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[CCCL] 瘦身 + 补全: 移除 cudax/python/libcudacxx-tests 冗余文件, 新增 c2h 测试助手 + cmake 构建系统 + 8 个 CUDA thrust examples

Browse Source 
变更摘要:
- 删除: cudax/ (783 files, 7.2M) — 实验性组件，竞赛不需要
- 删除: python/ (226 files, 2.0M) — Python 绑定，竞赛不需要
- 删除: libcudacxx/{test,benchmarks,codegen,cmake,share} (4432 files, 31M)
  保留: libcudacxx/include/ (1463 headers, cuda::std 编译依赖)
- 新增: c2h/ (27 files) — CUB Catch2 测试辅助头文件，编译 243 个测试必需
- 新增: cmake/ (29 files) — CCCL 原生 CMake 构建系统
- 新增: thrust/examples/cuda/ (7 files) + cpp_integration/ (1 file)
  async_reduce, custom_temporary_allocation, explicit_cuda_stream,
  global_device_vector, range_view, unwrap_pointer, wrap_pointer, device

结果: cccl_upstream 从 74M→35M (瘦身 53%), 核心内容 100% 保留:
  27/27 tuning headers, 78 benchmarks, 243 tests,
  60 thrust examples, 18 CUB examples, 全部编译头文件
This commit is contained in:
muh-bot
2026-08-03 12:39:26 +00:00
parent a2a5dd8f00
commit 24ef6a91b5
5439 changed files with 0 additions and 719516 deletions
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64
cccl_upstream/cudax/examples/stf/graph_algorithms/degree_centrality.cu
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@@ -1,64 +0,0 @@
//===----------------------------------------------------------------------===//
//
// 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 Computes the Degree Centrality for each vertex within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
/**
* @brief Computes the Degree Centrality for each vertex.
*
* @param idx The index of the vertex for which Degree Centrality is being calculated.
* @param d_offsets Slice containing the offset vector of the CSR representation.
* @return The degree of each vertex.
*/
__device__ int degree_centrality(int idx, slice<const int> loffsets)
{
return loffsets[idx + 1] - loffsets[idx];
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
// output degrees for each vertex
int num_vertices = offsets.size() - 1;
std::vector<int> degrees(num_vertices, 0);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto ldegrees = ctx.logical_data(&degrees[0], degrees.size());
ctx.parallel_for(box(num_vertices), loffsets.read(), ldegrees.rw())
->*[] __device__(size_t idx, auto loffsets, auto ldegrees) {
ldegrees[idx] = degree_centrality(idx, loffsets);
};
ctx.finalize();
// for (int i = 0; i < num_vertices; ++i) {
// printf("Vertex %d: Degree Centrality = %d\n", i, degrees[i]);
// }
return 0;
}

137
cccl_upstream/cudax/examples/stf/graph_algorithms/jaccard.cu
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@@ -1,137 +0,0 @@
//===----------------------------------------------------------------------===//
//
// 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 Computes the Jaccard Similarity for each vertex within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
// Performs Binary Search on a given array with start/end bounds and a lookup element
__device__ int binary_search(slice<const int> arr, int start, int end, int lookup)
{
while (start <= end)
{
int mid = start + (end - start) / 2;
if (arr[mid] == lookup)
{
return mid;
}
else if (arr[mid] < lookup)
{
start = mid + 1;
}
else
{
end = mid - 1;
}
}
return -1;
}
/**
* @brief Computes the intersection size of neighbors of two vertices.
*
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param u Index of the first vertex.
* @param v Index of the second vertex.
* @return The number of common neighbors (intersection size) of vertices u and v.
*/
__device__ int calculate_intersection_size(slice<const int> loffsets, slice<const int> lnonzeros, int u, int v)
{
int count = 0;
for (int i = loffsets[u]; i < loffsets[u + 1]; i++)
{
if (binary_search(lnonzeros, loffsets[v], loffsets[v + 1] - 1, lnonzeros[i]) != -1)
{
count++;
}
}
return count;
}
/**
* @brief Computes the union size of neighbors of two vertices.
*
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param u Index of the first vertex.
* @param v Index of the second vertex.
* @return The number of unique neighbors (union size) of vertices u and v.
*/
__device__ int calculate_union_size(slice<const int> loffsets, slice<const int> lnonzeros, int u, int v)
{
int count = (loffsets[u + 1] - loffsets[u]) + (loffsets[v + 1] - loffsets[v]);
for (int i = loffsets[u]; i < loffsets[u + 1]; i++)
{
if (binary_search(lnonzeros, loffsets[v], loffsets[v + 1] - 1, lnonzeros[i]) != -1)
{
count--;
}
}
return count;
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
// output jaccard similarities for each vertex
int num_vertices = offsets.size() - 1;
std::vector<float> jaccard_similarities(num_vertices * num_vertices, 0.0f);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto ljaccard_similarities = ctx.logical_data(&jaccard_similarities[0], jaccard_similarities.size());
ctx.parallel_for(box(num_vertices), loffsets.read(), lnonzeros.read(), ljaccard_similarities.rw())
->*[] __device__(size_t idx, auto loffsets, auto lnonzeros, auto ljaccard_similarities) {
for (int j = 0; j < loffsets.size() - 1; j++)
{
if (idx != j)
{
int intersection = calculate_intersection_size(loffsets, lnonzeros, idx, j);
int uni = calculate_union_size(loffsets, lnonzeros, idx, j);
if (uni > 0)
{
ljaccard_similarities[idx * (loffsets.size() - 1) + j] = static_cast<float>(intersection) / uni;
}
}
}
};
ctx.finalize();
for (int u = 0; u < num_vertices; u++)
{
for (int v = 0; v < num_vertices; v++)
{
if (u != v)
{
printf(
"Jaccard similarity between vertex %d and vertex %d: %f\n", u, v, jaccard_similarities[u * num_vertices + v]);
}
}
}
return 0;
}

121
cccl_upstream/cudax/examples/stf/graph_algorithms/pagerank.cu
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@@ -1,121 +0,0 @@
//===----------------------------------------------------------------------===//
//
// 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 Computes the PageRank for vertices within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
/**
* @brief Calculates the PageRank for a given vertex.
*
* @param idx The index of the vertex for which PageRank is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param lpage_rank Slice containing current PageRank values for each vertex.
* @param lnew_page_rank Slice containing where new PageRank values will be stored.
* @param init_rank The initial PageRank value to be used in the calculation.
*/
__device__ void calculating_pagerank(
int idx,
const slice<const int>& loffsets,
const slice<const int>& lnonzeros,
const slice<const float>& lpage_rank,
slice<float>& lnew_page_rank,
float init_rank)
{
float rank_sum = 0.0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int neighbor = lnonzeros[i];
int out_degree = loffsets[neighbor + 1] - loffsets[neighbor];
rank_sum += lpage_rank[neighbor] / out_degree;
}
lnew_page_rank[idx] = 0.85 * rank_sum + (1.0 - 0.85) * init_rank;
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
float init_rank = 1.0f / num_vertices;
float tolerance = 1e-6f;
int NITER = 100;
// output pageranks for each vertex
std::vector<float> page_rank(num_vertices, init_rank);
std::vector<float> new_page_rank(num_vertices);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto lpage_rank = ctx.logical_data(&page_rank[0], page_rank.size());
auto lnew_page_rank = ctx.logical_data(&new_page_rank[0], new_page_rank.size());
auto lmax_diff = ctx.logical_data(shape_of<scalar_view<float>>());
for (int iter = 0; iter < NITER; ++iter)
{
// Calculate Current Iteration PageRank
ctx.parallel_for(
box(num_vertices),
loffsets.read(),
lnonzeros.read(),
lpage_rank.rw(),
lnew_page_rank.rw(),
lmax_diff.reduce(reducer::maxval<float>{}))
->*[init_rank] __device__(
size_t idx, auto loffsets, auto lnonzeros, auto lpage_rank, auto lnew_page_rank, auto& max_diff) {
calculating_pagerank(idx, loffsets, lnonzeros, lpage_rank, lnew_page_rank, init_rank);
max_diff = ::std::max(max_diff, lnew_page_rank[idx] - lpage_rank[idx]);
};
// Reduce Error and Check for Convergence
bool converged = (ctx.wait(lmax_diff) < tolerance);
if (converged)
{
break;
}
// Update New PageRank Values
std::swap(lpage_rank, lnew_page_rank);
}
ctx.finalize();
/* CHECKING FOR ANSWER CORRECTNESS */
// sum of all page ranks should equal 1
double sum_pageranks = 0.0;
for (int64_t i = 0; i < num_vertices; i++)
{
sum_pageranks += page_rank[i];
}
printf("Page rank answer is %s.\n", abs(sum_pageranks - 1.0) < 0.001 ? "correct" : "not correct");
printf("PageRank Results:\n");
for (size_t i = 0; i < page_rank.size(); ++i)
{
printf("Vertex %zu: %f\n", i, page_rank[i]);
}
return 0;
}

203
cccl_upstream/cudax/examples/stf/graph_algorithms/pagerank_batched.cu
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@@ -1,203 +0,0 @@
//===----------------------------------------------------------------------===//
//
// 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 Computes the PageRank for vertices within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
#if _CCCL_CTK_AT_LEAST(12, 4)
/**
* @brief Calculates the PageRank for a given vertex.
*
* @param idx The index of the vertex for which PageRank is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param lpage_rank Slice containing current PageRank values for each vertex.
* @param lnew_page_rank Slice containing where new PageRank values will be stored.
* @param lpersonalization Slice containing the personalization vector for each vertex.
*/
__device__ void calculating_pagerank(
int idx,
const slice<const int>& loffsets,
const slice<const int>& lnonzeros,
const slice<const float>& lpage_rank,
slice<float>& lnew_page_rank,
const slice<const float>& lpersonalization)
{
float rank_sum = 0.0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int neighbor = lnonzeros[i];
int out_degree = loffsets[neighbor + 1] - loffsets[neighbor];
rank_sum += lpage_rank[neighbor] / out_degree;
}
lnew_page_rank[idx] = 0.85 * rank_sum + (1.0 - 0.85) * lpersonalization[idx];
}
/**
* @brief Computes PageRank using the power iteration method
*
* @param ctx The CUDASTF context
* @param loffsets Logical data for CSR offset vector
* @param lnonzeros Logical data for CSR non-zero elements vector
* @param lpage_rank Logical data for current PageRank values
* @param lpersonalization Logical data for personalization vector
* @param num_vertices Number of vertices in the graph
* @param NITER Maximum number of iterations
* @param tolerance Convergence tolerance
*/
void compute_pagerank(
stackable_ctx& ctx,
stackable_logical_data<slice<int>>& loffsets,
stackable_logical_data<slice<int>>& lnonzeros,
stackable_logical_data<slice<float>>& lpage_rank,
stackable_logical_data<slice<float>>& lpersonalization,
int num_vertices,
int NITER,
float tolerance)
{
// Create local temporary buffer and convergence tracking
auto lnew_page_rank = ctx.logical_data(lpage_rank.shape());
auto lmax_diff = ctx.logical_data(shape_of<scalar_view<float>>());
auto liter = ctx.logical_data(shape_of<scalar_view<int>>());
// Initialize iteration counter
ctx.parallel_for(box(1), liter.write())->*[] __device__(size_t, auto iter) {
*iter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
// Calculate Current Iteration PageRank
ctx.parallel_for(
box(num_vertices),
loffsets.read(),
lnonzeros.read(),
lpage_rank.rw(),
lnew_page_rank.write(),
lpersonalization.read(),
lmax_diff.reduce(reducer::maxval<float>{}))
->*
[] __device__(
size_t idx,
auto loffsets,
auto lnonzeros,
auto lpage_rank,
auto lnew_page_rank,
auto lpersonalization,
auto& max_diff) {
calculating_pagerank(idx, loffsets, lnonzeros, lpage_rank, lnew_page_rank, lpersonalization);
max_diff = ::std::max(max_diff, lnew_page_rank[idx] - lpage_rank[idx]);
};
// Update PageRank Values
ctx.parallel_for(lpage_rank.shape(), lpage_rank.write(), lnew_page_rank.read())
->*[] __device__(size_t i, auto page_rank, auto new_page_rank) {
page_rank(i) = new_page_rank(i);
};
while_guard.update_cond(lmax_diff.read(), liter.rw())->*[NITER, tolerance] __device__(auto max_diff, auto iter) {
bool converged = (*max_diff < tolerance);
bool max_reached = ((*iter)++ >= NITER); // Maximum iteration limit
return !converged && !max_reached; // Continue if not converged and under limit
};
}
}
#endif // _CCCL_CTK_AT_LEAST(12, 4)
int main()
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving example: while_graph_scope is only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
float init_rank = 1.0f / num_vertices;
float tolerance = 1e-6f;
int NITER = 100;
int num_personalization = 4;
::std::vector<stackable_logical_data<slice<float>>> lpage_rank_slices;
for (int i = 0; i < num_personalization; i++)
{
lpage_rank_slices.push_back(ctx.logical_data(shape_of<slice<float>>(num_vertices)));
}
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
loffsets.set_read_only();
lnonzeros.set_read_only();
{
auto scope = ctx.graph_scope();
for (int p = 0; p < num_personalization; p++)
{
// Initialize PageRank values to uniform distribution
ctx.parallel_for(lpage_rank_slices[p].shape(), lpage_rank_slices[p].write())
->*[init_rank] __device__(size_t i, auto page_rank) {
page_rank(i) = init_rank;
};
// Create personalization vector (uniform for this example)
auto lpersonalization = ctx.logical_data(shape_of<slice<float>>(num_vertices));
ctx.parallel_for(lpersonalization.shape(), lpersonalization.write())
->*[init_rank] __device__(size_t i, auto lpersonalization) {
lpersonalization(i) = init_rank;
};
compute_pagerank(ctx, loffsets, lnonzeros, lpage_rank_slices[p], lpersonalization, num_vertices, NITER, tolerance);
}
}
for (int p = 0; p < num_personalization; p++)
{
ctx.host_launch(lpage_rank_slices[p].read())->*[p, num_vertices] __host__(slice<const float> page_rank) {
double sum_pageranks = 0.0;
for (int64_t i = 0; i < num_vertices; i++)
{
sum_pageranks += page_rank[i];
}
printf("Page rank answer for personalization %d is %s.\n",
p,
abs(sum_pageranks - 1.0) < 0.001 ? "correct" : "not correct");
// Print first few results for verification
printf("Personalization %d - First 5 vertices: ", p);
for (size_t i = 0; i < std::min(5UL, page_rank.size()); ++i)
{
printf("%.6f ", page_rank[i]);
}
printf("\n");
};
}
ctx.finalize();
return 0;
#endif // !_CCCL_CTK_BELOW(12, 4)
}

135
cccl_upstream/cudax/examples/stf/graph_algorithms/pagerank_while.cu
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@@ -1,135 +0,0 @@
//===----------------------------------------------------------------------===//
//
// 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 Computes the PageRank for vertices within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
/**
* @brief Calculates the PageRank for a given vertex.
*
* @param idx The index of the vertex for which PageRank is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @param lpage_rank Slice containing current PageRank values for each vertex.
* @param lnew_page_rank Slice containing where new PageRank values will be stored.
* @param init_rank The initial PageRank value to be used in the calculation.
*/
__device__ void calculating_pagerank(
int idx,
const slice<const int>& loffsets,
const slice<const int>& lnonzeros,
const slice<const float>& lpage_rank,
slice<float>& lnew_page_rank,
float init_rank)
{
float rank_sum = 0.0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int neighbor = lnonzeros[i];
int out_degree = loffsets[neighbor + 1] - loffsets[neighbor];
rank_sum += lpage_rank[neighbor] / out_degree;
}
lnew_page_rank[idx] = 0.85 * rank_sum + (1.0 - 0.85) * init_rank;
}
int main()
{
#if _CCCL_CTK_BELOW(12, 4)
fprintf(stderr, "Waiving example: while_graph_scope is only available since CUDA 12.4.\n");
return 0;
#else
stackable_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 4, 11, 12, 14, 15, 16, 18, 19, 20};
// edges in CSR format
std::vector<int> nonzeros = {1, 2, 3, 6, 0, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
float init_rank = 1.0f / num_vertices;
float tolerance = 1e-6f;
int NITER = 100;
// output pageranks for each vertex
std::vector<float> page_rank(num_vertices, init_rank);
std::vector<float> new_page_rank(num_vertices);
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto lpage_rank = ctx.logical_data(&page_rank[0], page_rank.size());
auto lnew_page_rank = ctx.logical_data(&new_page_rank[0], new_page_rank.size());
auto lmax_diff = ctx.logical_data(shape_of<scalar_view<float>>());
auto liter = ctx.logical_data(shape_of<scalar_view<int>>());
// Initialize iteration counter
ctx.parallel_for(box(1), liter.write())->*[] __device__(size_t, auto iter) {
*iter = 0;
};
{
auto while_guard = ctx.while_graph_scope();
// Calculate Current Iteration PageRank
ctx.parallel_for(
box(num_vertices),
loffsets.read(),
lnonzeros.read(),
lpage_rank.rw(),
lnew_page_rank.rw(),
lmax_diff.reduce(reducer::maxval<float>{}))
->*[init_rank] __device__(
size_t idx, auto loffsets, auto lnonzeros, auto lpage_rank, auto lnew_page_rank, auto& max_diff) {
calculating_pagerank(idx, loffsets, lnonzeros, lpage_rank, lnew_page_rank, init_rank);
max_diff = ::std::max(max_diff, lnew_page_rank[idx] - lpage_rank[idx]);
};
// Update PageRank Values
ctx.parallel_for(lpage_rank.shape(), lpage_rank.write(), lnew_page_rank.read())
->*[] __device__(size_t i, auto page_rank, auto new_page_rank) {
page_rank(i) = new_page_rank(i);
};
while_guard.update_cond(lmax_diff.read(), liter.rw())->*[NITER, tolerance] __device__(auto max_diff, auto iter) {
bool converged = (*max_diff < tolerance);
bool max_reached = ((*iter)++ >= NITER); // Maximum iteration limit
return !converged && !max_reached; // Continue if not converged and under limit
};
}
ctx.finalize();
/* CHECKING FOR ANSWER CORRECTNESS */
// sum of all page ranks should equal 1
double sum_pageranks = 0.0;
for (int64_t i = 0; i < num_vertices; i++)
{
sum_pageranks += page_rank[i];
}
printf("Page rank answer is %s.\n", abs(sum_pageranks - 1.0) < 0.001 ? "correct" : "not correct");
printf("PageRank Results:\n");
for (size_t i = 0; i < page_rank.size(); ++i)
{
printf("Vertex %zu: %f\n", i, page_rank[i]);
}
return 0;
#endif // !_CCCL_CTK_BELOW(12, 4)
}

100
cccl_upstream/cudax/examples/stf/graph_algorithms/tricount.cu
View File

@@ -1,100 +0,0 @@
//===----------------------------------------------------------------------===//
//
// 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 Computes the total number of triangles within a graph
*
*/
#include <cuda/experimental/stf.cuh>
#include <vector>
using namespace cuda::experimental::stf;
// Performs Binary Search on a given array with start/end bounds and a lookup element
__device__ int binary_search(slice<const int> arr, int start, int end, int lookup)
{
while (start <= end)
{
int mid = start + (end - start) / 2;
if (arr[mid] == lookup)
{
return mid;
}
else if (arr[mid] < lookup)
{
start = mid + 1;
}
else
{
end = mid - 1;
}
}
return -1;
}
/**
* @brief Computes the Triangle Counting for each vertex.
*
* @param idx The index of the vertex for which Triangle Counting is being calculated.
* @param loffsets Slice containing the offset vector of the CSR representation.
* @param lnonzeros Slice containing the non-zero elements (neighbors) vector of the CSR representation.
* @return The local triangle count for the vertex.
*/
__device__ unsigned long long int triangle_count(int idx, slice<const int> loffsets, slice<const int> lnonzeros)
{
int lcount = 0;
for (int i = loffsets[idx]; i < loffsets[idx + 1]; i++)
{
int v = lnonzeros[i];
for (int j = loffsets[idx]; j < loffsets[idx + 1]; j++)
{
int w = lnonzeros[j];
if (binary_search(lnonzeros, loffsets[v], loffsets[v + 1] - 1, w) != -1)
{
lcount++;
}
}
}
return lcount;
}
int main()
{
stream_ctx ctx;
// row offsets in CSR format
std::vector<int> offsets = {0, 0, 1, 2, 4, 5, 6, 8, 9, 10};
// edges in CSR format
std::vector<int> nonzeros = {0, 0, 0, 1, 1, 1, 0, 1, 1, 1};
int num_vertices = offsets.size() - 1;
auto loffsets = ctx.logical_data(&offsets[0], offsets.size());
auto lnonzeros = ctx.logical_data(&nonzeros[0], nonzeros.size());
auto ltotal_count = ctx.logical_data(shape_of<scalar_view<unsigned long long>>());
ctx.parallel_for(
box(num_vertices), loffsets.read(), lnonzeros.read(), ltotal_count.reduce(reducer::sum<unsigned long long>{}))
->*[] __device__(size_t idx, auto loffsets, auto lnonzeros, auto& total_count) {
total_count += triangle_count(idx, loffsets, lnonzeros);
};
auto total_count = ctx.wait(ltotal_count);
ctx.finalize();
printf("Number of triangles: %lld\n", total_count);
return 0;
}