评估Imagine流架构

Jung Ho Ahn, W. Dally, Brucek Khailany, U. Kapasi, Abhishek Das
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引用次数: 150

摘要

本文介绍了Imagine流处理器原型的实验评估。Imagine (Kapasi et al., 2002)是一种流处理器,它采用两级寄存器层次结构,具有9.7 kb的本地寄存器文件容量和128 kb的流寄存器文件(SRF)容量,以捕获流应用程序中的生产者-消费者局域性。并行性利用了由48个浮点算术单元组成的数组,这些单元被组织为8个SIMD集群,每个集群有一个6宽的VLIW。我们使用一组综合微基准测试、关键媒体处理内核和完整的应用程序来评估Imagine架构的每个方面的性能。这些微基准测试表明,原型硬件可以达到7.96 GFLOPS或25.4 GOPS的算术性能,12.7 gb /s的SRF带宽,1.58 gb /s的存储系统带宽,每秒从主机处理器接收多达200万条流处理器指令。在一组媒体处理内核上,Imagine平均维持了43%的峰值算术性能。对完整应用程序的评估提供了执行时间在何处花费的细分。在完整的应用程序中,Imagine达到了39.4%的峰值性能,其余平均36.4%的时间损失是由于VLIW集群中算术单元之间的负载不平衡和内核内循环中有限的指令级并行性,10.6%是由于短流长度导致的内核启动和关闭开销,7.6%是由于内存停滞,其余是由于主机处理器带宽不足。本文进一步分析了主机指令带宽对应用程序性能的影响,特别是在较小的数据集上。总之,本文中描述的实验测量证明了流处理的高性能和效率:在200 MHz工作时,Imagine在QR分解时维持4.81 GFLOPS,而功耗为7.42瓦。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating the Imagine stream architecture
This paper describes an experimental evaluation of the prototype Imagine stream processor. Imagine (Kapasi et al., 2002) is a stream processor that employs a two-level register hierarchy with 9.7 Kbytes of local register file capacity and 128 Kbytes of stream register file (SRF) capacity to capture producer-consumer locality in stream applications. Parallelism is exploited using an array of 48 floating-point arithmetic units organized as eight SIMD clusters with a 6-wide VLIW per cluster. We evaluate the performance of each aspect of the Imagine architecture using a set of synthetic micro-benchmarks, key media processing kernels, and full applications. These micro-benchmarks show that the prototype hardware can attain 7.96 GFLOPS or 25.4 GOPS of arithmetic performance, 12.7 Gbytes/s of SRF bandwidth, 1.58 Gbytes/s of memory system bandwidth, and accept up to 2 million stream processor instructions per second from a host processor. On a set of media processing kernels, Imagine sustained an average of 43% of peak arithmetic performance. An evaluation of full applications provides a breakdown of where execution time is spent. Over full applications, Imagine achieves 39.4% of peak performance, of the remainder on average 36.4% of time is lost due to load imbalance between arithmetic units in the VLIW clusters and limited instruction-level parallelism within kernel inner loops, 10.6% is due to kernel startup and shutdown overhead because of short stream lengths, 7.6% is due to memory stalls, and the rest is due to insufficient host processor bandwidth. Further analysis included in the paper presents the impact of host instruction bandwidth on application performance, particularly on smaller datasets. In summary, the experimental measurements described in this paper demonstrate the high performance and efficiency of stream processing: operating at 200 MHz, Imagine sustains 4.81 GFLOPS on QR decomposition while dissipating 7.42 Watts.
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