cuda-samples/Samples/reduction/reduction_kernel.cu
2019-01-23 01:34:43 +05:30

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/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
*
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* notice, this list of conditions and the following disclaimer in the
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*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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/*
Parallel reduction kernels
*/
#ifndef _REDUCE_KERNEL_H_
#define _REDUCE_KERNEL_H_
#include <cooperative_groups.h>
#include <stdio.h>
namespace cg = cooperative_groups;
// Utility class used to avoid linker errors with extern
// unsized shared memory arrays with templated type
template <class T>
struct SharedMemory {
__device__ inline operator T *() {
extern __shared__ int __smem[];
return (T *)__smem;
}
__device__ inline operator const T *() const {
extern __shared__ int __smem[];
return (T *)__smem;
}
};
// specialize for double to avoid unaligned memory
// access compile errors
template <>
struct SharedMemory<double> {
__device__ inline operator double *() {
extern __shared__ double __smem_d[];
return (double *)__smem_d;
}
__device__ inline operator const double *() const {
extern __shared__ double __smem_d[];
return (double *)__smem_d;
}
};
/*
Parallel sum reduction using shared memory
- takes log(n) steps for n input elements
- uses n threads
- only works for power-of-2 arrays
*/
/* This reduction interleaves which threads are active by using the modulo
operator. This operator is very expensive on GPUs, and the interleaved
inactivity means that no whole warps are active, which is also very
inefficient */
template <class T>
__global__ void reduce0(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// load shared mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = (i < n) ? g_idata[i] : 0;
cg::sync(cta);
// do reduction in shared mem
for (unsigned int s = 1; s < blockDim.x; s *= 2) {
// modulo arithmetic is slow!
if ((tid % (2 * s)) == 0) {
sdata[tid] += sdata[tid + s];
}
cg::sync(cta);
}
// write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
/* This version uses contiguous threads, but its interleaved
addressing results in many shared memory bank conflicts.
*/
template <class T>
__global__ void reduce1(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// load shared mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = (i < n) ? g_idata[i] : 0;
cg::sync(cta);
// do reduction in shared mem
for (unsigned int s = 1; s < blockDim.x; s *= 2) {
int index = 2 * s * tid;
if (index < blockDim.x) {
sdata[index] += sdata[index + s];
}
cg::sync(cta);
}
// write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
/*
This version uses sequential addressing -- no divergence or bank conflicts.
*/
template <class T>
__global__ void reduce2(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// load shared mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = (i < n) ? g_idata[i] : 0;
cg::sync(cta);
// do reduction in shared mem
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (tid < s) {
sdata[tid] += sdata[tid + s];
}
cg::sync(cta);
}
// write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
/*
This version uses n/2 threads --
it performs the first level of reduction when reading from global memory.
*/
template <class T>
__global__ void reduce3(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// perform first level of reduction,
// reading from global memory, writing to shared memory
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * (blockDim.x * 2) + threadIdx.x;
T mySum = (i < n) ? g_idata[i] : 0;
if (i + blockDim.x < n) mySum += g_idata[i + blockDim.x];
sdata[tid] = mySum;
cg::sync(cta);
// do reduction in shared mem
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (tid < s) {
sdata[tid] = mySum = mySum + sdata[tid + s];
}
cg::sync(cta);
}
// write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = mySum;
}
/*
This version uses the warp shuffle operation if available to reduce
warp synchronization. When shuffle is not available the final warp's
worth of work is unrolled to reduce looping overhead.
See
http://devblogs.nvidia.com/parallelforall/faster-parallel-reductions-kepler/
for additional information about using shuffle to perform a reduction
within a warp.
Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory.
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes.
If blockSize > 32, allocate blockSize*sizeof(T) bytes.
*/
template <class T, unsigned int blockSize>
__global__ void reduce4(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// perform first level of reduction,
// reading from global memory, writing to shared memory
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * (blockDim.x * 2) + threadIdx.x;
T mySum = (i < n) ? g_idata[i] : 0;
if (i + blockSize < n) mySum += g_idata[i + blockSize];
sdata[tid] = mySum;
cg::sync(cta);
// do reduction in shared mem
for (unsigned int s = blockDim.x / 2; s > 32; s >>= 1) {
if (tid < s) {
sdata[tid] = mySum = mySum + sdata[tid + s];
}
cg::sync(cta);
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
if (cta.thread_rank() < 32) {
// Fetch final intermediate sum from 2nd warp
if (blockSize >= 64) mySum += sdata[tid + 32];
// Reduce final warp using shuffle
for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
mySum += tile32.shfl_down(mySum, offset);
}
}
// write result for this block to global mem
if (cta.thread_rank() == 0) g_odata[blockIdx.x] = mySum;
}
/*
This version is completely unrolled, unless warp shuffle is available, then
shuffle is used within a loop. It uses a template parameter to achieve
optimal code for any (power of 2) number of threads. This requires a switch
statement in the host code to handle all the different thread block sizes at
compile time. When shuffle is available, it is used to reduce warp
synchronization.
Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory.
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes.
If blockSize > 32, allocate blockSize*sizeof(T) bytes.
*/
template <class T, unsigned int blockSize>
__global__ void reduce5(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// perform first level of reduction,
// reading from global memory, writing to shared memory
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * (blockSize * 2) + threadIdx.x;
T mySum = (i < n) ? g_idata[i] : 0;
if (i + blockSize < n) mySum += g_idata[i + blockSize];
sdata[tid] = mySum;
cg::sync(cta);
// do reduction in shared mem
if ((blockSize >= 512) && (tid < 256)) {
sdata[tid] = mySum = mySum + sdata[tid + 256];
}
cg::sync(cta);
if ((blockSize >= 256) && (tid < 128)) {
sdata[tid] = mySum = mySum + sdata[tid + 128];
}
cg::sync(cta);
if ((blockSize >= 128) && (tid < 64)) {
sdata[tid] = mySum = mySum + sdata[tid + 64];
}
cg::sync(cta);
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
if (cta.thread_rank() < 32) {
// Fetch final intermediate sum from 2nd warp
if (blockSize >= 64) mySum += sdata[tid + 32];
// Reduce final warp using shuffle
for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
mySum += tile32.shfl_down(mySum, offset);
}
}
// write result for this block to global mem
if (cta.thread_rank() == 0) g_odata[blockIdx.x] = mySum;
}
/*
This version adds multiple elements per thread sequentially. This reduces
the overall cost of the algorithm while keeping the work complexity O(n) and
the step complexity O(log n). (Brent's Theorem optimization)
Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory.
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes.
If blockSize > 32, allocate blockSize*sizeof(T) bytes.
*/
template <class T, unsigned int blockSize, bool nIsPow2>
__global__ void reduce6(T *g_idata, T *g_odata, unsigned int n) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
T *sdata = SharedMemory<T>();
// perform first level of reduction,
// reading from global memory, writing to shared memory
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockSize * 2 + threadIdx.x;
unsigned int gridSize = blockSize * 2 * gridDim.x;
T mySum = 0;
// we reduce multiple elements per thread. The number is determined by the
// number of active thread blocks (via gridDim). More blocks will result
// in a larger gridSize and therefore fewer elements per thread
while (i < n) {
mySum += g_idata[i];
// ensure we don't read out of bounds -- this is optimized away for powerOf2
// sized arrays
if (nIsPow2 || i + blockSize < n) mySum += g_idata[i + blockSize];
i += gridSize;
}
// each thread puts its local sum into shared memory
sdata[tid] = mySum;
cg::sync(cta);
// do reduction in shared mem
if ((blockSize >= 512) && (tid < 256)) {
sdata[tid] = mySum = mySum + sdata[tid + 256];
}
cg::sync(cta);
if ((blockSize >= 256) && (tid < 128)) {
sdata[tid] = mySum = mySum + sdata[tid + 128];
}
cg::sync(cta);
if ((blockSize >= 128) && (tid < 64)) {
sdata[tid] = mySum = mySum + sdata[tid + 64];
}
cg::sync(cta);
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
if (cta.thread_rank() < 32) {
// Fetch final intermediate sum from 2nd warp
if (blockSize >= 64) mySum += sdata[tid + 32];
// Reduce final warp using shuffle
for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
mySum += tile32.shfl_down(mySum, offset);
}
}
// write result for this block to global mem
if (cta.thread_rank() == 0) g_odata[blockIdx.x] = mySum;
}
extern "C" bool isPow2(unsigned int x);
////////////////////////////////////////////////////////////////////////////////
// Wrapper function for kernel launch
////////////////////////////////////////////////////////////////////////////////
template <class T>
void reduce(int size, int threads, int blocks, int whichKernel, T *d_idata,
T *d_odata) {
dim3 dimBlock(threads, 1, 1);
dim3 dimGrid(blocks, 1, 1);
// when there is only one warp per block, we need to allocate two warps
// worth of shared memory so that we don't index shared memory out of bounds
int smemSize =
(threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T);
// choose which of the optimized versions of reduction to launch
switch (whichKernel) {
case 0:
reduce0<T><<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 1:
reduce1<T><<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 2:
reduce2<T><<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 3:
reduce3<T><<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 4:
switch (threads) {
case 512:
reduce4<T, 512>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 256:
reduce4<T, 256>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 128:
reduce4<T, 128>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 64:
reduce4<T, 64>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 32:
reduce4<T, 32>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 16:
reduce4<T, 16>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 8:
reduce4<T, 8>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 4:
reduce4<T, 4>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 2:
reduce4<T, 2>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 1:
reduce4<T, 1>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
}
break;
case 5:
switch (threads) {
case 512:
reduce5<T, 512>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 256:
reduce5<T, 256>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 128:
reduce5<T, 128>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 64:
reduce5<T, 64>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 32:
reduce5<T, 32>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 16:
reduce5<T, 16>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 8:
reduce5<T, 8>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 4:
reduce5<T, 4>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 2:
reduce5<T, 2>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 1:
reduce5<T, 1>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
}
break;
case 6:
default:
if (isPow2(size)) {
switch (threads) {
case 512:
reduce6<T, 512, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 256:
reduce6<T, 256, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 128:
reduce6<T, 128, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 64:
reduce6<T, 64, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 32:
reduce6<T, 32, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 16:
reduce6<T, 16, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 8:
reduce6<T, 8, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 4:
reduce6<T, 4, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 2:
reduce6<T, 2, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 1:
reduce6<T, 1, true>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
}
} else {
switch (threads) {
case 512:
reduce6<T, 512, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 256:
reduce6<T, 256, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 128:
reduce6<T, 128, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 64:
reduce6<T, 64, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 32:
reduce6<T, 32, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 16:
reduce6<T, 16, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 8:
reduce6<T, 8, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 4:
reduce6<T, 4, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 2:
reduce6<T, 2, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
case 1:
reduce6<T, 1, false>
<<<dimGrid, dimBlock, smemSize>>>(d_idata, d_odata, size);
break;
}
}
break;
}
}
// Instantiate the reduction function for 3 types
template void reduce<int>(int size, int threads, int blocks, int whichKernel,
int *d_idata, int *d_odata);
template void reduce<float>(int size, int threads, int blocks, int whichKernel,
float *d_idata, float *d_odata);
template void reduce<double>(int size, int threads, int blocks, int whichKernel,
double *d_idata, double *d_odata);
#endif // #ifndef _REDUCE_KERNEL_H_