Simulation and performance estimation for the Rewrite Rule Machine

Hitoshi Aida, J. Goguen, Sany M. Leinwand, P. Lincoln, J. Meseguer, B. Taheri, T. Winkler
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引用次数: 11

Abstract

The authors give an overview of the Rewrite Rule Machine's (RRM's) architecture and discuss performance estimates based on very detailed register-level simulations at the chip level, together with more abstract simulations and modeling for higher levels. For a 10000 ensemble RRM, the present estimates are as follows. (1) The raw peak performance is 576 trillion operations per second. (2) For general symbolic applications, ensemble Sun-relative speedup is roughly 6.7, and RRM performance with a wormhole network at 88% efficiency gives an idealized Sun-relative speedup of 59000. (3) For highly regular symbolic applications (the sorting problem is taken as a typical example), ensemble performance is a Sun-relative speedup of 127, and RRM performance is estimated at over 80% efficiency (relative to the cluster performance), yielding a Sun-relative speedup of over 91. (4) For systolic applications (a 2-D fluid flow problem is taken as a typical example), ensemble performance is a Sun-relative speedup of 400-670, and cluster-level performance, which should be attainable in practice, is at 82% efficiency.<>
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改写规则机的仿真与性能评估
作者概述了重写规则机(RRM)的体系结构,并讨论了基于芯片级非常详细的寄存器级模拟的性能估计,以及更抽象的模拟和更高级别的建模。对于10000个整体RRM,目前的估计数如下。(1)原始峰值性能为每秒576万亿次操作。(2)对于一般符号应用,集成太阳相对加速大约为6.7,而在88%效率的虫洞网络下,RRM性能的理想太阳相对加速为59000。(3)对于高度规则的符号应用(以排序问题为典型例子),集成性能的太阳相对加速为127,RRM性能的效率估计超过80%(相对于集群性能),产生的太阳相对加速超过91。(4)对于收缩应用(以二维流体流动问题为典型例子),集成性能的太阳相对加速为400-670,集群级性能的效率为82%,在实践中应该可以实现。
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