MicroGrad: A Centralized Framework for Workload Cloning and Stress Testing

TL;DR

MicroGrad is an automated, flexible framework that efficiently generates workload test cases for modern processors, achieving high accuracy and resource efficiency in stress testing and workload cloning tasks.

Contribution

It introduces MicroGrad, a novel framework combining code generation and gradient descent tuning for rapid, accurate workload cloning and stress testing of processors.

Findings

Achieves over 99% accuracy in workload generation

Outperforms competing techniques by 25-30% in accuracy

Reduces resource consumption by 35-60%

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 and 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. In this paper, we evaluate MicroGrad over different use cases and architectures and showcase that MicroGrad can achieve greater than 99\% accuracy across different tasks within few tuning epochs and low resource requirements. We also observe that MicroGrad's accuracy is 25 to 30\% higher than competing techniques. At the same time, it is 1.5x to 2.5x faster or would consume 35 to 60\% less compute resources (depending on implementation) over alternate mechanisms. Overall, MicroGrad's fast, resource efficient and accurate test case generation capability allow it to perform rapid evaluation of complex processors.