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
125 lines
3.0 KiB
Plaintext
125 lines
3.0 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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#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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static __global__ void scalar_div(const double* a, const double* b, double* c)
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{
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*c = (*a) / (*b);
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}
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static __global__ void scalar_minus(const double* a, double* res)
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{
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*res = -(*a);
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}
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/**
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* This class is an example of class to issue tasks when accessing a scalar
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* value.
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*
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* This is not meant to be the most efficient approach, but this is
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* supposedly convenient.
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*
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*/
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template <typename Ctx>
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class scalar
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{
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public:
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scalar(Ctx* ctx, bool is_tmp = false)
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: ctx(ctx)
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{
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size_t s = sizeof(double);
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if (is_tmp)
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{
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// There is no physical backing for this temporary vector
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h_addr = NULL;
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}
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else
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{
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h_addr = (double*) malloc(s);
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cuda_safe_call(cudaHostRegister(h_addr, s, cudaHostRegisterPortable));
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}
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data_place d = is_tmp ? data_place::invalid() : data_place::host();
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handle = ctx->logical_data(make_slice(h_addr), d);
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}
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// Copy constructor
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scalar(const scalar& a)
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: ctx(a.ctx)
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{
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h_addr = NULL;
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handle = ctx->logical_data(make_slice((double*) nullptr));
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ctx->task(handle.write(), a.handle.read())->*[](cudaStream_t stream, auto dst, auto src) {
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// There are likely much more efficient ways.
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cuda_safe_call(
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cudaMemcpyAsync(dst.data_handle(), src.data_handle(), sizeof(double), cudaMemcpyDeviceToDevice, stream));
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};
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}
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scalar operator/(scalar const& rhs) const
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{
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// Submit a task that computes this/rhs
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scalar res(ctx);
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ctx->task(handle.read(), rhs.handle.read(), res.handle.write())
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->*[](cudaStream_t stream, auto x, auto y1, auto result) {
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scalar_div<<<1, 1, 0, stream>>>(x.data_handle(), y1.data_handle(), result.data_handle());
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};
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return res;
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}
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scalar operator-() const
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{
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// Submit a task that computes -s
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scalar res(ctx);
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ctx->task(handle.read(), res.handle.write())->*[](cudaStream_t stream, auto x, auto result) {
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scalar_minus<<<1, 1, 0, stream>>>(x.data_handle(), result.data_handle());
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};
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return res;
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}
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Ctx* ctx;
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mutable logical_data<slice<double, 0>> handle;
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double* h_addr;
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};
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template <typename Ctx>
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void run()
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{
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Ctx ctx;
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scalar a(&ctx);
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scalar b(&ctx);
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*a.h_addr = 42.0;
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*b.h_addr = 12.3;
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scalar c = (-a) / b;
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ctx.host_launch(c.handle.read())->*[](auto x) {
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EXPECT(fabs(*x.data_handle() - (-42.0) / 12.3) < 0.001);
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};
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ctx.finalize();
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}
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int main()
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{
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run<stream_ctx>();
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run<graph_ctx>();
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}
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