GOSTCoin CUDA miner project, compatible with most nvidia cards, containing only gostd algo
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/*
* Copyright 2008-2012 NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <cstddef>
#include <thrust/detail/config.h>
namespace thrust
{
namespace system
{
namespace cuda
{
namespace detail
{
// XXX define our own device_properties_t to avoid errors when #including
// this file in the absence of a CUDA installation
struct device_properties_t
{
// mirror the type and spelling of cudaDeviceProp's members
// keep these alphabetized
int major;
int maxGridSize[3];
int maxThreadsPerBlock;
int maxThreadsPerMultiProcessor;
int minor;
int multiProcessorCount;
int regsPerBlock;
size_t sharedMemPerBlock;
int warpSize;
};
// XXX define our own device_properties_t to avoid errors when #including
// this file in the absence of a CUDA installation
struct function_attributes_t
{
// mirror the type and spelling of cudaFuncAttributes' members
// keep these alphabetized
size_t constSizeBytes;
size_t localSizeBytes;
int maxThreadsPerBlock;
int numRegs;
size_t sharedSizeBytes;
};
/*! Computes a block size in number of threads for a CUDA kernel using a occupancy-promoting heuristic.
* \param attributes The cudaFuncAttributes corresponding to a __global__ function of interest on a GPU of interest.
* \param properties The cudaDeviceProp corresponding to a GPU on which to launch the __global__ function of interest.
* \return A CUDA block size, in number of threads, which the resources of the GPU's streaming multiprocessor can
* accomodate and which is intended to promote occupancy. The result is equivalent to the one performed by
* the "CUDA Occupancy Calculator".
* \note The __global__ function of interest is presumed to use 0 bytes of dynamically-allocated __shared__ memory.
*/
inline __host__ __device__
std::size_t block_size_with_maximum_potential_occupancy(const function_attributes_t &attributes,
const device_properties_t &properties);
/*! Computes a block size in number of threads for a CUDA kernel using a occupancy-promoting heuristic.
* Use this version of the function when a CUDA block's dynamically-allocated __shared__ memory requirements
* vary with the size of the block.
* \param attributes The cudaFuncAttributes corresponding to a __global__ function of interest on a GPU of interest.
* \param properties The cudaDeviceProp corresponding to a GPU on which to launch the __global__ function of interest.
* \param block_size_to_dynamic_smem_bytes A unary function which maps an integer CUDA block size to the number of bytes
* of dynamically-allocated __shared__ memory required by a CUDA block of that size.
* \return A CUDA block size, in number of threads, which the resources of the GPU's streaming multiprocessor can
* accomodate and which is intended to promote occupancy. The result is equivalent to the one performed by
* the "CUDA Occupancy Calculator".
*/
template<typename UnaryFunction>
inline __host__ __device__
std::size_t block_size_with_maximum_potential_occupancy(const function_attributes_t &attributes,
const device_properties_t &properties,
UnaryFunction block_size_to_dynamic_smem_size);
/*! Returns the maximum amount of dynamic shared memory each block
* can utilize without reducing thread occupancy.
*
* \param properties CUDA device properties
* \param attributes CUDA function attributes
* \param blocks_per_processor Number of blocks per streaming multiprocessor
*/
inline __host__ __device__
size_t proportional_smem_allocation(const device_properties_t &properties,
const function_attributes_t &attributes,
size_t blocks_per_processor);
template<typename UnaryFunction>
inline __host__ __device__
size_t max_blocksize_subject_to_smem_usage(const device_properties_t &properties,
const function_attributes_t &attributes,
UnaryFunction blocksize_to_dynamic_smem_usage);
namespace cuda_launch_config_detail
{
using std::size_t;
namespace util
{
template<typename T>
inline __host__ __device__
T min_(const T &lhs, const T &rhs)
{
return rhs < lhs ? rhs : lhs;
}
template <typename T>
struct zero_function
{
inline __host__ __device__
T operator()(T)
{
return 0;
}
};
// x/y rounding towards +infinity for integers, used to determine # of blocks/warps etc.
template<typename L, typename R>
inline __host__ __device__ L divide_ri(const L x, const R y)
{
return (x + (y - 1)) / y;
}
// x/y rounding towards zero for integers, used to determine # of blocks/warps etc.
template<typename L, typename R>
inline __host__ __device__ L divide_rz(const L x, const R y)
{
return x / y;
}
// round x towards infinity to the next multiple of y
template<typename L, typename R>
inline __host__ __device__ L round_i(const L x, const R y){ return y * divide_ri(x, y); }
// round x towards zero to the next multiple of y
template<typename L, typename R>
inline __host__ __device__ L round_z(const L x, const R y){ return y * divide_rz(x, y); }
} // end namespace util
// granularity of shared memory allocation
inline __host__ __device__
size_t smem_allocation_unit(const device_properties_t &properties)
{
switch(properties.major)
{
case 1: return 512;
case 2: return 128;
case 3: return 256;
default: return 256; // unknown GPU; have to guess
}
}
// granularity of register allocation
inline __host__ __device__
size_t reg_allocation_unit(const device_properties_t &properties, const size_t regsPerThread)
{
switch(properties.major)
{
case 1: return (properties.minor <= 1) ? 256 : 512;
case 2: switch(regsPerThread)
{
case 21:
case 22:
case 29:
case 30:
case 37:
case 38:
case 45:
case 46:
return 128;
default:
return 64;
}
case 3: return 256;
default: return 256; // unknown GPU; have to guess
}
}
// granularity of warp allocation
inline __host__ __device__
size_t warp_allocation_multiple(const device_properties_t &properties)
{
return (properties.major <= 1) ? 2 : 1;
}
// number of "sides" into which the multiprocessor is partitioned
inline __host__ __device__
size_t num_sides_per_multiprocessor(const device_properties_t &properties)
{
switch(properties.major)
{
case 1: return 1;
case 2: return 2;
case 3: return 4;
default: return 4; // unknown GPU; have to guess
}
}
inline __host__ __device__
size_t max_blocks_per_multiprocessor(const device_properties_t &properties)
{
return (properties.major <= 2) ? 8 : 16;
}
inline __host__ __device__
size_t max_active_blocks_per_multiprocessor(const device_properties_t &properties,
const function_attributes_t &attributes,
int CTA_SIZE,
size_t dynamic_smem_bytes)
{
// Determine the maximum number of CTAs that can be run simultaneously per SM
// This is equivalent to the calculation done in the CUDA Occupancy Calculator spreadsheet
//////////////////////////////////////////
// Limits due to threads/SM or blocks/SM
//////////////////////////////////////////
const size_t maxThreadsPerSM = properties.maxThreadsPerMultiProcessor; // 768, 1024, 1536, etc.
const size_t maxBlocksPerSM = max_blocks_per_multiprocessor(properties);
// Calc limits
const size_t ctaLimitThreads = (CTA_SIZE <= properties.maxThreadsPerBlock) ? maxThreadsPerSM / CTA_SIZE : 0;
const size_t ctaLimitBlocks = maxBlocksPerSM;
//////////////////////////////////////////
// Limits due to shared memory/SM
//////////////////////////////////////////
const size_t smemAllocationUnit = smem_allocation_unit(properties);
const size_t smemBytes = attributes.sharedSizeBytes + dynamic_smem_bytes;
const size_t smemPerCTA = util::round_i(smemBytes, smemAllocationUnit);
// Calc limit
const size_t ctaLimitSMem = smemPerCTA > 0 ? properties.sharedMemPerBlock / smemPerCTA : maxBlocksPerSM;
//////////////////////////////////////////
// Limits due to registers/SM
//////////////////////////////////////////
const size_t regAllocationUnit = reg_allocation_unit(properties, attributes.numRegs);
const size_t warpAllocationMultiple = warp_allocation_multiple(properties);
const size_t numWarps = util::round_i(util::divide_ri(CTA_SIZE, properties.warpSize), warpAllocationMultiple);
// Calc limit
size_t ctaLimitRegs;
if(properties.major <= 1)
{
// GPUs of compute capability 1.x allocate registers to CTAs
// Number of regs per block is regs per thread times number of warps times warp size, rounded up to allocation unit
const size_t regsPerCTA = util::round_i(attributes.numRegs * properties.warpSize * numWarps, regAllocationUnit);
ctaLimitRegs = regsPerCTA > 0 ? properties.regsPerBlock / regsPerCTA : maxBlocksPerSM;
}
else
{
// GPUs of compute capability 2.x and higher allocate registers to warps
// Number of regs per warp is regs per thread times times warp size, rounded up to allocation unit
const size_t regsPerWarp = util::round_i(attributes.numRegs * properties.warpSize, regAllocationUnit);
const size_t numSides = num_sides_per_multiprocessor(properties);
const size_t numRegsPerSide = properties.regsPerBlock / numSides;
ctaLimitRegs = regsPerWarp > 0 ? ((numRegsPerSide / regsPerWarp) * numSides) / numWarps : maxBlocksPerSM;
}
//////////////////////////////////////////
// Overall limit is min() of limits due to above reasons
//////////////////////////////////////////
return util::min_(ctaLimitRegs, util::min_(ctaLimitSMem, util::min_(ctaLimitThreads, ctaLimitBlocks)));
}
} // end namespace cuda_launch_config_detail
template<typename UnaryFunction>
inline __host__ __device__
std::size_t block_size_with_maximum_potential_occupancy(const function_attributes_t &attributes,
const device_properties_t &properties,
UnaryFunction block_size_to_dynamic_smem_size)
{
size_t max_occupancy = properties.maxThreadsPerMultiProcessor;
size_t largest_blocksize = cuda_launch_config_detail::util::min_(properties.maxThreadsPerBlock, attributes.maxThreadsPerBlock);
size_t granularity = properties.warpSize;
size_t max_blocksize = 0;
size_t highest_occupancy = 0;
for(size_t blocksize = largest_blocksize; blocksize != 0; blocksize -= granularity)
{
size_t occupancy = blocksize * cuda_launch_config_detail::max_active_blocks_per_multiprocessor(properties, attributes, blocksize, block_size_to_dynamic_smem_size(blocksize));
if(occupancy > highest_occupancy)
{
max_blocksize = blocksize;
highest_occupancy = occupancy;
}
// early out, can't do better
if(highest_occupancy == max_occupancy)
break;
}
return max_blocksize;
}
inline __host__ __device__
std::size_t block_size_with_maximum_potential_occupancy(const function_attributes_t &attributes,
const device_properties_t &properties)
{
return block_size_with_maximum_potential_occupancy(attributes, properties, cuda_launch_config_detail::util::zero_function<std::size_t>());
}
inline __host__ __device__
size_t proportional_smem_allocation(const device_properties_t &properties,
const function_attributes_t &attributes,
size_t blocks_per_processor)
{
size_t smem_per_processor = properties.sharedMemPerBlock;
size_t smem_allocation_unit = cuda_launch_config_detail::smem_allocation_unit(properties);
size_t total_smem_per_block = cuda_launch_config_detail::util::round_z(smem_per_processor / blocks_per_processor, smem_allocation_unit);
size_t static_smem_per_block = attributes.sharedSizeBytes;
return total_smem_per_block - static_smem_per_block;
}
template<typename UnaryFunction>
inline __host__ __device__
size_t max_blocksize_subject_to_smem_usage(const device_properties_t &properties,
const function_attributes_t &attributes,
UnaryFunction blocksize_to_dynamic_smem_usage)
{
size_t largest_blocksize = (thrust::min)(properties.maxThreadsPerBlock, attributes.maxThreadsPerBlock);
size_t granularity = properties.warpSize;
for(int blocksize = largest_blocksize; blocksize > 0; blocksize -= granularity)
{
size_t total_smem_usage = blocksize_to_dynamic_smem_usage(blocksize) + attributes.sharedSizeBytes;
if(total_smem_usage <= properties.sharedMemPerBlock)
{
return blocksize;
}
}
return 0;
}
} // end detail
} // end cuda
} // end system
} // end thrust