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.
@article{arxiv.2008.02300,
title = {MGPU-TSM: A Multi-GPU System with Truly Shared Memory},
author = {Saiful A. Mojumder and Yifan Sun and Leila Delshadtehrani and Yenai Ma and Trinayan Baruah and José L. Abellán and John Kim and David Kaeli and Ajay Joshi},
journal= {arXiv preprint arXiv:2008.02300},
year = {2020}
}