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.
*/
/*! \file reduce.inl
* \brief Inline file for reduce.h
*/
#include <thrust/detail/config.h>
#include <thrust/distance.h>
#include <thrust/iterator/iterator_traits.h>
#include <thrust/detail/minmax.h>
#include <thrust/detail/temporary_array.h>
#include <thrust/system/detail/generic/select_system.h>
#include <thrust/system/cuda/detail/runtime_introspection.h>
#include <thrust/system/cuda/detail/extern_shared_ptr.h>
#include <thrust/system/cuda/detail/block/reduce.h>
#include <thrust/system/cuda/detail/detail/launch_closure.h>
#include <thrust/system/cuda/detail/detail/launch_calculator.h>
#include <thrust/system/cuda/detail/execution_policy.h>
namespace thrust
{
namespace system
{
namespace cuda
{
namespace detail
{
namespace reduce_detail
{
/*
* Reduce a vector of n elements using binary_op()
*
* The order of reduction is not defined, so binary_op() should
* be a commutative (and associative) operator such as
* (integer) addition. Since floating point operations
* do not completely satisfy these criteria, the result is
* generally not the same as a consecutive reduction of
* the elements.
*
* Uses the same pattern as reduce6() in the CUDA SDK
*
*/
template <typename InputIterator,
typename Size,
typename T,
typename OutputIterator,
typename BinaryFunction,
typename Context>
struct unordered_reduce_closure
{
InputIterator input;
Size n;
T init;
OutputIterator output;
BinaryFunction binary_op;
unsigned int shared_array_size;
typedef Context context_type;
context_type context;
unordered_reduce_closure(InputIterator input, Size n, T init, OutputIterator output, BinaryFunction binary_op, unsigned int shared_array_size, Context context = Context())
: input(input), n(n), init(init), output(output), binary_op(binary_op), shared_array_size(shared_array_size), context(context) {}
__device__ __thrust_forceinline__
void operator()(void)
{
typedef typename thrust::iterator_value<OutputIterator>::type OutputType;
extern_shared_ptr<OutputType> shared_array;
Size grid_size = context.block_dimension() * context.grid_dimension();
Size i = context.linear_index();
input += i;
// compute reduction with all blockDim.x threads
OutputType sum = thrust::raw_reference_cast(*input);
i += grid_size;
input += grid_size;
while (i < n)
{
OutputType val = thrust::raw_reference_cast(*input);
sum = binary_op(sum, val);
i += grid_size;
input += grid_size;
}
// write first shared_array_size values into shared memory
if (context.thread_index() < shared_array_size)
shared_array[context.thread_index()] = sum;
// accumulate remaining values (if any) to shared memory in stages
if (context.block_dimension() > shared_array_size)
{
unsigned int lb = shared_array_size;
unsigned int ub = shared_array_size + lb;
while (lb < context.block_dimension())
{
context.barrier();
if (lb <= context.thread_index() && context.thread_index() < ub)
{
OutputType tmp = shared_array[context.thread_index() - lb];
shared_array[context.thread_index() - lb] = binary_op(tmp, sum);
}
lb += shared_array_size;
ub += shared_array_size;
}
}
context.barrier();
block::reduce_n(context, shared_array, thrust::min<unsigned int>(context.block_dimension(), shared_array_size), binary_op);
if (context.thread_index() == 0)
{
OutputType tmp = shared_array[0];
if (context.grid_dimension() == 1)
tmp = binary_op(init, tmp);
output += context.block_index();
*output = tmp;
}
}
};
__THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_BEGIN
template<typename DerivedPolicy,
typename InputIterator,
typename OutputType,
typename BinaryFunction>
OutputType reduce(execution_policy<DerivedPolicy> &exec,
InputIterator first,
InputIterator last,
OutputType init,
BinaryFunction binary_op)
{
// we're attempting to launch a kernel, assert we're compiling with nvcc
// ========================================================================
// X Note to the user: If you've found this line due to a compiler error, X
// X you need to compile your code using nvcc, rather than g++ or cl.exe X
// ========================================================================
THRUST_STATIC_ASSERT( (thrust::detail::depend_on_instantiation<InputIterator, THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC>::value) );
typedef typename thrust::iterator_difference<InputIterator>::type difference_type;
difference_type n = thrust::distance(first,last);
if (n == 0)
return init;
typedef thrust::detail::temporary_array<OutputType, DerivedPolicy> OutputArray;
typedef typename OutputArray::iterator OutputIterator;
typedef detail::blocked_thread_array Context;
typedef unordered_reduce_closure<InputIterator,difference_type,OutputType,OutputIterator,BinaryFunction,Context> Closure;
function_attributes_t attributes = detail::closure_attributes<Closure>();
// TODO chose this in a more principled manner
size_t threshold = thrust::max<size_t>(2 * attributes.maxThreadsPerBlock, 1024);
device_properties_t properties = device_properties();
// launch configuration
size_t num_blocks;
size_t block_size;
size_t array_size;
size_t smem_bytes;
// first level reduction
if (static_cast<size_t>(n) < threshold)
{
num_blocks = 1;
block_size = thrust::min(static_cast<size_t>(n), static_cast<size_t>(attributes.maxThreadsPerBlock));
array_size = thrust::min(block_size, (properties.sharedMemPerBlock - attributes.sharedSizeBytes) / sizeof(OutputType));
smem_bytes = sizeof(OutputType) * array_size;
}
else
{
detail::launch_calculator<Closure> calculator;
thrust::tuple<size_t,size_t,size_t> config = calculator.with_variable_block_size_available_smem();
num_blocks = thrust::min(thrust::get<0>(config), static_cast<size_t>(n) / thrust::get<1>(config));
block_size = thrust::get<1>(config);
array_size = thrust::min(block_size, thrust::get<2>(config) / sizeof(OutputType));
smem_bytes = sizeof(OutputType) * array_size;
}
// TODO assert(n <= num_blocks * block_size);
// TODO if (shared_array_size < 1) throw cuda exception "insufficient shared memory"
OutputArray output(exec, num_blocks);
Closure closure(first, n, init, output.begin(), binary_op, array_size);
//std::cout << "Launching " << num_blocks << " blocks of kernel with " << block_size << " threads and " << smem_bytes << " shared memory per block " << std::endl;
detail::launch_closure(closure, num_blocks, block_size, smem_bytes);
// second level reduction
if (num_blocks > 1)
{
typedef detail::blocked_thread_array Context;
typedef unordered_reduce_closure<OutputIterator,difference_type,OutputType,OutputIterator,BinaryFunction,Context> Closure;
function_attributes_t attributes = detail::closure_attributes<Closure>();
num_blocks = 1;
block_size = thrust::min(output.size(), static_cast<size_t>(attributes.maxThreadsPerBlock));
array_size = thrust::min(block_size, (properties.sharedMemPerBlock - attributes.sharedSizeBytes) / sizeof(OutputType));
smem_bytes = sizeof(OutputType) * array_size;
// TODO if (shared_array_size < 1) throw cuda exception "insufficient shared memory"
Closure closure(output.begin(), output.size(), init, output.begin(), binary_op, array_size);
//std::cout << "Launching " << num_blocks << " blocks of kernel with " << block_size << " threads and " << smem_bytes << " shared memory per block " << std::endl;
detail::launch_closure(closure, num_blocks, block_size, smem_bytes);
}
return output[0];
} // end reduce
} // end reduce_detail
__THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_END
template<typename DerivedPolicy,
typename InputIterator,
typename OutputType,
typename BinaryFunction>
OutputType reduce(execution_policy<DerivedPolicy> &exec,
InputIterator first,
InputIterator last,
OutputType init,
BinaryFunction binary_op)
{
return reduce_detail::reduce(exec, first, last, init, binary_op);
} // end reduce()
} // end namespace detail
} // end namespace cuda
} // end namespace system
} // end namespace thrust