NDLI: LogCA: A High-Level Performance Model for Hardware Accelerators

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LogCA: A High-Level Performance Model for Hardware Accelerators

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LogCA: A High-Level Performance Model for Hardware Accelerators

Content Provider	ACM Digital Library
Author	Wood, David A. Altaf, Muhammad Shoaib Bin
Abstract	With the end of Dennard scaling, architects have increasingly turned to special-purpose hardware accelerators to improve the performance and energy efficiency for some applications. Unfortunately, accelerators don't always live up to their expectations and may under-perform in some situations. Understanding the factors which effect the performance of an accelerator is crucial for both architects and programmers early in the design stage. Detailed models can be highly accurate, but often require low-level details which are not available until late in the design cycle. In contrast, simple analytical models can provide useful insights by abstracting away low-level system details. In this paper, we propose LogCA---a high-level performance model for hardware accelerators. LogCA helps both programmers and architects identify performance bounds and design bottlenecks early in the design cycle, and provide insight into which optimizations may alleviate these bottlenecks. We validate our model across a variety of kernels, ranging from sub-linear to super-linear complexities on both on-chip and off-chip accelerators. We also describe the utility of LogCA using two retrospective case studies. First, we discuss the evolution of interface design in SUN/Oracle's encryption accelerators. Second, we discuss the evolution of memory interface design in three different GPU architectures. In both cases, we show that the adopted design optimizations for these machines are similar to LogCA's suggested optimizations. We argue that architects and programmers can use insights from these retrospective studies for improving future designs.
Starting Page	375
Ending Page	388
Page Count	14
File Format	PDF
ISBN	9781450348928
DOI	10.1145/3079856.3080216
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Accelerators Analytical modeling Heterogenous architectures Performance
Content Type	Text
Resource Type	Article

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