The I/O bottleneck in high-performance computing is becoming worse as application data continues to grow. In this work, we explore how patterns of I/O within these applications can significantly affect the effectiveness of the underlying storage systems and how these same patterns can be utilized to improve many aspects of the I/O stack and mitigate the I/O bottleneck. We offer three main contributions in this paper. First, we develop and evaluate algorithms by which I/O patterns can be efficiently discovered and described. Second, we implement one such algorithm to reduce the metadata quantity in a virtual parallel file system by up to several orders of magnitude, thereby increasing the performance of writes and reads by up to 40 and 480 percent respectively. Third, we build a prototype file system with pattern-aware prefetching and evaluate it to show a 46 percent reduction in I/O latency. Finally, we believe that efficient pattern discovery and description, coupled with the observed predictability of complex patterns within many high-performance applications, offers significant potential to enable many additional I/O optimizations.
There yet exist no truly parallel file systems. Those that make the claim fall short when it comes to providing adequate concurrent write performance at large scale. This limitation causes large usability headaches in HPC.Users need two major capabilities missing from current parallel file systems. One, they need low latency interactivity. Two, they need high bandwidth for large parallel IO; this capability must be resistant to IO patterns and should not require tuning. There are no existing parallel file systems which provide these features. Frighteningly, exascale renders these features even less attainable from currently available parallel file systems. Fortunately, there is a path forward.
The I/O bottleneck in high-performance computing is becoming worse as application data continues to grow. In this work, we explore how patterns of I/O within these applications can significantly affect the effectiveness of the underlying storage systems and how these same patterns can be utilized to improve many aspects of the I/O stack and mitigate the I/O bottleneck. We offer three main contributions in this paper. First, we develop and evaluate algorithms by which I/O patterns can be efficiently discovered and described. Second, we implement one such algorithm to reduce the metadata quantity in a virtual parallel file system by up to several orders of magnitude, thereby increasing the performance of writes and reads by up to 40 and 480 percent respectively. Third, we build a prototype file system with pattern-aware prefetching and evaluate it to show a 46 percent reduction in I/O latency. Finally, we believe that efficient pattern discovery and description, coupled with the observed predictability of complex patterns within many high-performance applications, offers significant potential to enable many additional I/O optimizations.
HDF5 is a data model, library and file format for storing and managing data. It is designed for flexible and efficient I/O for high volume and complex data. Natively, it uses a single-file format where multiple HDF5 objects are stored in a single file. In a parallel HDF5 application, multiple processes access a single file, thereby resulting in a performance bottleneck in I/O. Additionally, a single-file format does not allow semantic post processing on individual objects outside the scope of the HDF5 application. We have developed a new plugin for HDF5 using its Virtual Object Layer that serves two purposes: 1) it uses PLFS to convert the single-file layout into a data layout that is optimized for the underlying file system, and 2) it stores data in a unique way that enables semantic post-processing on data. We measure the performance of the plugin and discuss work leveraging the new semantic post-processing functionality enabled. We further discuss the applicability of this approach for exascale burst buffer storage systems.
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