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Functions
MapReduce Kernels
Kernels
Collaboration diagram for MapReduce Kernels:

Functions

static std::string skepu::UnaryMapReduceKernel_CL ("__kernel void UnaryMapReduceKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, size_t n, __local TYPE* sdata, CONST_TYPE const1)\n""{\n"" size_t blockSize = get_local_size(0);\n"" size_t tid = get_local_id(0);\n"" size_t i = get_group_id(0)*blockSize + get_local_id(0);\n"" size_t gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = FUNCTIONNAME_MAP(input[i], const1);\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" TYPE tempMap;\n"" tempMap = FUNCTIONNAME_MAP(input[i], const1);\n"" result = FUNCTIONNAME_REDUCE(result, tempMap, (TYPE)0);\n"" i += gridSize;\n"" }\n"" sdata[tid] = result;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(blockSize >= 512) { if (tid < 256 && tid + 256 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 256], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 256) { if (tid < 128 && tid + 128 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 128], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 128) { if (tid < 64 && tid + 64 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 64], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 64) { if (tid < 32 && tid + 32 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 32], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 32) { if (tid < 16 && tid + 16 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 16], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 16) { if (tid < 8 && tid + 8 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 8], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 8) { if (tid < 4 && tid + 4 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 4], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 4) { if (tid < 2 && tid + 2 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 2], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 2) { if (tid < 1 && tid + 1 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 1], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(tid == 0)\n"" {\n"" output[get_group_id(0)] = sdata[tid];\n"" }\n""}\n")
 
static std::string skepu::BinaryMapReduceKernel_CL ("__kernel void BinaryMapReduceKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* output, size_t n, __local TYPE* sdata, CONST_TYPE const1)\n""{\n"" size_t blockSize = get_local_size(0);\n"" size_t tid = get_local_id(0);\n"" size_t i = get_group_id(0)*blockSize + get_local_id(0);\n"" size_t gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = FUNCTIONNAME_MAP(input1[i], input2[i], const1);\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" TYPE tempMap;\n"" tempMap = FUNCTIONNAME_MAP(input1[i], input2[i], const1);\n"" result = FUNCTIONNAME_REDUCE(result, tempMap, (TYPE)0);\n"" i += gridSize;\n"" }\n"" sdata[tid] = result;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(blockSize >= 512) { if (tid < 256 && tid + 256 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 256], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 256) { if (tid < 128 && tid + 128 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 128], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 128) { if (tid < 64 && tid + 64 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 64], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 64) { if (tid < 32 && tid + 32 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 32], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 32) { if (tid < 16 && tid + 16 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 16], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 16) { if (tid < 8 && tid + 8 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 8], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 8) { if (tid < 4 && tid + 4 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 4], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 4) { if (tid < 2 && tid + 2 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 2], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 2) { if (tid < 1 && tid + 1 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 1], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(tid == 0)\n"" {\n"" output[get_group_id(0)] = sdata[tid];\n"" }\n""}\n")
 
static std::string skepu::TrinaryMapReduceKernel_CL ("__kernel void TrinaryMapReduceKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* input3, __global TYPE* output, size_t n, __local TYPE* sdata, CONST_TYPE const1)\n""{\n"" size_t blockSize = get_local_size(0);\n"" size_t tid = get_local_id(0);\n"" size_t i = get_group_id(0)*blockSize + get_local_id(0);\n"" size_t gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = FUNCTIONNAME_MAP(input1[i], input2[i], input3[i], const1);\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" TYPE tempMap;\n"" tempMap = FUNCTIONNAME_MAP(input1[i], input2[i], input3[i], const1);\n"" result = FUNCTIONNAME_REDUCE(result, tempMap, (TYPE)0);\n"" i += gridSize;\n"" }\n"" sdata[tid] = result;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(blockSize >= 512) { if (tid < 256 && tid + 256 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 256], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 256) { if (tid < 128 && tid + 128 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 128], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 128) { if (tid < 64 && tid + 64 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 64], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 64) { if (tid < 32 && tid + 32 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 32], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 32) { if (tid < 16 && tid + 16 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 16], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 16) { if (tid < 8 && tid + 8 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 8], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 8) { if (tid < 4 && tid + 4 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 4], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 4) { if (tid < 2 && tid + 2 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 2], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 2) { if (tid < 1 && tid + 1 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 1], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(tid == 0)\n"" {\n"" output[get_group_id(0)] = sdata[tid];\n"" }\n""}\n")
 
template<typename T , typename UnaryFunc , typename BinaryFunc >
__global__ void skepu::MapReduceKernel1_CU (UnaryFunc mapFunc, BinaryFunc reduceFunc, T *input, T *output, size_t n)
 
template<typename T , typename BinaryFunc1 , typename BinaryFunc2 >
__global__ void skepu::MapReduceKernel2_CU (BinaryFunc1 mapFunc, BinaryFunc2 reduceFunc, T *input1, T *input2, T *output, size_t n)
 
template<typename T , typename TrinaryFunc , typename BinaryFunc >
__global__ void skepu::MapReduceKernel3_CU (TrinaryFunc mapFunc, BinaryFunc reduceFunc, T *input1, T *input2, T *input3, T *output, size_t n)
 

Detailed Description

Definitions of CUDA and OpenCL kernels for the MapReduce skeleton.

Definitions of CUDA and OpenCL kernels for the Reduce skeleton.

Function Documentation

static std::string skepu::BinaryMapReduceKernel_CL ( "__kernel void BinaryMapReduceKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* output, size_t n, __local TYPE* sdata, CONST_TYPE const1)\n""{\n"" size_t blockSize = get_local_size(0);\n"" size_t tid = get_local_id(0);\n"" size_t i = get_group_id(0)*blockSize + get_local_id(0);\n"" size_t gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = FUNCTIONNAME_MAP(input1[i], input2[i], const1);\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" TYPE tempMap;\n"" tempMap = FUNCTIONNAME_MAP(input1[i], input2[i], const1);\n"" result = FUNCTIONNAME_REDUCE(result, tempMap, (TYPE)0);\n"" i += gridSize;\n"" }\n"" sdata[tid] = result;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(blockSize >= 512) { if (tid < 256 && tid + 256 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 256], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 256) { if (tid < 128 && tid + 128 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 128], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 128) { if (tid < 64 && tid + 64 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 64], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 64) { if (tid < 32 && tid + 32 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 32], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 32) { if (tid < 16 && tid + 16 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 16], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 16) { if (tid < 8 && tid + 8 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 8], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 8) { if (tid < 4 && tid + 4 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 4], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 4) { if (tid < 2 && tid + 2 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 2], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 2) { if (tid < 1 && tid + 1 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 1], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(tid == 0)\n"" {\n"" output[get_group_id(0)] = sdata[tid];\n"" }\n""}\n"  )
static

OpenCL MapReduce kernel, using the same pattern as reduce6 in the CUDA SDK. See whitepaper from NVIDIA on optimizing reduction for the GPU. For binary map user functions.

template<typename T , typename UnaryFunc , typename BinaryFunc >
__global__ void skepu::MapReduceKernel1_CU ( UnaryFunc  mapFunc,
BinaryFunc  reduceFunc,
T *  input,
T *  output,
size_t  n 
)

CUDA MapReduce kernel, using the same pattern as reduce6 in the CUDA SDK. See whitepaper from NVIDIA on optimizing reduction for the GPU. For unary map user functions.

Referenced by skepu::MapReduce< MapFunc, ReduceFunc >::CU().

template<typename T , typename BinaryFunc1 , typename BinaryFunc2 >
__global__ void skepu::MapReduceKernel2_CU ( BinaryFunc1  mapFunc,
BinaryFunc2  reduceFunc,
T *  input1,
T *  input2,
T *  output,
size_t  n 
)

CUDA MapReduce kernel, using the same pattern as reduce6 in the CUDA SDK. See whitepaper from NVIDIA on optimizing reduction for the GPU. For binary map user functions.

Referenced by skepu::MapReduce< MapFunc, ReduceFunc >::CU().

template<typename T , typename TrinaryFunc , typename BinaryFunc >
__global__ void skepu::MapReduceKernel3_CU ( TrinaryFunc  mapFunc,
BinaryFunc  reduceFunc,
T *  input1,
T *  input2,
T *  input3,
T *  output,
size_t  n 
)

CUDA MapReduce kernel, using the same pattern as reduce6 in the CUDA SDK. See whitepaper from NVIDIA on optimizing reduction for the GPU. For trinary map user functions.

Referenced by skepu::MapReduce< MapFunc, ReduceFunc >::CU().

static std::string skepu::TrinaryMapReduceKernel_CL ( "__kernel void TrinaryMapReduceKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* input3, __global TYPE* output, size_t n, __local TYPE* sdata, CONST_TYPE const1)\n""{\n"" size_t blockSize = get_local_size(0);\n"" size_t tid = get_local_id(0);\n"" size_t i = get_group_id(0)*blockSize + get_local_id(0);\n"" size_t gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = FUNCTIONNAME_MAP(input1[i], input2[i], input3[i], const1);\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" TYPE tempMap;\n"" tempMap = FUNCTIONNAME_MAP(input1[i], input2[i], input3[i], const1);\n"" result = FUNCTIONNAME_REDUCE(result, tempMap, (TYPE)0);\n"" i += gridSize;\n"" }\n"" sdata[tid] = result;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(blockSize >= 512) { if (tid < 256 && tid + 256 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 256], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 256) { if (tid < 128 && tid + 128 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 128], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 128) { if (tid < 64 && tid + 64 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 64], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 64) { if (tid < 32 && tid + 32 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 32], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 32) { if (tid < 16 && tid + 16 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 16], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 16) { if (tid < 8 && tid + 8 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 8], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 8) { if (tid < 4 && tid + 4 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 4], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 4) { if (tid < 2 && tid + 2 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 2], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 2) { if (tid < 1 && tid + 1 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 1], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(tid == 0)\n"" {\n"" output[get_group_id(0)] = sdata[tid];\n"" }\n""}\n"  )
static

OpenCL MapReduce kernel, using the same pattern as reduce6 in the CUDA SDK. See whitepaper from NVIDIA on optimizing reduction for the GPU. For trinary map user functions.

static std::string skepu::UnaryMapReduceKernel_CL ( "__kernel void UnaryMapReduceKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, size_t n, __local TYPE* sdata, CONST_TYPE const1)\n""{\n"" size_t blockSize = get_local_size(0);\n"" size_t tid = get_local_id(0);\n"" size_t i = get_group_id(0)*blockSize + get_local_id(0);\n"" size_t gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = FUNCTIONNAME_MAP(input[i], const1);\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" TYPE tempMap;\n"" tempMap = FUNCTIONNAME_MAP(input[i], const1);\n"" result = FUNCTIONNAME_REDUCE(result, tempMap, (TYPE)0);\n"" i += gridSize;\n"" }\n"" sdata[tid] = result;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(blockSize >= 512) { if (tid < 256 && tid + 256 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 256], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 256) { if (tid < 128 && tid + 128 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 128], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 128) { if (tid < 64 && tid + 64 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 64], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 64) { if (tid < 32 && tid + 32 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 32], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 32) { if (tid < 16 && tid + 16 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 16], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 16) { if (tid < 8 && tid + 8 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 8], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 8) { if (tid < 4 && tid + 4 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 4], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 4) { if (tid < 2 && tid + 2 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 2], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(blockSize >= 2) { if (tid < 1 && tid + 1 < n) { sdata[tid] = FUNCTIONNAME_REDUCE(sdata[tid], sdata[tid + 1], (TYPE)0); } barrier(CLK_LOCAL_MEM_FENCE); }\n"" if(tid == 0)\n"" {\n"" output[get_group_id(0)] = sdata[tid];\n"" }\n""}\n"  )
static

OpenCL MapReduce kernel, using the same pattern as reduce6 in the CUDA SDK. See whitepaper from NVIDIA on optimizing reduction for the GPU. For unary map user functions.

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