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Optimizing memory access Efficiency in cuda Kernel via Data Layout Technique

Journal of Computer and Communications 2024年第5期12卷 124-139页

作者： Neda Seifi Abdullah Al-Mamun Department of Computer & Cyber Sciences&#8212 SCCS Augusta University Augusta Georgia USA

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for cuda programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for cuda programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

关键词： Data Layout optimization cuda Performance optimization GPU memory optimization Dynamic Programming Matrix Multiplication memory access pattern optimization in cuda

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