Throughput Optimization with a NUMA-Aware Runtime System for Efficient Scientific Data Streaming
Pages 795 - 805
Abstract
With the surge in data generation rates from advanced scientific instruments, there is an urgent need for effective network management and resource utilization strategies for data streaming. Present strategies often lag behind hardware advancements, leading to resource underutilization. Modern servers typically employ non-uniform memory access (NUMA) multiprocessors, which, despite their benefits, can pose performance challenges. This paper presents a novel runtime system tailored for efficient multi-stream data management, optimizing both its compression and decompression phases, and enhancing network I/O based on the server’s unique hardware design. Our system coordinates parallel tasks for data compression, decompression, and transfer, aiming to reduce network data influx. Empirical tests show that aligning streaming tasks with the right NUMA domain results in a 1.48X throughput boost compared to cutting-edge methods and a 2.6X improvement over standard techniques.
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- Throughput Optimization with a NUMA-Aware Runtime System for Efficient Scientific Data Streaming
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November 2023
2180 pages
ISBN:9798400707858
DOI:10.1145/3624062
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Published: 12 November 2023
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SC-W 2023
SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
November 12 - 17, 2023
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