Paper Infomation
Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance Computing
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Author: Shuntao Tang, Wei Chen
Abstract: This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models, pivotal for advancing high-performance computing (HPC). Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units, it delves into the nuanced optimization of memory access, thread management, algorithmic design, and data structures. These optimizations are critical for exploiting the parallel processing capabilities of GPUs, addressing both the theoretical frameworks and practical implementations. By integrating advanced strategies such as memory coalescing, dynamic scheduling, and parallel algorithmic transformations, this research aims to significantly elevate computational efficiency and throughput. The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains, highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments. The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers, fostering advancements in computational sciences and technology.
Keywords: Optimization Techniques, GPU-Based, Parallel Programming Models, High-Performance Computing
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