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[CCCL] Add missing CCCL components: c2h, nvbench_helper, cmake, cudax, AGENTS.md

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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
This commit is contained in:
muh-bot
2026-08-06 02:14:18 +00:00
parent b0d597363a
commit dedf08166a
864 changed files with 174321 additions and 0 deletions
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58
cccl_upstream/cudax/test/stf/algorithm/algorithm_with_read.cu Normal file
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//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
template <typename T>
void init(context& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
context ctx;
auto a = ctx.logical_data<int>(10000000);
auto b = ctx.logical_data<int>(10000000);
auto c = ctx.logical_data<int>(10000000);
auto d = ctx.logical_data<int>(10000000);
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 17);
/* a += 1; a += b; */
auto fn = [](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
sa(i) += 1;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
};
algorithm alg;
for (size_t i = 0; i < 100; i++)
{
alg.run_as_task(fn, ctx, a.rw(), b.read());
alg.run_as_task(fn, ctx, a.rw(), c.read());
alg.run_as_task(fn, ctx, c.rw(), d.read());
alg.run_as_task(fn, ctx, d.rw(), a.read());
}
ctx.finalize();
}

108
cccl_upstream/cudax/test/stf/algorithm/graph_algorithms.cu Normal file
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//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
template <typename ctx_t, typename T, typename T2>
void lib_call(ctx_t& ctx, logical_data<T> a, logical_data<T2> b)
{
nvtx_range r("lib_call");
// b *= 2
// a = a + b
// b = a
ctx.parallel_for(b.shape(), b.rw())->*[] __device__(size_t i, auto sb) {
sb(i) *= 2;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
}
template <typename T>
void init(context& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
context ctx;
auto a = ctx.logical_data<int>(size_t(10000000));
auto b = ctx.logical_data<int>(size_t(10000000));
auto c = ctx.logical_data<int>(size_t(10000000));
auto d = ctx.logical_data<int>(size_t(10000000));
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 42);
auto fn = [](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
ctx.parallel_for(b.shape(), b.rw())->*[] __device__(size_t i, auto sb) {
sb(i) *= 2;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
};
algorithm alg;
{
nvtx_range r("run");
for (size_t i = 0; i < 100; i++)
{
ctx.task(a.rw(), b.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sa, slice<int> sb) {
alg.run(fn, ctx, stream, sa, sb);
};
ctx.task(b.rw(), c.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sb, slice<int> sc) {
alg.run(fn, ctx, stream, sb, sc);
};
ctx.task(c.rw(), d.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sc, slice<int> sd) {
alg.run(fn, ctx, stream, sc, sd);
};
ctx.task(d.rw(), a.rw())->*[&alg, &fn, &ctx](cudaStream_t stream, slice<int> sd, slice<int> sa) {
alg.run(fn, ctx, stream, sd, sa);
};
}
}
{
nvtx_range r("run_as_task");
for (size_t i = 0; i < 100; i++)
{
alg.run_as_task(fn, ctx, a.rw(), b.rw());
alg.run_as_task(fn, ctx, a.rw(), c.rw());
alg.run_as_task(fn, ctx, c.rw(), d.rw());
alg.run_as_task(fn, ctx, d.rw(), a.rw());
}
}
ctx.finalize();
}

68
cccl_upstream/cudax/test/stf/algorithm/in_graph_ctx.cu Normal file
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//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
template <typename context_t, typename T>
void init(context_t& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
// context ctx = graph_ctx();
graph_ctx ctx;
auto a = ctx.logical_data<int>(size_t(1000000));
auto b = ctx.logical_data<int>(size_t(1000000));
auto c = ctx.logical_data<int>(size_t(1000000));
auto d = ctx.logical_data<int>(size_t(1000000));
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 42);
auto fn = [](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
sa(i) *= 3;
};
ctx.parallel_for(b.shape(), b.rw())->*[] __device__(size_t i, auto sb) {
sb(i) *= 2;
};
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
};
algorithm alg;
for (size_t i = 0; i < 5; i++)
{
alg.run_as_task(fn, ctx, a.rw(), b.rw());
alg.run_as_task(fn, ctx, a.rw(), c.rw());
alg.run_as_task(fn, ctx, c.rw(), d.rw());
alg.run_as_task(fn, ctx, d.rw(), a.rw());
}
ctx.finalize();
// cudaGraphDebugDotPrint(ctx.get_graph(), "pif.dot", 0);
}

67
cccl_upstream/cudax/test/stf/algorithm/nested.cu Normal file
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//===----------------------------------------------------------------------===//
//
// Part of CUDASTF in CUDA C++ Core Libraries,
// under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
//
//===----------------------------------------------------------------------===//
#include <cuda/experimental/stf.cuh>
using namespace cuda::experimental::stf;
template <typename T>
void init(context& ctx, logical_data<T> l, int val)
{
ctx.parallel_for(l.shape(), l.write())->*[=] __device__(size_t i, auto s) {
s(i) = val;
};
}
int main()
{
context ctx;
auto a = ctx.logical_data<int>(size_t(1000000));
auto b = ctx.logical_data<int>(size_t(1000000));
auto c = ctx.logical_data<int>(size_t(1000000));
auto d = ctx.logical_data<int>(size_t(1000000));
init(ctx, a, 12);
init(ctx, b, 35);
init(ctx, c, 42);
init(ctx, d, 17);
auto fn1 = [](context ctx, logical_data<slice<int>> a) {
ctx.parallel_for(a.shape(), a.rw())->*[] __device__(size_t i, auto sa) {
sa(i) += 1;
};
};
algorithm alg1;
auto fn2 = [&alg1, &fn1](context ctx, logical_data<slice<int>> a, logical_data<slice<int>> b) {
alg1.run_as_task(fn1, ctx, a.rw());
alg1.run_as_task(fn1, ctx, b.rw());
ctx.parallel_for(a.shape(), a.rw(), b.read())->*[] __device__(size_t i, auto sa, auto sb) {
sa(i) += sb(i);
};
ctx.parallel_for(a.shape(), a.read(), b.write())->*[] __device__(size_t i, auto sa, auto sb) {
sb(i) = sa(i);
};
};
algorithm alg2;
for (size_t i = 0; i < 100; i++)
{
alg2.run_as_task(fn2, ctx, a.rw(), b.rw());
alg2.run_as_task(fn2, ctx, a.rw(), c.rw());
alg2.run_as_task(fn2, ctx, c.rw(), d.rw());
alg2.run_as_task(fn2, ctx, d.rw(), a.rw());
}
ctx.finalize();
}