Snehasish Kumar
ABSTRACT
There exist a multitude of execution models available today for a developer to target. The choices vary from general purpose processors to fixed-function hardware accelerators with a large number of variations in-between. There is a growing demand to assess the potential benefits of porting or rewriting an application to a target architecture in order to fully exploit the benefits of performance and/or energy efficiency offered by such targets. However, as a first step of this process, it is necessary to determine whether the application has characteristics suitable for acceleration.
In this paper, we present Peruse, a tool to characterize the features of loops in an application and to help the programmer understand the amenability of loops for acceleration. We consider a diverse set of features ranging from loop characteristics (e.g., loop exit points) and operation mixes (e.g., control vs data operations) to wider code region characteristics (e.g., idempotency, vectorizability). Peruse is language, architecture, and input independent and uses the intermediate representation of compilers to do the characterization. Using static analyses makes Peruse scalable and enables analysis of large applications to identify and extract interesting loops suitable for acceleration. We show analysis results for unmodified applications from the SPEC CPU benchmark suite, Polybench, and HPC workloads.
For an end-user it is more desirable to get an estimate of the potential speedup due to acceleration. We use the workload characterization results of Peruse as features and develop a machine-learning based model to predict the potential speedup of a loop when off-loaded to a fixed function hardware accelerator. We use the model to predict the speedup of loops selected by Peruse and achieve an accuracy of 79%.
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