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project_6/cccl_upstream/cudax/examples/stf/pi.cu
EngineX CI 56fd68e7dd [INFRA] Import NVIDIA/CCCL upstream as optimization reference library
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
2026-07-30 09:35:51 +00:00

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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.
//
//===----------------------------------------------------------------------===//
/**
* @file
*
* @brief Approximate pi using Monte Carlo method
*
*/
#include <cuda/experimental/stf.cuh>
#include <curand_kernel.h>
#include <stdio.h>
using namespace cuda::experimental::stf;
int main(int, char**)
{
context ctx;
auto lsum = ctx.logical_data(shape_of<scalar_view<size_t>>());
size_t N = 1000000;
ctx.parallel_for(box(N), lsum.reduce(reducer::sum<size_t>{}))->*[] __device__(size_t i, auto& sum) {
curandState local_state;
curand_init(1234, i, 0, &local_state);
double x = curand_uniform_double(&local_state); // Random x in [0, 1)
double y = curand_uniform_double(&local_state); // Random y in [0, 1)
// Count (x,y) coordinates which are within the unit circle
if (x * x + y * y <= 1.0)
{
sum++;
}
};
// We get the ratio of "shots" within the unit circle and the total number of
// "shots". The surface of the quarter of unit circle [0, 1) x [0, 1) is pi/4
auto res = ctx.wait(lsum);
double pi_val = (4.0 * res) / N;
ctx.finalize();
_CCCL_ASSERT(fabs(pi_val - 3.1415) < 0.1, "Invalid result");
}