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arXiv · 2008.02300

MGPU-TSM: A Multi-GPU System with Truly Shared Memory

Saiful A. Mojumder·Yifan Sun·Leila Delshadtehrani·Yenai Ma·Trinayan Baruah·José L. Abellán·John Kim·David Kaeli·Ajay Joshi
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Abstract

The sizes of GPU applications are rapidly growing. They are exhausting the compute and memory resources of a single GPU, and are demanding the move to multiple GPUs. However, the performance of these applications scales sub-linearly with GPU count because of the overhead of data movement across multiple GPUs. Moreover, a lack of hardware support for coherency exacerbates the problem because a programmer must either replicate the data across GPUs or fetch the remote data using high-overhead off-chip links. To address these problems, we propose a multi-GPU system with truly shared memory (MGPU-TSM), where the main memory is physically shared across all the GPUs. We eliminate remote accesses and avoid data replication using an MGPU-TSM system, which simplifies the memory hierarchy. Our preliminary analysis shows that MGPU-TSM with 4 GPUs performs, on average, 3.9x? better than the current best performing multi-GPU configuration for standard application benchmarks.

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Saiful A. Mojumder, Yifan Sun, Leila Delshadtehrani, Yenai Ma, Trinayan Baruah, José L. Abellán, John Kim, David Kaeli, Ajay Joshi. 2020-08-05. MGPU-TSM: A Multi-GPU System with Truly Shared Memory. https://arxiv.org/abs/2008.02300

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