Leveraging the accelerated processing units for seismic imaging: A performance and power efficiency comparison against CPUs and GPUs

Author:

Said Issam1,Fortin Pierre1,Lamotte Jean–Luc1,Calandra Henri2

Affiliation:

1. Sorbonne Universités, Paris, France

2. Total E&P, Houston, USA

Abstract

Oil and gas companies rely on high performance computing to process seismic imaging algorithms such as reverse time migration. Graphics processing units are used to accelerate reverse time migration, but these deployments suffer from limitations such as the lack of high graphics processing unit memory capacity, frequent CPU-GPU communications that may be bottlenecked by the PCI bus transfer rate, and high power consumptions. Recently, AMD has launched the Accelerated Processing Unit (APU): a processor that merges a CPU and a graphics processing unit on the same die featuring a unified CPU-GPU memory. In this paper, we explore how efficiently may the APU be applicable to reverse time migration. Using OpenCL (along with MPI and OpenMP), a CPU/APU/GPU comparative study is conducted on a single node for the 3D acoustic reverse time migration, and then extended on up to 16 nodes. We show the relevance of overlapping the I/O and MPI communications with the computations for the APU and graphics processing unit clusters, that performance results of APUs range between those of CPUs and those of graphics processing units, and that the APU power efficiency is greater than or equal to the graphics processing unit one.

Publisher

SAGE Publications

Subject

Hardware and Architecture,Theoretical Computer Science,Software

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Applying Non-Local Means Filter on Seismic Exploration;Computer Systems Science and Engineering;2022

2. Tuning seismic imaging workflows for GPU supercomputer performance at scale;SEG Technical Program Expanded Abstracts 2020;2020-09-30

3. Optimizing the parameters of the Lustre-file-system-based HPC system for reverse time migration;The Journal of Supercomputing;2019-10-26

4. Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors;CCF Transactions on High Performance Computing;2019-06-12

5. Dual tree traversal on integrated GPUs for astrophysical N-body simulations;The International Journal of High Performance Computing Applications;2019-04-15

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