Time and Energy Benefits of Using Automatic Optimization Compilers for NPDP Tasks

Abstract

In this article, we analyze the program codes generated automatically using three advanced optimizers: Pluto, Traco, and Dapt, which are specifically tailored for the NPDP benchmark set. This benchmark set comprises ten program loops, predominantly from the field of bioinformatics. The codes exemplify dynamic programming, a challenging task for well-known tools used in program loop optimization. Given the intricacy involved, we opted for three automatic compilers based on the polyhedral model and various loop-tiling strategies. During our evaluation of the code’s performance, we meticulously considered locality and concurrency to accurately estimate time and energy efficiency. Notably, we dedicated significant attention to the latest Dapt compiler, which applies space–time loop tiling to generate highly efficient code for the NPDP benchmark suite loops. By employing the aforementioned optimizers and conducting an in-depth analysis, we aim to demonstrate the effectiveness and potential of automatic transformation techniques in enhancing the performance and energy efficiency of dynamic programming codes.

GPT-Driven Source-to-Source Transformation for Generating Compilable Parallel CUDA Code for Nussinov’s Algorithm

“…Our primary objective in this study is time comparison. However, on the 32-core Intel Xeon processor, we have access to parallel and tiled versions of the Nussinov algorithm through well-known compilers-Pluto, TRACO, and DAPT-from previous publications [38,39]. On the A100 graphics card, boasting 6192 CUDA cores, the code must be scalable and fine-grained to fully utilize its resources.…”