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Accelerating Collective Communications with Lossy Compression on GPU
SessionPoster Reception
DescriptionGPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. To address this issue, traditional approaches integrate lossy compression directly into GPU-aware collectives, which still suffer from serious issues such as underutilized GPU devices and uncontrolled data distortion.
In this poster, we propose GPU-LCC, a general framework that designs and optimizes GPU-aware, compression-enabled collectives with well-controlled error propagation. To validate our framework, we evaluate the performance on up to 512 NVIDIA A100 GPUs with real-world applications and datasets. Experimental results demonstrate that our GPU-LCC-accelerated collective computation (Allreduce), can outperform NCCL as well as Cray MPI by up to 4.5X and 20.2X, respectively. Furthermore, our accuracy evaluation with an image-stacking application confirms the high reconstructed data quality of our accuracy-aware framework.
In this poster, we propose GPU-LCC, a general framework that designs and optimizes GPU-aware, compression-enabled collectives with well-controlled error propagation. To validate our framework, we evaluate the performance on up to 512 NVIDIA A100 GPUs with real-world applications and datasets. Experimental results demonstrate that our GPU-LCC-accelerated collective computation (Allreduce), can outperform NCCL as well as Cray MPI by up to 4.5X and 20.2X, respectively. Furthermore, our accuracy evaluation with an image-stacking application confirms the high reconstructed data quality of our accuracy-aware framework.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
LocationDEF Concourse
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