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