dedf08166a
Added 863 files from NVIDIA/cccl sparse checkout: - c2h/ (27 files): Catch2 test helpers — generators, validators, runner - nvbench_helper/ (10 files): Benchmark harness utilities - cmake/ (29 files): CMake presets and build helpers - cudax/ (794 files): Experimental CUDA extensions - AGENTS.md: NVIDIA's official AI agent instructions for CCCL - CMakePresets.json: Standardized build configurations - cccl-version.json: Version tracking Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to competition value and PRD items. cccl_upstream now covers 100% of competition-critical assets: - 27 tuning headers (SM80/90/100 benchmark data) - 32 dispatch headers (algorithm implementations) - 60 Thrust examples (correctness verification) - 217 CUB Catch2 tests (regression matrix) - 153 CUB benchmarks (parameter space search) - 18 CUB examples (API verification) - 27 test helpers + benchmark harness - 794 cudax experimental extensions
121 lines
3.6 KiB
Plaintext
121 lines
3.6 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/stream/reduction.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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#include <iostream>
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using namespace cuda::experimental::stf;
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using scalar_t = slice_stream_interface<int, 1>;
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template <typename T>
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__global__ void set_value(T* addr, T val)
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{
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*addr = val;
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}
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template <typename T>
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__global__ void add(const T* in_addr, T* inout_addr)
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{
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*inout_addr += *in_addr;
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}
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/*
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* Define a SUM reduction operator over a scalar
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*/
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class scalar_sum_t : public stream_reduction_operator_untyped
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{
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public:
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scalar_sum_t()
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: stream_reduction_operator_untyped() {};
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void stream_redux_op(
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logical_data_untyped& d,
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const data_place& /*unused*/,
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instance_id_t inout_instance_id,
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const data_place& /*unused*/,
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instance_id_t in_instance_id,
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const exec_place& /*unused*/,
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cudaStream_t s) override
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{
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auto& in_instance = d.instance<typename scalar_t::element_type>(in_instance_id);
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auto& inout_instance = d.instance<typename scalar_t::element_type>(inout_instance_id);
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add<<<1, 1, 0, s>>>(in_instance.data_handle(), inout_instance.data_handle());
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}
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void stream_init_op(logical_data_untyped& d,
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const data_place& /*unused*/,
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instance_id_t out_instance_id,
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const exec_place& /*unused*/,
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cudaStream_t s) override
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{
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auto& out_instance = d.instance<typename scalar_t::element_type>(out_instance_id);
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// fprintf(stderr, "REDUX INIT d %p memory node %d instance id %d => addr %p\n", d, out_memory_node,
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// out_instance_id, *out_instance);
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set_value<<<1, 1, 0, s>>>(out_instance.data_handle(), 0);
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}
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};
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int main()
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{
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stream_ctx ctx;
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const int N = 128;
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// We have an array, and a handle for each entry of the array
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int array[N];
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logical_data<slice<int>> array_handles[N];
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/*
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* We are going to compute the sum of this array
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*/
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for (int i = 0; i < N; i++)
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{
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array[i] = i;
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array_handles[i] = ctx.logical_data(&array[i], {1});
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array_handles[i].set_symbol(std::string("array[") + std::to_string(i) + std::string("]"));
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}
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logical_data<slice<int>> var_handle = ctx.logical_data(shape_of<slice<int>>(1));
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var_handle.set_symbol("var");
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int check_sum = 0;
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for (int i = 0; i < N; i++)
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{
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check_sum += array[i];
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}
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auto redux_op = std::make_shared<scalar_sum_t>();
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for (int i = 0; i < N; i++)
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{
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ctx.task(var_handle.relaxed(redux_op), array_handles[i].read())
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->*[](cudaStream_t stream, auto d_var, auto d_array_i) {
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add<<<1, 1, 0, stream>>>(d_array_i.data_handle(), d_var.data_handle());
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};
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}
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// Force the reconstruction of data on the device, so that no transfers are
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// necessary while reconstructing the result.
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// This will of course not be necessary in the future ...
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ctx.task(var_handle.read())->*[](cudaStream_t /*unused*/, auto /*unused*/) {};
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// Check result
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ctx.task(exec_place::host(), var_handle.read())->*[=](cudaStream_t stream, auto h_var) {
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cuda_safe_call(cudaStreamSynchronize(stream));
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int value = h_var(0);
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EXPECT(value == check_sum);
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};
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ctx.finalize();
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}
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