
assert(old == 1u);
}
else {
// Use atomicCAS here to ensure that the reads aren't cached
unsigned int val = atomicCAS(semaphore, 2u, 2u);
while (val < 2u) {
val = atomicCAS(semaphore, 2u, 2u);
}
}
}
}
__syncthreads();
eigen_assert(gridDim.x == 1 || *semaphore >= 2u);
typename Self::CoeffReturnType accum = reducer.initialize();
Index max_iter = numext::mini<Index>(num_coeffs - first_index, NumPerThread*BlockSize);
@ -141,6 +164,15 @@ __global__ void FullReductionKernel(Reducer reducer, const Self input, Index num
if ((threadIdx.x & (warpSize - 1)) == 0) {
atomicReduce(output, accum, reducer);
}
if (gridDim.x > 1 && threadIdx.x == 0) {
unsigned int ticket = atomicInc(semaphore, UINT_MAX);
assert(ticket >= 2u);
if (ticket == gridDim.x + 1) {
// We're the last block, reset the semaphore
*semaphore = 0;
}
}
}
@ -246,15 +278,13 @@ struct FullReductionLauncher<Self, Op, float, PacketAccess> {
const int num_per_thread = 128;
const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
unsigned int* semaphore = NULL;
if (num_blocks > 1) {
// We initialize the outputs outside the reduction kernel when we can't be sure that there
// won't be a race conditions between multiple thread blocks.
LAUNCH_CUDA_KERNEL((ReductionInitKernel<Scalar, Index>),
1, 32, 0, device, reducer.initialize(), 1, output);
semaphore = device.semaphore();
}
LAUNCH_CUDA_KERNEL((FullReductionKernel<block_size, num_per_thread, Self, Op, Index>),
num_blocks, block_size, 0, device, reducer, self, num_coeffs, output);
num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, semaphore);
}
};