Context: The increasing demand for high-performance computing has established General-Purpose Graphics Processing Units () as a cornerstone of modern parallelism, successfully circumventing the scalability challenges presented by Amdahl’s Law. However, efficiently translating hardware potential into realized performance requires specialized programming knowledge. This paper addresses the gap between architectural capabilities and accessible, strategic programming methodologies. Methods: We conducted a systematic review and conceptual synthesis of GPGPU programming strategies, categorizing them into -centric (data locality, coalescing) and -centric (parallel decomposition, divergence minimization) paradigms. The analysis links these strategies directly to fundamental architectural primitives, such as the memory hierarchy and Streaming Multiprocessors. A comparative analysis is introduced, contrasting the GPGPU's throughput-centric design with the latency-centric architecture of many-core x86 systems. Performance implications are discussed through the lens of established optimization principles. Results: Strategic programming decisions—particularly those concerning the effective utilization of and the minimization of —are demonstrated to be the dominant factors in performance scaling. Techniques like parallel reduction and algorithmic auto-tuning consistently yield order-of-magnitude improvements over naive implementations. The efficacy of these strategies is shown to be critically dependent on evolving architectural features, necessitating a fundamental understanding of the core architectural divergence from traditional computing. Conclusion: Maximizing the potential of GPGPU computing hinges on the developer’s ability to implement. While high-level tools are emerging, the immediate future of extreme performance lies in a rigorous, strategic approach to code design. Future research must focus on simplifying this complexity through {ML -driven auto-tuning and standardized higher-level abstraction models.
Volkov et al. (2025) studied this question.