Compiler-Runtime Co-Design for Performance-Portable Gpu Programming on Cpus
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | With the rapid development of Artificial Intelligence and High-Performance Computing, an increasing number of programs are being implemented for GPU execution. However, due to the limited supply and high cost, GPUs are not always readily accessible to all developers. As a result, it is common for developers to wait hours for their jobs to be scheduled on available GPU resources in data centers.On the other hand, CPUs are the most ubiquitous and accessible architecture in data centers. As a result, researchers have proposed executing GPU programs on CPUs. Using CPUs as an alternative backend for GPU programs opens new opportunities for improving hardware utilization and reducing data center costs. Additionally, CPU-based execution enables developers to leverage mature CPU debugging toolkits to debug GPU programs. CPUs can also serve as an accessible educational platform for students who do not have access to GPUs.Although GPU-to-CPU migration has been studied for a long time, it remains an unsolved problem. Specifically, two aspects are critical: coverage and performance. In other words, the goal is to execute as many GPU programs on CPUs as possible, and ensure that the migrated programs efficiently utilize CPU computational resources to achieve high performance.This dissertation presents a compiler-runtime co-design solution for GPU-to-CPU migration that improves both coverage and performance. The proposed solution is end-to-end, accepting CUDA source code and automatically generating CPU executable files without requiring any manual pre- or post-processing.The codebase for the corresponding project is available as open source on GitHub (https://github.com/cupbop/CuPBoP). |
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| ISBN: | 9798263343309 |
| Fuente: | ProQuest Dissertations & Theses Global |