不规则算法在鸸鹋上的编程策略
Programming Strategies for Irregular Algorithms on the Emu Chick
摘要：
Emu Chick原型在一个新颖的硬件系统中实现了迁移存储器端处理。Emu Chick不是通过系统互连传输大量数据，而是在每次远程内存读取开始之前将轻量级线程上下文移动到近内存核心。以前的工作的特点是性能的小鸡原型方面的内存带宽和编程的差异，从更典型的，非迁移平台，但还没有一个分析的算法在这个系统上。这项工作评估不规则的算法，可以受益于轻量级的，内存侧处理的小鸡，并展示了技术和优化策略，实现性能稀疏矩阵向量乘法运算（SpMV），广度优先搜索（BFS），并在多达8个分布式节点，包括64 nodelets在小鸡系统中的图形对齐。我们还定义和证明相对指标比较原型FPGA为基础的硬件与既定的ASIC架构。Chick目前支持高达68倍的图形对齐缩放，80 MTEPS的平衡图形上的BFS，以及50%的SpMV测量STREAM带宽。
英文摘要：
The Emu Chick prototype implements migratory memory-side processing in a novel hardware system. Rather than transferring large amounts of data across the system interconnect, the Emu Chick moves lightweight thread contexts to near-memory cores before the beginning of each remote memory read. Previous work has characterized the performance of the Chick prototype in terms of memory bandwidth and programming differences from more typical, non-migratory platforms, but there has not yet been an analysis of algorithms on this system. This work evaluates irregular algorithms that could benefit from the lightweight, memory-side processing of the Chick and demonstrates techniques and optimization strategies for achieving performance in sparse matrix-vector multiply operation (SpMV), breadth-first search (BFS), and graph alignment across up to eight distributed nodes encompassing 64 nodelets in the Chick system. We also define and justify relative metrics to compare prototype FPGA-based hardware with established ASIC architectures. The Chick currently supports up to 68x scaling for graph alignment, 80 MTEPS for BFS on balanced graphs, and 50\% of measured STREAM bandwidth for SpMV.
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