在GraphBLAS中高效实现推拉式
Implementing Push-Pull Efficiently in GraphBLAS
摘要：
我们因素比默的推挽式，也被称为方向优化的广度优先搜索（DOBFS）到3个可分离的优化，并分析它们的泛化能力，渐近加速，并对整体加速的贡献。我们表明，掩蔽是高性能的关键，可以推广到所有的图形算法的稀疏模式的输出是已知的先验。我们表明，这些图形算法优化，共同构成DOBFS，可以整齐，可分离地使用线性代数描述，并可以表示在GraphBLAS线性代数为基础的框架。我们提供的实验证据表明，通过这些优化，在基于线性代数的图形框架中表达的DOBFS在GPU和多线程CPU上与最先进的图形框架获得了具有竞争力的性能，在Scale 22 RMAT图形上实现了101 GTEPS。
英文摘要：
We factor Beamer's push-pull, also known as direction-optimized breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them for generalizability, asymptotic speedup, and contribution to overall speedup. We demonstrate that masking is critical for high performance and can be generalized to all graph algorithms where the sparsity pattern of the output is known a priori. We show that these graph algorithm optimizations, which together constitute DOBFS, can be neatly and separably described using linear algebra and can be expressed in the GraphBLAS linear-algebra-based framework. We provide experimental evidence that with these optimizations, a DOBFS expressed in a linear-algebra-based graph framework attains competitive performance with state-of-the-art graph frameworks on the GPU and on a multi-threaded CPU, achieving 101 GTEPS on a Scale 22 RMAT graph.
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