标题： 图计算在细粒度并行架构上能有多快 显示英文标题

标题： How Fast Can Graph Computations Go on Fine-grained Parallel Architectures

摘要： 大规模图问题具有关键且日益重要的地位，而历史上并行架构对此的支持很少。 本着协同设计的精神，我们探讨了这样一个问题：在细粒度架构上，图计算能有多快？ 我们探索了专为细粒度并行性、自然编程以及现实世界图中出现的不规则性和偏差进行优化的架构的可能性。 使用两个图基准测试，PageRank（PR）和广度优先搜索（BFS），我们评估了一个细粒度图架构 UpDown，以探索性能协同设计可以实现什么。 为了展示可编程性，我们编写了这些算法的五个变体。 对最多256个节点（524,288条通道）的仿真以及对16,384个节点（33M条通道）的预测表明，UpDown系统可以在RMAT上实现637K GTEPS PR和989K GTEPS BFS，分别超过最佳先前结果的5倍和100倍。 显示英文摘要

摘要： Large-scale graph problems are of critical and growing importance and historically parallel architectures have provided little support. In the spirit of co-design, we explore the question, How fast can graph computing go on a fine-grained architecture? We explore the possibilities of an architecture optimized for fine-grained parallelism, natural programming, and the irregularity and skew found in real-world graphs. Using two graph benchmarks, PageRank (PR) and Breadth-First Search (BFS), we evaluate a Fine-Grained Graph architecture, UpDown, to explore what performance codesign can achieve. To demonstrate programmability, we wrote five variants of these algorithms. Simulations of up to 256 nodes (524,288 lanes) and projections to 16,384 nodes (33M lanes) show the UpDown system can achieve 637K GTEPS PR and 989K GTEPS BFS on RMAT, exceeding the best prior results by 5x and 100x respectively.