Practical Prefetching Techniques for Multiprocessor File Systems

Improvements in the processing speed of multiprocessors are outpacing improvements in the speed of disk hardware. Parallel disk I/O subsystems have been proposed as one way to close the gap between processor and disk speeds. In a previous paper we showed that prefetching and caching have the potential to deliver the performance benefits of parallel file systems to parallel applications. In this paper we describe experiments with practical prefetching policies that base decisions only on on-line reference history, and that can be implemented efficiently. We also test the ability of these policies across a range of architectural parameters

Practical Prefetching Techniques for Multiprocessor File Systems

Improvements in the processing speed of multiprocessors are outpacing improvements in the speed of disk hardware. Parallel disk I/O subsystems have been proposed as one way to close the gap between processor and disk speeds. In a previous paper we showed that prefetching and caching have the potential to deliver the performance benefits of parallel file systems to parallel applications. In this paper we describe experiments with practical prefetching policies that base decisions only on on-line reference history, and that can be implemented efficiently. We also test the ability of these policies across a range of architectural parameters

Metadata And Data Management In High Performance File And Storage Systems

With the advent of emerging e-Science applications, today\u27s scientific research increasingly relies on petascale-and-beyond computing over large data sets of the same magnitude. While the computational power of supercomputers has recently entered the era of petascale, the performance of their storage system is far lagged behind by many orders of magnitude. This places an imperative demand on revolutionizing their underlying I/O systems, on which the management of both metadata and data is deemed to have significant performance implications. Prefetching/caching and data locality awareness optimizations, as conventional and effective management techniques for metadata and data I/O performance enhancement, still play their crucial roles in current parallel and distributed file systems. In this study, we examine the limitations of existing prefetching/caching techniques and explore the untapped potentials of data locality optimization techniques in the new era of petascale computing. For metadata I/O access, we propose a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata serversan arrangement envisioned necessary for petabyte scale distributed storage systems. For data I/O access, we design and implement Segment-structured On-disk data Grouping and Prefetching (SOGP), a combined prefetching and data placement technique to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. One high-performance local I/O software package in SOGP work for Parallel Virtual File System in the number of about 2000 C lines was released to Argonne National Laboratory in 2007 for potential integration into the production mode

High-performance data-parallel input/output

Existing parallel file systems are proving inadequate in two important arenas: programmability and performance. Both of these inadequacies can largely be traced to the fact that nearly all parallel file systems evolved from Unix and rely on a Unix-oriented, single-stream, block-at-a-time approach to file I/O. This one-size-fits-all approach to parallel file systems is inadequate for supporting applications running on distributed-memory parallel computers. This research provides a migration path away from the traditional approaches to parallel I/O at two levels. At the level seen by the programmer, we show how file operations can be closely integrated with the semantics of a parallel language. Principles for this integration are illustrated in their application to C*, a virtual-processor- oriented language. The result is that traditional C file operations with familiar semantics can be used in C* where the programmer works--at the virtual processor level. To facilitate high performance within this framework, machine-independent modes are used. Modes change the performance of file operations, not their semantics, so programmers need not use ambiguous operations found in many parallel file systems. An automatic mode detection technique is presented that saves the programmer from extra syntax and low-level file system details. This mode detection system ensures that the most commonly encountered file operations are performed using high-performance modes. While the high-performance modes allow fast collective movement of file data, they must include optimizations for redistribution of file data, a common operation in production scientific code. This need is addressed at the file system level, where we provide enhancements to Disk-Directed I/O for redistributing file data. Two enhancements are geared to speeding fine-grained redistributions. One uses a two-phase, or indirect, approach to redistributing data among compute nodes. The other relies on I/O nodes to guide the redistribution by building packets bound for compute nodes. We model the performance of these enhancements and determine the key parameters determining when each approach should be used. Finally, we introduce the notion of collective prefetching and identify its performance benefits and implementation tradeoffs