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HPC Global File System Performance Analysis Using A Scientific-Application Derived Benchmark

  • Author(s): Borrill, Julian
  • et al.
Abstract

With the exponential growth of high-fidelity sensor and simulated data, the scientific community is increasingly reliant on ultrascale HPC resources to handle its data analysis requirements. However, to use such extreme computing power effectively, the I/O components must be designed in a balanced fashion, as any architectural bottleneck will quickly render the platform intolerably inefficient. To understand I/O performance of data-intensive applications in realistic computational settings, we develop a lightweight, portable benchmark called MADbench2, which is derived directly from a large-scale Cosmic Microwave Background (CMB) data analysis package. Our study represents one of the most comprehensive I/O analyses of modern parallel file systems, examining a broad range of system architectures and configurations, including Lustre on the Cray XT3, XT4, and Intel Itanium2 clusters; GPFS on IBM Power5 and AMD Opteron platforms; a BlueGene/P installation using GPFS and PVFS2 file systems; and CXFS on the SGI Altix\-3700. We present extensive synchronous I/O performance data comparing a number of key parameters including concurrency, POSIX- versus MPI-IO, and unique-versus shared-file accesses, using both the default environment as well as highly-tuned I/O parameters. Finally, we explore the potential of asynchronous I/O and show that only the two of the nine evaluated systems benefited from MPI-2's asynchronous MPI-IO. On those systems, experimental results indicate that the computational intensity required to hide I/O effectively is already close to the practical limit of BLAS3 calculations. Overall, our study quantifies vast differences in performance and functionality of parallel file systems across state-of-the-art platforms -- showing I/O rates that vary up to 75x on the examined architectures -- while providing system designers and computational scientists a lightweight tool for conducting further analysis.

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