H5Intent: Autotuning HDF5 With User Intent

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-11-06 DOI:10.1109/TPDS.2024.3492704

Hariharan Devarajan;Gerd Heber;Kathryn Mohror

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引用次数: 0

Abstract

The complexity of data management in HPC systems stems from the diversity in I/O behavior exhibited by new workloads, multistage workflows, and multitiered storage systems. The HDF5 library is a popular interface to interact with storage systems in HPC workloads. The library manages the complexity of diverse I/O behaviors by providing user-level configurations to optimize the I/O for HPC workloads. The HDF5 library exposes hundreds of configuration properties that can be set to alter how HDF5 manages I/O requests for better performance. However, determining which properties to set is quite challenging for users who lack expertise in HDF5 library internals. We propose a paradigm change through our H5Intent software, where users specify the intent of I/O operations and the software can set various HDF5 properties automatically to optimize the I/O behavior. This work demonstrates several use cases where mapping user-defined intents to HDF5 properties can be exploited to optimize I/O. In this study, we make three observations. First, I/O intents can accurately define HDF5 properties while managing conflicts between various properties and improving the I/O performance of microbenchmarks by up to 22×. Second, I/O intents can be efficiently passed to HDF5 with a small footprint of 6.74MB per node for thousands of intents per process. Third, an H5Intent VOL connector can dynamically map I/O intents to HDF5 properties for various I/O behaviors exhibited by our microbenchmark and improve I/O performance by up to 8.8×. Overall, H5Intent software improves the I/O performance of complex large-scale workloads we studied by up to 11×.

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.