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Introducing Prefetching and Data Compression to Accelerate Checkpointing for Inverse Seismic Problems
SessionPoster Reception
DescriptionRemote Time Migration (RTM) poses substantial computational challenges, demanding large memory and extended processing times. Our RTM implementation processes three-dimensional fields on multiple NVIDIA GPUs using the Revolve algorithm for checkpointing. However, transferring data between the host and GPU memory introduces a bottleneck.
We introduced a checkpoint prefetching mechanism to overcome this, anticipating memory transfers from host to GPU. Additionally, we integrated GPU data compression using the cuZFP library to reduce data transfer sizes further. The experimental results demonstrated significant performance improvements, achieving a speedup of 1.98x - 2.53x in our benchmark dataset. Prefetching + compression techniques together could reduce host-to-GPU memory transfers by up to 16x.
We introduced a checkpoint prefetching mechanism to overcome this, anticipating memory transfers from host to GPU. Additionally, we integrated GPU data compression using the cuZFP library to reduce data transfer sizes further. The experimental results demonstrated significant performance improvements, achieving a speedup of 1.98x - 2.53x in our benchmark dataset. Prefetching + compression techniques together could reduce host-to-GPU memory transfers by up to 16x.
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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