MINIME-GPU
Etem Deniz, Alper Şen · ACM Transactions on Architecture and Code Optimization · 2015
We introduce MINIME-GPU, a novel automated benchmark synthesis framework for graphics processing units (GPUs) that serves to speed up architectural simulation of modern GPU architectures. Our framework captures important characteristics of original GPU applications and generates synthetic GPU benchmarks using the Open Computing Language (OpenCL) library from those applications. To the best of our knowledge, this is the first time synthetic OpenCL benchmarks for GPUs are generated from existing applications. We use several characteristics, including instruction throughput, compute unit occupancy, and memory efficiency, to compare the similarity of original applications and their corresponding synthetic benchmarks. The experimental results show that our synthetic benchmark generation framework is capable of generating synthetic benchmarks that have similar characteristics with the original applications from which they are generated. On average, the similarity (accuracy) is 96% and the speedup is 541 ×. In addition, our synthetic benchmarks use the OpenCL library, which allows us to obtain portable human readable benchmarks as opposed to using assembly-level code, and they are faster and smaller than the original applications from which they are generated. We experimentally validated that our synthetic benchmarks preserve the characteristics of the original applications across different architectures.