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Scaling GPU-to-CPU Migration for Efficient Distributed Execution on CPU Clusters
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DOI：10.1145/3774934.3786435.png)
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Abstract
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The growing demand for GPU resources has led to wide-spread shortages in data centers, prompting the exploration of CPUs as an alternative for executing GPU programs. While prior research supports executing GPU programs on single CPUs, these approaches struggle to achieve competitive performance due to the computational capacity gap between GPUs and CPUs. To further improve performance, we introduce CuCC, a framework that scales GPU-to-CPU migration to CPU clusters and utilizes distributed CPU nodes to execute GPU programs. Compared to single-CPU execution, CPU cluster execution requires cross-node communication to maintain data consistency. We present the CuCC execution workflow and communication optimizations, which aim to reduce network overhead. Evaluations demonstrate that CuCC achieves high scalability on large-scale CPU clusters and delivers runtimes approaching those of GPUs. In terms of cluster-wide throughput, CuCC enables CPUs to achieve an average of 2.59x higher through-put than GPUs.
Keywords:
compiler optimization
GPU-to-CPU migration
CPU cluster
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Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimizationIF0
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