Autotuning Benchmarking Techniques: A Roofline Model Case Study

TL;DR

This paper introduces an autotuning tool for benchmarking the DGEMM operation using the Roofline model, significantly reducing search time while maintaining accuracy, and demonstrating its effectiveness across various hardware architectures.

Contribution

The paper presents a novel autotuning approach that efficiently finds optimal benchmarking configurations for hardware using confidence intervals and early stopping, improving search speed by up to 116x.

Findings

Achieves up to 116.33x faster benchmarking search time.

Maintains less than 2% error compared to hand-tuned parameters.

Effective across multiple hardware architectures.

Abstract

Peak performance metrics published by vendors often do not correspond to what can be achieved in practice. It is therefore of great interest to do extensive benchmarking on core applications and library routines. Since DGEMM is one of the most used in compute-intensive numerical codes, it is typically highly vendor optimized and of great interest for empirical benchmarks. In this paper we show how to build a novel tool that autotunes the benchmarking process for the Roofline model. Our novel approach can efficiently and reliably find optimal configurations for any target hardware. Results of our tool on a range of hardware architectures and comparisons to theoretical peak performance are included. Our tool autotunes the benchmarks for the target architecture by deciding the optimal parameters through state space reductions and exhaustive search. Our core idea includes calculating the confidence interval using the variance and mean and comparing it against the current optimum solution. We can then terminate the evaluation process early if the confidence interval's maximum is lower than the current optimum solution. This dynamic approach yields a search time improvement of up to 116.33x for the DGEMM benchmarking process compared to a traditional fixed sample-size methodology. Our tool produces the same benchmarking result with an error of less than 2% for each of the optimization techniques we apply, while providing a great reduction in search time. We compare these results against hand-tuned benchmarking parameters. Results from the memory-intensive TRIAD benchmark, and some ideas for future directions are also included.