7f4761ae52
This allows each model to initialise their arrays with a parallel approach, which yields the first touch required for good performance on NUMA architectures.
58 lines
1.2 KiB
C++
58 lines
1.2 KiB
C++
// Copyright (c) 2015-16 Tom Deakin, Simon McIntosh-Smith,
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// University of Bristol HPC
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//
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// For full license terms please see the LICENSE file distributed with this
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// source code
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#pragma once
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#include <iostream>
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#include <stdexcept>
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#include "RAJA/RAJA.hxx"
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#include "Stream.h"
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#define IMPLEMENTATION_STRING "RAJA"
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#ifdef RAJA_TARGET_CPU
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typedef RAJA::IndexSet::ExecPolicy<
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RAJA::seq_segit,
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RAJA::omp_parallel_for_exec> policy;
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#else
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const size_t block_size = 128;
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typedef RAJA::IndexSet::ExecPolicy<
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RAJA::seq_segit,
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RAJA::cuda_exec<block_size>> policy;
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#endif
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template <class T>
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class RAJAStream : public Stream<T>
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{
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protected:
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// Size of arrays
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unsigned int array_size;
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// Contains iteration space
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RAJA::IndexSet index_set;
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// Device side pointers to arrays
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T* d_a;
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T* d_b;
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T* d_c;
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public:
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RAJAStream(const unsigned int, const int);
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~RAJAStream();
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virtual void copy() override;
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virtual void add() override;
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virtual void mul() override;
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virtual void triad() override;
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virtual void init_arrays(T initA, T initB, T initC) override;
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virtual void read_arrays(
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std::vector<T>& a, std::vector<T>& b, std::vector<T>& c) override;
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};
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