Many scientific applications that run on today’s multiprocessors, such as weather forecasting and seismic analysis, are bottlenecked by their file-I/O needs. Even if the multiprocessor is configured with sufficient I/O hardware, the file-system software often fails to provide the available bandwidth to the application. Although libraries and enhanced file-system interfaces can make a significant improvement, we believe that fundamental changes are needed in the file-server software. We propose a new technique, disk-directed I/O, to allow the disk servers to determine the flow of data for maximum performance. Our simulations show that tremendous performance gains are possible. Indeed, disk-directed I/O provided consistent high performance that was largely independent of data distribution, obtained up to 93 % of peak disk bandwidth, and was as much as 16 times faster than traditional parallel file systems.

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We discuss the issues involved in implementing MPI-IO portably on multiple machines and file systems and also achieving high performance. One way to implement MPI-IO portably is to implement it on top of the basic Unix I/O functions (open, lseek, read, write, and close), which are themselves portable. We argue that this approach has limitations in both functionality and performance. We instead advocatean implementation approach that combines a large portion of portable code and a small portion of code that is optimized separately for different machines and file systems. We have used such an approach to develop a high-performance, portable MPI-IO implementation, called ROMIO. In addition to basic I/O functionality, we consider the issues of supporting other MPI-IO features, such as 64-bit file sizes, noncontiguous accesses, collective I/O, asynchronous I/O, consistency and atomicity semantics, user-supplied hints, shared file pointers, portable data representation, and file preallocati...

The I/O access patterns of parallel programs often consist of accesses to a large number of small, noncontiguous pieces of data. If an application’s I/O needs are met by making many small, distinct I/O requests, however, the I/O performance degrades drastically. To avoid this problem, MPI-IO allows users to access a noncontiguous data set with a single I/O function call. This feature provides MPI-IO implementations an opportunity to optimize data access. We describe how our MPI-IO implementation, ROMIO, delivers high performance in the presence of noncontiguous requests. We explain in detail the two key optimizations ROMIO performs: data sieving for noncontiguous requests from one process and collective I/O for noncontiguous requests from multiple processes. We describe how one can implement these optimizations portably on multiple machines and file systems, control their memory requirements, and also achieve high performance. We demonstrate the performance and portability with performance results for three applications—an astrophysics-application template (DIST3D), the NAS BTIO benchmark, and an unstructured code (UNSTRUC)—on five different parallel machines:

\We are developing a software system called PASSION: Parallel And Scalable Software for Input-Output which provides software support for high performance parallel I/O. PASSION provides support at the language, compiler, runtime as well as file system level. PASSION provides runtime procedures for parallel access to files (read/write), as well as for out-of-core computations. These routines can either be used together with a compiler to translate out-of-core data parallel programs written in a language like HPF, or used directly by application programmers. A number of optimizations such as Two-Phase Access, Data Sieving, Data Prefetching and Data Reuse have been incorporated in the PASSION Runtime Library for improved performance. PASSION also provides an initial framework for runtime support for out-of-core irregular problems. The goal of the PASSION compiler is to automatically translate out- of-core data parallel programs to node programs for distributed memory machines, with calls to the PASSION Runtime Library. At the language level, PASSION suggests extensions to HPF for out-of-core programs. At the file system level, PASSION provides support for buffering and prefetching data from disks. A portable parallel file system is also being developed as part of this project, which can be used across homogeneous or heterogeneous networks of workstations. PASSION also provides support for integrating data and task parallelism using parallel I/O techniques. We have used PASSION to implement a number of out-of-core applications such as a Laplace's equation solver, 2D FFT, Matrix Multiplication, LU Decomposition, image processing applications as well as unstructured mesh kernels in molecular dynamics and computational fluid dynamics. We are currently in the process of using PASSION in applications in CFD (3D turbulent flows), molecular structure calculations, seismic computations, and earth and space science applications such as Four-Dimensional Data Assimilation. PASSION is currently available on the Intel Paragon, Touchstone Delta and iPSC/860. Efforts are underway to port it to the IBM SP-1 and SP-2 using the Vesta Parallel File System.

In this paper, we propose a strategy for implementing parallel-I/O interfaces portably and efficiently. We have defined an abstract-device interface for parallel I/O, called ADIO. Any parallel-I/O API can be implemented on multiple file systems by implementing the API portably on top of ADIO, and implementing only ADIO on different file systems. This approach simplifies the task of implementing an API and yet exploits the specific high-performance features of individual file systems. We have used ADIO to implement the Intel PFS interface and subsets of MPI-IO and IBM PIOFS interfaces on PFS, PIOFS, Unix, and NFS file systems. Our performance studies indicate that the overhead of using ADIO as an implementation strategy...

The GridFTP extensions to the File Transfer Protocol define a general-purpose mechanism for secure, reliable, high-performance data movement. We report here on the Globus striped GridFTP framework, a set of client and server libraries designed to support the construction of data-intensive tools and applications. We describe the design of both this framework and a striped GridFTP server constructed within the framework. We show that this server is faster than other FTP servers in both single-process and striped configurations, achieving, for example, speeds of 27.3 Gbit/s memory-to-memory and 17 Gbit/s disk-to-disk over a 60 millisecond round trip time, 30 Gbit/s network. In another experiment, we show that the server can support 1800 concurrent clients without excessive load. We argue that this combination of performance and modular structure make the Globus GridFTP framework both a good foundation on which to build tools and applications, and a unique testbed for the study of innovative data management techniques and network protocols. 1

SOLAR is a portable high-performance library for out-of-core dense matrix computations. It combines portability with high performance by using existing high-performance in-core subroutine libraries and by using an optimized matrix input-output library. SOLAR works on parallel computers, workstations, and personal computers. It supports in-core computations on both shared-memory and distributed-memory machines, and its matrix input-output library supports both conventional I/O interfaces and parallel I/O interfaces. This paper discusses the overall design of SOLAR, its interfaces, and the design of several important subroutines. Experimental results show that SOLAR can factor on a single workstation an out-of-core positive-definite symmetric matrix at a rate exceeding 215 Mflops, and an out-of-core general matrix at a rate exceeding 195 Mflops. Less than 16 % of the running time is spent on I/O in these computations. These results indicate that SOLAR's portability does not compromise its performance. We expect that the combination of portability, modularity, and the use of a high-level I/O interface will make the library an important platform for research on out-of-core algorithms and on parallel I/O.

A number of applications on parallel computers deal with very large data sets that cannot fit in the main memory. In such applications, data must be stored in files on disks and fetched into memory during program execution. Parallel programs with large out-of-core arrays stored in files must read/write smaller sections of the arrays from/to files. In this paper, we describe a method for accessing sections of out-of-core arrays efficiently. Our method, the extended two phase method, uses collective I/O: Processors cooperate to combine several I/O requests into fewer larger granularity requests, reorder requests so that the file is accessed in proper sequence, and eliminate simultaneous I/O requests for the same data. In addition, the I/O workload is divided among processors dynamically, depending on the access requests. We present performance results obtained from two real out-of-core parallel applications – matrix multiplication and a Laplace’s equation solver – and several synthetic access patterns, all on the Intel Touchstone Delta. These results indicate that the extended two-phase method significantly outperformed a direct (non-collective) method for accessing out-of-core array sections.

Several algorithms for parallel disk systems have appeared in the literature recently, and they are asymptotically optimal in terms of the number of disk accesses. Scalable systems with parallel disks must be able to run these algorithms. We present for the first time a list of capabilities that must be provided by the system to support these optimal algorithms: control over declustering, querying about the configuration, independent I/O, and turning off parity, file caching, and prefetching. We summarize recent theoretical and empirical work that justifies the need for these capabilities. In addition, we sketch an organization for a parallel file interface with low-level primitives and higher-level operations.