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gunrock

C/C++

Gunrock 2.1.0

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Gunrock：CUDA/C ++ GPU图分析

例子	项目模板	文档	github动作

Gunrock ¹是一个用于GPU专门设计的图形处理的CUDA库。它使用高级同步/异步，以数据为中心的抽象，重点介绍了顶点或边缘边缘的操作。 Gunrock通过耦合高性能GPU计算基础和优化策略，尤其是在细粒载荷平衡领域，通过使用高级编程模型，使程序员能够快速开发新的图形原始图，从而在note gpus缩小node sips sips sige sige sige sige sige sign sige and gpus sprape gpu和simimal gpu sprogramss nek gpus cons gpu计算策略和优化策略之间取得了平衡。

分支	目的	版本	地位
`main`	从`gunrock/essentials`移植的默认分支是正式发行分支。	$ geq $ `2.xx`	积极的
`develop`	开发功能分支，从`gunrock/essentials`移植。	$ geq $ `2.xx`	积极的
`master`	`gunrock/gunrock`版本`1.xx`接口的先前版本分支，保留所有提交历史记录。	$ leq $ `1.xx`	弃用
`dev`	`gunrock/gunrock`的先前开发分支。现在，所有更改都合并为主`master` 。	$ leq $ `1.xx`	弃用

快速启动指南

在构建Gunrock之前，请确保系统上安装了CUDA工具包² 。其他外部依赖性（例如NVIDIA/thrust ， NVIDIA/cub等）会自动使用cmake获取。

git clone https://github.com/gunrock/gunrock.git
cd gunrock
mkdir build && cd build
cmake .. 
make sssp # or for all algorithms, use: make -j$(nproc)
bin/sssp ../datasets/chesapeake/chesapeake.mtx

实现图算法

有关详细说明，请参阅完整的文档。下面的示例显示了使用Gunrock的以数据为中心的散装同步编程模型的简单API，我们在GPU上实施了广度优先的搜索。此示例跳过了创建problem_t的设置阶段，并直接跳入了enactor_t算法。

我们首先使用初始源顶点准备前沿，以开始基于推动的BFS遍历。一个简单的f->push_back(source)放置了我们将用于第一次迭代的初始顶点。

 void prepare_frontier ( frontier_t * f,
                      gcuda:: multi_context_t & context) override {
  auto P = this -> get_problem ();
  f-> push_back (P-> param . single_source );
}

然后，我们开始我们的迭代循环，该循环迭代直至满足收敛条件。如果未指定条件，则当边界为空时，环会收敛。

 void loop (gcuda:: multi_context_t & context) override {
  auto E = this -> get_enactor ();   // Pointer to enactor interface.
  auto P = this -> get_problem ();   // Pointer to problem (data) interface.
  auto G = P-> get_graph ();        // Graph that we are processing.

  auto single_source = P-> param . single_source ;  // Initial source node.
  auto distances = P-> result . distances ;         // Distances array for BFS.
  auto visited = P-> visited . data (). get ();       // Visited map.
  auto iteration = this -> iteration ;             // Iteration we are on.

  // Following lambda expression is applied on every source,
  // neighbor, edge, weight tuple during the traversal.
  // Our intent here is to find and update the minimum distance when found.
  // And return which neighbor goes in the output frontier after traversal.
  auto search = [=] __host__ __device__ (
                      vertex_t const & source,    // ... source
                      vertex_t const & neighbor,  // neighbor
                      edge_t const & edge,        // edge
                      weight_t const & weight     // weight (tuple).
                      ) -> bool {
    auto old_distance =
      math::atomic::min (&distances[neighbor], iteration + 1 );
    return (iteration + 1 < old_distance);
  };

  // Execute advance operator on the search lambda expression.
  // Uses load_balance_t::block_mapped algorithm (try others for perf. tuning.)
  operators::advance::execute<operators:: load_balance_t ::block_mapped>(
    G, E, search, context);
}

包括/gunrock/算法/bfs.hxx

如何引用Gunrock＆Essentials

感谢您援引我们的工作。

 @article { Wang:2017:GGG ,
  author =	 { Yangzihao Wang and Yuechao Pan and Andrew Davidson
                  and Yuduo Wu and Carl Yang and Leyuan Wang and
                  Muhammad Osama and Chenshan Yuan and Weitang Liu and
                  Andy T. Riffel and John D. Owens } ,
  title =	 { {G}unrock: {GPU} Graph Analytics } ,
  journal =	 { ACM Transactions on Parallel Computing } ,
  year =	 2017 ,
  volume =	 4 ,
  number =	 1 ,
  month =	 aug,
  pages =	 { 3:1--3:49 } ,
  doi =		 { 10.1145/3108140 } ,
  ee =		 { http://arxiv.org/abs/1701.01170 } ,
  acmauthorize = { https://dl.acm.org/doi/10.1145/3108140?cid=81100458295 } ,
  url =		 { http://escholarship.org/uc/item/9gj6r1dj } ,
  code =	 { https://github.com/gunrock/gunrock } ,
  ucdcite =	 { a115 } ,
}

 @InProceedings { Osama:2022:EOP ,
  author =	 { Muhammad Osama and Serban D. Porumbescu and John D. Owens } ,
  title =	 { Essentials of Parallel Graph Analytics } ,
  booktitle =	 { Proceedings of the Workshop on Graphs,
                  Architectures, Programming, and Learning } ,
  year =	 2022 ,
  series =	 { GrAPL 2022 } ,
  month =	 may,
  pages =	 { 314--317 } ,
  doi =		 { 10.1109/IPDPSW55747.2022.00061 } ,
  url =          { https://escholarship.org/uc/item/2p19z28q } ,
}