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Title: Institutional Computing Parallel File Systems

 [1];  [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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Resource Relation:
Conference: Institutional Computing Users Group ; 2015-05-14 - 2015-05-14 ; Los Alamos, New Mexico, United States
Country of Publication:
United States
Mathematics & Computing(97); Computer Science

Citation Formats

Torrez, Alfred, and Kettering, Brett Michael. Institutional Computing Parallel File Systems. United States: N. p., 2015. Web.
Torrez, Alfred, & Kettering, Brett Michael. Institutional Computing Parallel File Systems. United States.
Torrez, Alfred, and Kettering, Brett Michael. 2015. "Institutional Computing Parallel File Systems". United States. doi:.
title = {Institutional Computing Parallel File Systems},
author = {Torrez, Alfred and Kettering, Brett Michael},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 5

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  • Parallel I/O plays an increasingly important role in today's data intensive computing applications. While much attention has been paid to parallel read performance, most of this work has focused on the parallel file system, middleware, or application layers, ignoring the potential for improvement through more effective use of local storage. In this paper, we present the design and implementation of segment-structured on-disk data grouping and prefetching (SOGP), a technique that leverages additional local storage to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. Parallel virtual file system (PVFS) ismore » chosen as an example. Our experiments show that an SOGP-enhanced PVFS prototype system can outperform a traditional Linux-Ext3-based PVFS for many applications and benchmarks, in some tests by as much as 230% in terms of I/O bandwidth.« less
  • Livermore Computing is an early and aggressive adopter of parallel file systems including, for example, GPFS from IBM and Lustre for our present Linux systems. As such, we have acquired more than our share of battle scars from encountering bugs in 'bleeding edge' file systems that we have pressed into production to serve our customers' massive I/O requirements. A major role of the Scalable I/O Project is to detect errors before our end users do. In order to do this, we have developed highly parallel test codes to stress and probe potentially weak areas of file system behavior. This papermore » describes those test programs and how we make use of them.« less
  • Diskless high-performance computing (HPC) systems utilizing networked storage have become popular in the last several years. Removing disk drives significantly increases compute node reliability as they are known to be a major source of failures. Furthermore, networked storage solutions utilizing parallel I/O and replication are able to provide increased scalability and availability. Reducing a compute node to processor(s), memory and network interface(s) greatly reduces its physical size, which in turn allows for large-scale dense HPC solutions. However, one major obstacle is the requirement by certain operating systems (OSs), such as Linux, for a root file system. While one solution ismore » to remove this requirement from the OS, another is to share the root file system over the networked storage. This paper evaluates three networked file system solutions, NFSv4, Lustre and PVFS2, with respect to their performance, scalability, and availability features for servicing a common root file system in a diskless HPC configuration. Our findings indicate that Lustre is a viable solution as it meets both, scaling and performance requirements. However, certain availability issues regarding single points of failure and control need to be considered.« less
  • Parallel scientific applications store and retrieve very large, structured datasets. Directly supporting these structured accesses is an important step in providing high-performance I/O solutions for these applications. High-level interfaces such as HDF5 and Parallel netCDF provide convenient APIs for accessing structured datasets, and the MPI-IO interface also supports efficient access to structured data. However, parallel ?le systems do not traditionally support such access. In this work we present an implementation of structured data access support in the context of the Parallel Virtual File System (PVFS). We call this support 'datatype I/O' because of its similarity to MPI datatypes. This supportmore » is built by using a reusable datatype-processing component from the MPICH2 MPI implementation. We describe how this component is leveraged to efficiently process structured data representations resulting from MPI-IO operations. We quantitatively assess the solution using three test applications. We also point to further optimizations in the processing path that could be leveraged for even more efficient operation.« less