1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178
|
/*
* -----------------------------------------------------------------
* Programmer(s): Slaven Peles @ LLNL
* -----------------------------------------------------------------
* LLNS Copyright Start
* Copyright (c) 2014, Lawrence Livermore National Security
* This work was performed under the auspices of the U.S. Department
* of Energy by Lawrence Livermore National Laboratory in part under
* Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344.
* Produced at the Lawrence Livermore National Laboratory.
* All rights reserved.
* For details, see the LICENSE file.
* LLNS Copyright End
* -----------------------------------------------------------------
*/
#ifndef _THREAD_PARTITIONING_HPP_
#define _THREAD_PARTITIONING_HPP_
#include <iostream>
#include <cuda_runtime.h>
namespace suncudavec
{
template<class T, class I>
class StreamPartitioning
{
public:
StreamPartitioning(I N, unsigned block)
: block_(block),
grid_((N + block - 1) / block)
{
}
explicit StreamPartitioning(StreamPartitioning<T, I>& p)
: block_(p.block_),
grid_(p.grid_)
{
}
unsigned grid() const
{
return grid_;
}
unsigned block() const
{
return block_;
}
private:
unsigned block_;
unsigned grid_;
};
template<class T, class I=int>
class ReducePartitioning
{
public:
ReducePartitioning(I N, unsigned block)
: block_(block),
grid_((N + (block_ * 2 - 1)) / (block_ * 2)),
shMemSize_(block_*sizeof(T))
{
allocateBuffer();
}
explicit ReducePartitioning(StreamPartitioning<T, I>& p)
: block_(p.block_),
grid_(p.grid_),
shMemSize_(p.shMemSize_)
{
allocateBuffer();
}
~ReducePartitioning()
{
cudaError_t err;
if (bufferSize_ > 0)
free(h_buffer_);
if (bufferSize_ > 0)
{
err = cudaFree(d_buffer_);
if(err != cudaSuccess)
std::cerr << "Failed to free device vector (error code " << err << ")!\n";
}
}
int setPartitioning(I N, unsigned& grid, unsigned& block, unsigned& shMemSize)
{
block = block_;
grid = (N + (block * 2 - 1)) / (block * 2);
shMemSize = block * sizeof(T);
return 0;
}
unsigned grid() const
{
return grid_;
}
unsigned block() const
{
return block_;
}
unsigned shmem() const
{
return shMemSize_;
}
unsigned int buffSize()
{
return bufferSize_;
}
T* devBuffer()
{
return d_buffer_;
}
const T* devBuffer() const
{
return d_buffer_;
}
T* hostBuffer()
{
return h_buffer_;
}
const T* hostBuffer() const
{
return h_buffer_;
}
void copyFromDevBuffer(unsigned int n) const
{
cudaError_t err = cudaMemcpy(h_buffer_, d_buffer_, n*sizeof(T), cudaMemcpyDeviceToHost);
if(err != cudaSuccess)
std::cerr << "Failed to copy vector from device to host (error code " << err << ")!\n";
}
private:
int allocateBuffer()
{
bufferSize_ = grid_ * sizeof(T);
h_buffer_ = static_cast<T*>(malloc(bufferSize_));
if(h_buffer_ == NULL)
std::cerr << "Failed to allocate host vector!\n";
cudaError_t err;
err = cudaMalloc((void**) &d_buffer_, bufferSize_);
if(err != cudaSuccess)
std::cerr << "Failed to allocate device vector (error code " << err << ")!\n";
return 0;
}
private:
unsigned block_;
unsigned grid_;
unsigned shMemSize_;
T* d_buffer_;
T* h_buffer_;
unsigned bufferSize_;
};
} // namespace suncudavec
#endif // _THREAD_PARTITIONING_HPP_
|