56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
71 lines
2.3 KiB
Plaintext
71 lines
2.3 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#pragma once
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//! \file
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//! \brief DOT algorithm
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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template <typename T>
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using vector_t = stackable_logical_data<slice<T>>;
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template <typename T>
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using scalar_t = stackable_logical_data<scalar_view<T>>;
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template <typename T = double>
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struct csr_matrix
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{
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csr_matrix(stackable_logical_data<slice<T>> _val_handle,
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stackable_logical_data<slice<size_t>> _row_handle,
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stackable_logical_data<slice<size_t>> _col_handle)
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: val_handle(mv(_val_handle))
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, row_handle(mv(_row_handle))
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, col_handle(mv(_col_handle))
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{}
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/* Description of the CSR */
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mutable stackable_logical_data<slice<T>> val_handle;
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mutable stackable_logical_data<slice<size_t>> row_handle;
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mutable stackable_logical_data<slice<size_t>> col_handle;
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};
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// Note that a and b might be the same logical data
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template <typename ctx_t, typename T>
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void DOT(ctx_t& ctx, vector_t<T>& a, vector_t<T>& b, scalar_t<T>& res)
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{
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ctx.parallel_for(a.shape(), a.read(), b.read(), res.reduce(reducer::sum<T>{})).set_symbol("DOT")->*
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[] __device__(size_t i, auto da, auto db, T& dres) {
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dres += da(i) * db(i);
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};
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};
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template <typename ctx_t, typename T>
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void SPMV(ctx_t& ctx, csr_matrix<T>& a, vector_t<T>& x, vector_t<T>& y)
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{
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ctx.parallel_for(y.shape(), a.val_handle.read(), a.col_handle.read(), a.row_handle.read(), x.read(), y.write())
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.set_symbol("SPMV")
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->*[] _CCCL_DEVICE(size_t row, auto da_val, auto da_col, auto da_row, auto dx, auto dy) {
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int row_start = da_row(row);
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int row_end = da_row(row + 1);
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double sum = 0.0;
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for (int elt = row_start; elt < row_end; elt++)
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{
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sum += da_val(elt) * dx(da_col(elt));
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}
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dy(row) = sum;
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};
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}
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