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iMLBench: A Machine Learning Benchmark Suite for CPU-GPU Integrated Architectures
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DOI：10.1109/TPDS.2020.3046870.png)
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Abstract
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Utilizing heterogeneous accelerators, especially GPUs, to accelerate machine learning tasks has shown to be a great success in recent years. GPUs bring huge performance improvements to machine learning and greatly promote the widespread adoption of machine learning. However, the discrete CPU-GPU architecture design with high PCIe transmission overhead decreases the GPU computing benefits in machine learning training tasks. To overcome such limitations, hardware vendors release CPU-GPU integrated architectures with shared unified memory. In this article, we design a benchmark suite for machine learning training on CPU-GPU integrated architectures, called iMLBench, covering a wide range of machine learning applications and kernels. We mainly explore two features on integrated architectures: 1) zero-copy, which means that the PCIe overhead has been eliminated for machine learning tasks and 2) co-running, which means that the CPU and the GPU co-run together to process a single machine learning task. Our experimental results on iMLBench show that the integrated architecture brings an average 7.1x performance improvement over the original implementations. Specifically, the zero-copy design brings 4.65x performance improvement, and co-running brings 1.78x improvement. Moreover, integrated architectures exhibit promising results from both performance-per-dollar and energy perspectives, achieving 6.50x performance-price ratio while 4.06x energy efficiency over discrete GPUs. The benchmark is open-sourced at https://github.com/ChenyangZhang-cs/iMLBench.
Keywords:
Computer architecture
Machine learning
Benchmark testing
Graphics processing units
Task analysis
Hardware
Training
Machine learning
benchmark
CPU
GPU
integrated architectures
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