论文标题
Micrograd：用于克隆和应力测试的集中式框架
MicroGrad: A Centralized Framework for Workload Cloning and Stress Testing
论文作者
论文摘要
我们提出了Micrograd，这是一个集中式的自动化框架，能够在面对不断发展的应用程序域时有效地分析复杂现代处理器的功能，限制和敏感性。 Micrograd使用Microprobe，一种灵活的代码生成框架作为后端和基于梯度下降的调谐机制，以有效地使测试用例的演变适合诸如工作量克隆和应力测试之类的任务。 Micrograd可以与各种执行基础架构（例如性能和功率模拟器以及本机硬件）接口。此外，模块化的“抽象工作负载模型”方法可以轻松扩展其以供进一步使用。 在本文中，我们在不同的用例和架构上评估了微电磁体，并展示了微rad在几个调整时期和低资源需求中的不同任务中可以达到超过99 \％精度。我们还观察到，微电磁的准确性比竞争技术高25至30 \％。同时，它比替代机制的计算资源（取决于实施）少1.5倍至2.5倍，或者消耗35至60 \％的计算资源（取决于实施）。总体而言，Micrograd的快速，资源效率和准确的测试案例生成能力使其可以对复杂处理器进行快速评估。
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.
