La Superba: Leveraging a Self-Comparison Method to Understand the Performance Benefits of Sparse Acceleration Optimizations

Evaluation of modern sparse accelerators is essential for enhancing their performance and guiding future research. However, modern fine-grained assessments of individual optimizations within these accelerators remain challenging and often lead to inconsistencies between proposals. To address this issue, we introduce La Superba, an optimization-granular analysis of prior studies using a self-comparison technique. La Superba expands existing approaches for analyzing constituent parts of sparse accelerators and introduces a set of categorizations for these optimizations. Our method involves isolating each optimization by systematically removing it from the accelerator and comparing the modified accelerator to its original configuration. This technique is applied to optimizations within three state-of-the-art sparse matrix multiplication accelerators, identifying and examining their individual impacts as well as categorizing them to uncover broader patterns in the performance benefits they offer. Through targeted case studies, we demonstrate the performance effects of some of our chosen optimizations, providing deeper insights into their contributions than those detailed in the original studies. We conclude the paper by discussing overarching patterns in optimization structures, utilizing our categorization as a foundation. By establishing a structured framework for evaluation, this work not only clarifies the contributions of specific optimizations but also provides a valuable tool for future designers and researchers to systematically assess and innovate within the sparse accelerator design space.