MicroGrad: A Centralized Framework for Workload Cloning and Stress Testing

Abstract

We present MicroGrad, a centralized automated framework that is able to efficiently analyze the capabilities, limits and sensitivities of complex modern processors in the face of constantly evolving application domains. MicroGrad uses Microprobe, a flexible code generation framework as its back-end and a Gradient Descent based tuning mechanism to efficiently enable the evolution of the test cases to suit tasks such as Workload Cloning and Stress Testing. MicroGrad can interface with a variety of execution infrastructure such as performance/power simulators as well as native hardware. Further, the modular 'abstract workload model' approach to building MicroGrad allows it to be easily extended for further use-enabling more novel usecases, diverse platforms or robust tuning algorithms. In this paper, we evaluate MicroGrad over different use cases and architectures and showcase that MicroGrad can achieve greater than 95% accuracy across different tasks within few tuning epochs and low resource requirements. We also observe that MicroGrad's accuracy is 25-30% higher than competing techniques. At the same time, MicroGrad is able to reach tuning goals 4-5x faster compared to alternate mechanisms. Overall, MicroGrad's fast, resource efficient and accurate test case generation capability allows it to perform rapid evaluation of complex processors.