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SkePU 0.7
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SkePU 0.7
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The main nemaspace for SkePU library. More...
Classes | |
| class | Generate |
| A class representing the Generate skeleton. More... | |
| class | Map |
| A class representing the Map skeleton. More... | |
| class | MapArray |
| A class representing the MapArray skeleton. More... | |
| class | MapOverlap |
| A class representing the MapOverlap skeleton. More... | |
| class | MapReduce |
| A class representing the MapReduce skeleton. More... | |
| class | Matrix |
| A matrix container class (2D matrix), internally uses 1D container (std::vector). More... | |
| class | Reduce |
| A class representing the Reduce skeleton. More... | |
| class | Scan |
| A class representing the Scan skeleton. More... | |
| class | DataCollector2D |
| A class that can be used to collect 2D data. More... | |
| class | Device_CL |
| A class representing an OpenCL device. More... | |
| class | Device_CU |
| A class representing a CUDA device. More... | |
| class | DeviceMemPointer_CL |
| A class representing an OpenCL device memory allocation. More... | |
| class | DeviceMemPointer_CU |
| A class representing a CUDA device memory allocation. More... | |
| class | DeviceMemPointer_Matrix_CL |
| A class representing an OpenCL device memory allocation for Matrix. More... | |
| struct | openclGenProp |
| struct | openclDeviceProp |
| class | Environment |
| A class representing a execution environment. More... | |
| class | ExecPlan |
| A class that describes an execution plan, not used very much in this case as decision is mostly left to StarPU. More... | |
| class | Threads |
| class | ThreadPool |
| to enable thread pooling while using multiple CUDA devices. ThreadPool class manages all the ThreadPool related activities. This includes keeping track of idle threads and ynchronizations between all threads. More... | |
| class | TimerLinux_GTOD |
| A class that can be used measure time on Linux systems. More... | |
| class | Task |
| A class representing a Task for the farm skeleton. More... | |
| class | Vector |
| A vector container class, implemented as a wrapper for std::vector. It is configured to use StarPU DSM as its backend, which means that it does not do lazy memory copying in this translation. More... | |
Enumerations | |
| enum | OverlapPolicy |
| enum | EdgePolicy |
| enum | AccessType |
| Can be used to specify whether the access is row-wise or column-wise. More... | |
| enum | ScanType |
Functions | |
| void | join_farm_all () |
| void | join_farm () |
| template<typename Function1 , typename Function2 > | |
| void | farm (Function1 *f1, Function2 *f2) |
| static std::string | GenerateKernel_CL ("__kernel void GenerateKernel_KERNELNAME(__global TYPE* output, unsigned int numElements, TYPE const1, unsigned int indexOffset)\n""{\n"" unsigned int i = get_global_id(0);\n"" unsigned int gridSize = get_local_size(0)*get_num_groups(0);\n"" while(i < numElements)\n"" {\n"" output[i] = FUNCTIONNAME(i+indexOffset, const1);\n"" i += gridSize;\n"" }\n""}\n") |
| template<typename T , typename GenerateFunc > | |
| __global__ void | GenerateKernel_CU (GenerateFunc generateFunc, T *output, unsigned int numElements, unsigned int indexOffset) |
| static std::string | UnaryMapKernel_CL ("__kernel void UnaryMapKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, unsigned int numElements, TYPE const1)\n""{\n"" int i = get_global_id(0);\n"" unsigned int gridSize = get_local_size(0)*get_num_groups(0);\n"" while(i < numElements)\n"" {\n"" output[i] = FUNCTIONNAME(input[i], const1);\n"" i += gridSize;\n"" }\n""}\n") |
| static std::string | BinaryMapKernel_CL ("__kernel void BinaryMapKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* output, unsigned int n, TYPE const1)\n""{\n"" int i = get_global_id(0);\n"" unsigned int gridSize = get_local_size(0)*get_num_groups(0);\n"" while(i < n)\n"" {\n"" output[i] = FUNCTIONNAME(input1[i], input2[i], const1);\n"" i += gridSize;\n"" }\n""}\n") |
| static std::string | TrinaryMapKernel_CL ("__kernel void TrinaryMapKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* input3, __global TYPE* output, unsigned int n, TYPE const1)\n""{\n"" int i = get_global_id(0);\n"" unsigned int gridSize = get_local_size(0)*get_num_groups(0);\n"" while(i < n)\n"" {\n"" output[i] = FUNCTIONNAME(input1[i], input2[i], input3[i], const1);\n"" i += gridSize;\n"" }\n""}\n") |
| template<typename T , typename UnaryFunc > | |
| __global__ void | MapKernelUnary_CU (UnaryFunc mapFunc, T *input, T *output, unsigned int n, T const1) |
| template<typename T , typename BinaryFunc > | |
| __global__ void | MapKernelBinary_CU (BinaryFunc mapFunc, T *input1, T *input2, T *output, unsigned int n, T const1) |
| template<typename T , typename TrinaryFunc > | |
| __global__ void | MapKernelTrinary_CU (TrinaryFunc mapFunc, T *input1, T *input2, T *input3, T *output, unsigned int n, T const1) |
| static std::string | MapArrayKernel_CL ("__kernel void MapArrayKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* output, unsigned int n)\n""{\n"" int i = get_global_id(0);\n"" unsigned int gridSize = get_local_size(0)*get_num_groups(0);\n"" while(i < n)\n"" {\n"" output[i] = FUNCTIONNAME(&input1[0], input2[i]);\n"" i += gridSize;\n"" }\n""}\n") |
| template<typename T , typename ArrayFunc > | |
| __global__ void | MapArrayKernel_CU (ArrayFunc mapArrayFunc, T *input1, T *input2, T *output, unsigned int n) |
| static std::string | MapOverlapKernel_CL ("__kernel void MapOverlapKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, __global TYPE* wrap, int n, int overlap, int out_offset, int out_numelements, int poly, TYPE pad, __local TYPE* sdata)\n""{\n"" int tid = get_local_id(0);\n"" int i = get_group_id(0) * get_local_size(0) + get_local_id(0);\n"" if(poly == 0)\n"" {\n"" sdata[overlap+tid] = (i < n) ? input[i] : pad;\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (get_group_id(0) == 0) ? pad : input[i-overlap];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != get_num_groups(0)-1 && i+overlap < n) ? input[i+overlap] : pad;\n"" }\n"" }\n"" else if(poly == 1)\n"" {\n"" if(i < n)\n"" {\n"" sdata[overlap+tid] = input[i];\n"" }\n"" else if(i-n < overlap)\n"" {\n"" sdata[overlap+tid] = wrap[overlap+(i-n)];\n"" }\n"" else\n"" {\n"" sdata[overlap+tid] = pad;\n"" }\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (get_group_id(0) == 0) ? wrap[tid] : input[i-overlap];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != get_num_groups(0)-1 && i+overlap < n) ? input[i+overlap] : wrap[overlap+(i+overlap-n)];\n"" }\n"" }\n"" else if(poly == 2)\n"" {\n"" sdata[overlap+tid] = (i < n) ? input[i] : input[n-1];\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (get_group_id(0) == 0) ? input[0] : input[i-overlap];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != get_num_groups(0)-1 && i+overlap < n) ? input[i+overlap] : input[n-1];\n"" }\n"" }\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if( (i >= out_offset) && (i < out_offset+out_numelements) )\n"" {\n"" output[i-out_offset] = FUNCTIONNAME(&(sdata[tid+overlap]),1);\n"" }\n""}\n") |
| static std::string | MapOverlapKernel_CL_Matrix_Row ("__kernel void MapOverlapKernel_MatRowWise_KERNELNAME(__global TYPE* input, __global TYPE* output, __global TYPE* wrap, int n, int overlap, int out_offset, int out_numelements, int poly, TYPE pad, int blocksPerRow, int rowWidth, __local TYPE* sdata)\n""{\n"" int tid = get_local_id(0);\n"" int i = get_group_id(0) * get_local_size(0) + get_local_id(0);\n"" int wrapIndex= 2 * overlap * (int)(get_group_id(0)/blocksPerRow);\n"" int tmp= (get_group_id(0) % blocksPerRow);\n"" int tmp2= (get_group_id(0) / blocksPerRow);\n"" if(poly == 0)\n"" {\n"" sdata[overlap+tid] = (i < n) ? input[i] : pad;\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (tmp==0) ? pad : input[i-overlap];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != (get_num_groups(0)-1) && (i+overlap < n) && tmp!=(blocksPerRow-1)) ? input[i+overlap] : pad;\n"" }\n"" }\n"" else if(poly == 1)\n"" {\n"" if(i < n)\n"" {\n"" sdata[overlap+tid] = input[i];\n"" }\n"" else if(i-n < overlap)\n"" {\n"" sdata[overlap+tid] = wrap[(overlap+(i-n))+ wrapIndex];\n"" }\n"" else\n"" {\n"" sdata[overlap+tid] = pad;\n"" }\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (tmp==0) ? wrap[tid+wrapIndex] : input[i-overlap];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != (get_num_groups(0)-1) && i+overlap < n && tmp!=(blocksPerRow-1)) ? input[i+overlap] : wrap[overlap+wrapIndex+(tid+overlap-get_local_size(0))];\n"" }\n"" }\n"" else if(poly == 2)\n"" {\n"" sdata[overlap+tid] = (i < n) ? input[i] : input[n-1];\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (tmp==0) ? input[tmp2*rowWidth] : input[i-overlap];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != (get_num_groups(0)-1) && (i+overlap < n) && (tmp!=(blocksPerRow-1))) ? input[i+overlap] : input[(tmp2+1)*rowWidth-1];\n"" }\n"" }\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if( (i >= out_offset) && (i < out_offset+out_numelements) )\n"" {\n"" output[i-out_offset] = FUNCTIONNAME(&(sdata[tid+overlap]),1);\n"" }\n""}\n") |
| static std::string | MapOverlapKernel_CL_Matrix_Col ("__kernel void MapOverlapKernel_MatColWise_KERNELNAME(__global TYPE* input, __global TYPE* output, __global TYPE* wrap, int n, int overlap, int out_offset, int out_numelements, int poly, TYPE pad, int blocksPerCol, int rowWidth, int colWidth, __local TYPE* sdata)\n""{\n"" int tid = get_local_id(0);\n"" int i = get_group_id(0) * get_local_size(0) + get_local_id(0);\n"" int wrapIndex= 2 * overlap * (int)(get_group_id(0)/blocksPerCol);\n"" int tmp= (get_group_id(0) % blocksPerCol);\n"" int tmp2= (get_group_id(0) / blocksPerCol);\n"" int arrInd = (tid + tmp*get_local_size(0))*rowWidth + tmp2;\n"" if(poly == 0)\n"" {\n"" sdata[overlap+tid] = (i < n) ? input[arrInd] : pad;\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (tmp==0) ? pad : input[(arrInd-(overlap*rowWidth))];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != (get_num_groups(0)-1) && (arrInd+(overlap*rowWidth)) < n && (tmp!=(blocksPerCol-1))) ? input[(arrInd+(overlap*rowWidth))] : pad;\n"" }\n"" }\n"" else if(poly == 1)\n"" {\n"" if(i < n)\n"" {\n"" sdata[overlap+tid] = input[arrInd];\n"" }\n"" else if(i-n < overlap)\n"" {\n"" sdata[overlap+tid] = wrap[(overlap+(i-n))+ wrapIndex];\n"" }\n"" else\n"" {\n"" sdata[overlap+tid] = pad;\n"" }\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (tmp==0) ? wrap[tid+wrapIndex] : input[(arrInd-(overlap*rowWidth))];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != (get_num_groups(0)-1) && (arrInd+(overlap*rowWidth)) < n && (tmp!=(blocksPerCol-1))) ? input[(arrInd+(overlap*rowWidth))] : wrap[overlap+wrapIndex+(tid+overlap-get_local_size(0))];\n"" }\n"" }\n"" else if(poly == 2)\n"" {\n"" sdata[overlap+tid] = (i < n) ? input[arrInd] : input[n-1];\n"" if(tid < overlap)\n"" {\n"" sdata[tid] = (tmp==0) ? input[tmp2] : input[(arrInd-(overlap*rowWidth))];\n"" }\n"" if(tid >= (get_local_size(0)-overlap))\n"" {\n"" sdata[tid+2*overlap] = (get_group_id(0) != (get_num_groups(0)-1) && (arrInd+(overlap*rowWidth)) < n && (tmp!=(blocksPerCol-1))) ? input[(arrInd+(overlap*rowWidth))] : input[tmp2+(colWidth-1)*rowWidth];\n"" }\n"" }\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if( (i >= out_offset) && (i < out_offset+out_numelements) )\n"" {\n"" output[arrInd-out_offset] = FUNCTIONNAME(&(sdata[tid+overlap]), 1);\n"" }\n""}\n") |
| template<int poly, typename T , typename OverlapFunc > | |
| __global__ void | MapOverlapKernel_CU (OverlapFunc mapOverlapFunc, T *input, T *output, T *wrap, unsigned int n, unsigned int out_offset, unsigned int out_numelements, T pad) |
| template<int poly, typename T , typename OverlapFunc > | |
| __global__ void | MapOverlapKernel_CU_Matrix_Row (OverlapFunc mapOverlapFunc, T *input, T *output, T *wrap, unsigned int n, unsigned int out_offset, unsigned int out_numelements, T pad, unsigned int blocksPerRow, unsigned int rowWidth) |
| template<int poly, typename T , typename OverlapFunc > | |
| __global__ void | MapOverlapKernel_CU_Matrix_Col (OverlapFunc mapOverlapFunc, T *input, T *output, T *wrap, unsigned int n, unsigned int out_offset, unsigned int out_numelements, T pad, unsigned int blocksPerCol, unsigned int rowWidth, unsigned int colWidth) |
| static std::string | UnaryMapReduceKernel_CL ("__kernel void UnaryMapReduceKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, unsigned int n, __local TYPE* sdata, TYPE const1)\n""{\n"" unsigned int blockSize = get_local_size(0);\n"" unsigned int tid = get_local_id(0);\n"" unsigned int i = get_group_id(0)*blockSize + get_local_id(0);\n"" unsigned int 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 | BinaryMapReduceKernel_CL ("__kernel void BinaryMapReduceKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* output, unsigned int n, __local TYPE* sdata, TYPE const1)\n""{\n"" unsigned int blockSize = get_local_size(0);\n"" unsigned int tid = get_local_id(0);\n"" unsigned int i = get_group_id(0)*blockSize + get_local_id(0);\n"" unsigned int 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 | TrinaryMapReduceKernel_CL ("__kernel void TrinaryMapReduceKernel_KERNELNAME(__global TYPE* input1, __global TYPE* input2, __global TYPE* input3, __global TYPE* output, unsigned int n, __local TYPE* sdata, TYPE const1)\n""{\n"" unsigned int blockSize = get_local_size(0);\n"" unsigned int tid = get_local_id(0);\n"" unsigned int i = get_group_id(0)*blockSize + get_local_id(0);\n"" unsigned int 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 | MapReduceKernel1_CU (UnaryFunc mapFunc, BinaryFunc reduceFunc, T *input, T *output, unsigned int n) |
| template<typename T , typename BinaryFunc1 , typename BinaryFunc2 > | |
| __global__ void | MapReduceKernel2_CU (BinaryFunc1 mapFunc, BinaryFunc2 reduceFunc, T *input1, T *input2, T *output, unsigned int n) |
| template<typename T , typename TrinaryFunc , typename BinaryFunc > | |
| __global__ void | MapReduceKernel3_CU (TrinaryFunc mapFunc, BinaryFunc reduceFunc, T *input1, T *input2, T *input3, T *output, unsigned int n) |
| static std::string | ReduceKernel_CL ("__kernel void ReduceKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, unsigned int n, __local TYPE* sdata)\n""{\n"" unsigned int blockSize = get_local_size(0);\n"" unsigned int tid = get_local_id(0);\n"" unsigned int i = get_group_id(0)*blockSize + get_local_id(0);\n"" unsigned int gridSize = blockSize*get_num_groups(0);\n"" TYPE result = 0;\n"" if(i < n)\n"" {\n"" result = input[i];\n"" i += gridSize;\n"" }\n"" while(i < n)\n"" {\n"" result = FUNCTIONNAME(result, input[i], (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(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(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(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(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(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(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(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(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(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 BinaryFunc > | |
| __global__ void | ReduceKernel_CU (BinaryFunc reduceFunc, T *input, T *output, unsigned int n) |
| static std::string | ScanKernel_CL ("__kernel void ScanKernel_KERNELNAME(__global TYPE* input, __global TYPE* output, __global TYPE* blockSums, unsigned int n, unsigned int numElements, __local TYPE* sdata)\n""{\n"" unsigned int threadIdx = get_local_id(0);\n"" unsigned int blockDim = get_local_size(0);\n"" unsigned int blockIdx = get_group_id(0);\n"" unsigned int gridDim = get_num_groups(0);\n"" int thid = threadIdx;\n"" int pout = 0;\n"" int pin = 1;\n"" int mem = get_global_id(0);\n"" int blockNr = blockIdx;\n"" unsigned int gridSize = blockDim*gridDim;\n"" unsigned int numBlocks = numElements/(blockDim) + (numElements%(blockDim) == 0 ? 0:1);\n"" int offset;\n"" while(blockNr < numBlocks)\n"" {\n"" sdata[pout*n+thid] = (mem < numElements) ? input[mem] : 0;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" for(offset = 1; offset < n; offset *=2)\n"" {\n"" pout = 1-pout;\n"" pin = 1-pout;\n"" if(thid >= offset)\n"" sdata[pout*n+thid] = FUNCTIONNAME(sdata[pin*n+thid], sdata[pin*n+thid-offset], (TYPE)0);\n"" else\n"" sdata[pout*n+thid] = sdata[pin*n+thid];\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" }\n"" if(thid == blockDim - 1)\n"" blockSums[blockNr] = sdata[pout*n+blockDim-1];\n"" if(mem < numElements)\n"" output[mem] = sdata[pout*n+thid];\n"" mem += gridSize;\n"" blockNr += gridDim;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" }\n""}\n") |
| static std::string | ScanUpdate_CL ("__kernel void ScanUpdate_KERNELNAME(__global TYPE* data, __global TYPE* sums, int isInclusive, TYPE init, int n, __global TYPE* ret, __local TYPE* sdata)\n""{\n"" __local TYPE offset;\n"" __local TYPE inc_offset;\n"" unsigned int threadIdx = get_local_id(0);\n"" unsigned int blockDim = get_local_size(0);\n"" unsigned int blockIdx = get_group_id(0);\n"" unsigned int gridDim = get_num_groups(0);\n"" int thid = threadIdx;\n"" int blockNr = blockIdx;\n"" unsigned int gridSize = blockDim*gridDim;\n"" int mem = get_global_id(0);\n"" unsigned int numBlocks = n/(blockDim) + (n%(blockDim) == 0 ? 0:1);\n"" while(blockNr < numBlocks)\n"" {\n"" if(thid == 0)\n"" {\n"" if(isInclusive == 0)\n"" {\n"" offset = init;\n"" if(blockNr > 0)\n"" {\n"" offset = FUNCTIONNAME(offset, sums[blockNr-1], (TYPE)0);\n"" inc_offset = sums[blockNr-1];\n"" }\n"" }\n"" else\n"" {\n"" if(blockNr > 0)\n"" offset = sums[blockNr-1];\n"" }\n"" }\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(isInclusive == 1)\n"" {\n"" if(blockNr > 0)\n"" sdata[thid] = (mem < n) ? FUNCTIONNAME(offset, data[mem], (TYPE)0) : 0;\n"" else\n"" sdata[thid] = (mem < n) ? data[mem] : 0;\n"" if(mem == n-1)\n"" *ret = sdata[thid];\n"" }\n"" else\n"" {\n"" if(mem == n-1)\n"" *ret = FUNCTIONNAME(inc_offset, data[mem], (TYPE)0);\n"" if(thid == 0)\n"" sdata[thid] = offset;\n"" else\n"" sdata[thid] = (mem-1 < n) ? FUNCTIONNAME(offset, data[mem-1], (TYPE)0) : 0;\n"" }\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" if(mem < n)\n"" data[mem] = sdata[thid];\n"" mem += gridSize;\n"" blockNr += gridDim;\n"" barrier(CLK_LOCAL_MEM_FENCE);\n"" }\n""}\n") |
| static std::string | ScanAdd_CL ("__kernel void ScanAdd_KERNELNAME(__global TYPE* data, TYPE sum, int n)\n""{\n"" int i = get_global_id(0);\n"" unsigned int gridSize = get_local_size(0)*get_num_groups(0);\n"" while(i < n)\n"" {\n"" data[i] = FUNCTIONNAME(data[i], sum, (TYPE)0);\n"" i += gridSize;\n"" }\n""}\n") |
| template<typename T , typename BinaryFunc > | |
| __global__ void | ScanKernel_CU (BinaryFunc scanFunc, T *input, T *output, T *blockSums, unsigned int n, unsigned int numElements) |
| template<typename T , typename BinaryFunc > | |
| __global__ void | ScanUpdate_CU (BinaryFunc scanFunc, T *data, T *sums, int isInclusive, T init, int n, T *ret) |
| template<typename T , typename BinaryFunc > | |
| __global__ void | ScanAdd_CU (BinaryFunc scanFunc, T *data, T sum, int n) |
The main nemaspace for SkePU library.
All classes and functions in the SkePU library are in this namespace.
| enum skepu::AccessType |
Can be used to specify whether the access is row-wise or column-wise.
Used in some cases to mention type of access required in a certain operation.
| enum skepu::EdgePolicy |
Enumeration of the different edge policies (what happens when a read outside the vector is performed) that the map overlap skeletons support.
| enum skepu::OverlapPolicy |
Enumeration of the different overlap patterns for Mapoverlap for Matrix container.
| enum skepu::ScanType |
Enumeration of the two types of Scan that can be performed: Inclusive and Exclusive.
| void skepu::farm | ( | Function1 * | f1, |
| Function2 * | f2 | ||
| ) |
binary compose, run functions asynchronously
| void skepu::join_farm | ( | ) |
if you set useTagID to true before calling farm, then tasks will not be automaitcally joined at then end of farm. In this case, you need to explicitly call this method which will just ensure completion of spawned tasks by all previous farm calls.
| void skepu::join_farm_all | ( | ) |
if you set useTagID to true before calling farm, then tasks will not be automaitcally joined at then end of farm. In this case, you need to explicitly call this method which will just ensure completion of all previpously created tasks.
1.7.4
