Benchmark synthesis for architecture and compiler exploration

This paper presents a novel benchmark synthesis framework with three key features. First, it generates synthetic benchmarks in a high-level programming language (C in our case), in contrast to prior work in benchmark synthesis which generates synthetic benchmarks in assembly. Second, the synthetic benchmarks hide proprietary information from the original workloads they are built after. Hence, companies may want to distribute synthetic benchmark clones to third parties as proxies for their proprietary codes; third parties can then optimize the target system without having access to the original codes. Third, the synthetic benchmarks are shorter running than the original workloads they are modeled after, yet they are representative. In summary, the proposed framework generates small (thus quick to simulate) and representative benchmarks that can serve as proxies for other workloads without revealing proprietary information; and because the benchmarks are generated in a high-level programming language, they can be used to explore both the architecture and compiler spaces. The results obtained with our initial framework are promising. We demonstrate that we can generate synthetic proxy benchmarks for the MiBench benchmarks, and we show that they are representative across a range of machines with different instruction-set architectures, microarchitectures, and compilers and optimization levels, while being 30 times shorter running on average. We also verify using software plagiarism detection tools that the synthetic benchmark clones hide proprietary information from the original workloads.This paper presents a novel benchmark synthesis framework with three key features. First, it generates synthetic benchmarks in a high-level programming language (C in our case), in contrast to prior work in benchmark synthesis which generates synthetic benchmarks in assembly. Second, the synthetic benchmarks hide proprietary information from the original workloads they are built after. Hence, companies may want to distribute synthetic benchmark clones to third parties as proxies for their proprietary codes; third parties can then optimize the target system without having access to the original codes. Third, the synthetic benchmarks are shorter running than the original workloads they are modeled after, yet they are representative. In summary, the proposed framework generates small (thus quick to simulate) and representative benchmarks that can serve as proxies for other workloads without revealing proprietary information; and because the benchmarks are generated in a high-level programming language, they can be used to explore both the architecture and compiler spaces. The results obtained with our initial framework are promising. We demonstrate that we can generate synthetic proxy benchmarks for the MiBench benchmarks, and we show that they are representative across a range of machines with different instruction-set architectures, microarchitectures, and compilers and optimization levels, while being 30 times shorter running on average. We also verify using software plagiarism detection tools that the synthetic benchmark clones hide proprietary information from the original workloads.C