As Linux clusters have matured as platforms for lowcost, high-performance parallel computing, software packages to provide many key services have emerged, especially in areas such as message passing and networking. One area devoid of support, however, has been parallel file systems, which are critical for highperformance I/O on such clusters. We have developed a parallel file system for Linux clusters, called the Parallel Virtual File System (PVFS). PVFS is intended both as a high-performance parallel file system that anyone can download and use and as a tool for pursuing further research in parallel I/O and parallel file systems for Linux clusters. In this paper, we describe the design and implementation of PVFS and present performance results on the Chiba City cluster at Argonne. We provide performance results for a workload of concurrent reads and writes for various numbers of compute nodes, I/O nodes, and I/O request sizes. We also present performance results for MPI-IO on PVFS, b...

1 1 Introduction 2 2 Linking and running programs 2 2.1 Scripts to Compile and Link Applications . . . . . . . . . . . . . . . . . . . 3 2.1.1 Fortran 90 and the MPI module . . . . . . . . . . . . . . . . . . . . 4 2.2 Compiling and Linking without the Scripts . . . . . . . . . . . . . . . . . . 4 2.3 Running with mpirun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3.1 SMP Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3.2 Multiple Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.4 More detailed control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 Special features of different systems 6 3.1 Workstation clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 Checking your machines list . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.2 Using the Secure Shell . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.3 Using the Secure Server . . . . . . . . . . . . . . . . ....

This paper presents an end-to-end, low-cost solution for visualizing time-varying volume data rendered on a parallel computer located at a remote site. Pipelining and careful grouping of processors are used to hide I/O time and to maximize processors utilization. Compression is used to significantly cut down the cost of transferring output images from the parallel computer to a display device through a wide-area network. This complete rendering pipeline makes possible highly efficient rendering and remote viewing of high resolution time-varying data sets in the absence of high-speed network and parallel I/O support. To study the performance of this rendering pipeline and to demonstrate high-performance remote visualization, tests were conducted on a PC cluster in Japan as well as an SGI Origin 2000 operated at the NASA Ames Research Center with the display located at UC Davis. Keywords: High Performance Computing, Image Compression, Parallel Volume Rendering, Pipelining, Remote Visualization, Scientific Visualization, Time-Varying Data, Wide-Area Network 1

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Many scientific applications have large I/O requirements, in terms of both the size of data and the number of files or data sets. Management, storage, efficient access, and analysis of this data present an extremely challenging task. Traditionally, two different solutions are used for this problem: file I/O or databases. File I/O can provide high performance but is tedious to use with large numbers of files and large and complex data sets. Databases can be convenient, flexible, and powerful but do not perform and scale well for parallel supercomputing applications. We have developed a software system, called Scientific Data Manager (SDM), that aims to combine the good features of both file I/O and databases. SDM provides a high-level API to the user and, internally, uses a parallel file system to store real data and a database to store application-related metadata. SDM takes advantage of various I/O optimizations available in MPI-IO, such as collective I/O and noncontiguous requests, in a manner that is transparent to the user. As a result, users can write and retrieve data with the performance of parallel file I/O, without having to bother with the details of actually performing file I/O. In this paper, we describe the design and implementation of SDM. With the help of two parallel application templates, ASTRO3D and an Euler solver, we illustrate how some of the design criteria affect performance. 0-7803-9802-5/2000/$10.00 c 2000 IEEE

Abstract. Formal verification methods based on model checking are applied to analyze the correctness properties of one existing and two new distributed locking protocols implemented using MPI’s one-sided communication. Model checking exposed an overlooked correctness issue with the first of these protocols which was developed relying only on manual reasoning. Model checking helped confirm the basic correctness properties of the two new protocols, while also identifying the remaining problems in them. Our experience is that MPI based programming, especially the tricky and relatively poorly undersood one-sided communication features, stand to gain immensely from model checking. Considering that many other areas of concurrent hardware and software design are now routinely employing model checking, our experience confirms that the MPI community can greatly benefit from the use of formal verification. 1

The Message Passing Interface (MPI) has been extremely successful as a portable way to program high-performance parallel computers.

Parallel computing is integral to high performance computing, but it is not uniquely sufficient. With the adoption of parallel computing, some additional supporting technologies are required. Parallel I/O is one such supporting technology, providing high speed data storage in parallel computing environments. Parallel I/O systems have emerged and are beginning to see use in the main stream; however, research into optimizing these systems is still an open area. In particular, techniques for optimizing parallel I/O have focused on disk performance optimization when other resources might have equal or greater impact on overall performance. Other work has looked at adaptive techniques for optimizing in these systems, but has focused on caching and prefetching only.

Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable.

1 Introduction Often, processes must acquire exclusive access to a range of bytes. One appli-cation of byte-range locks is to implement the atomic mode of access defined in MPI-IO, the I/O interface that is part of MPI-2 [7]. MPI-IO, by default,supports weak consistency semantics in which the outcome of concurrent overlapping writes from multiple processes to a common file is undefined. The user,however, can optionally select stronger consistency semantics on a per file basis by calling the function MPI File set atomicity with flag=true. In this mode,called the atomic mode, if two processes associated with the same open file write concurrently to overlapping regions of the file, the result is the data written byeither one process or the other, and nothing in between. In order to implement the atomic mode, either the underlying file systemmust provide functions that guarantee atomicity, or the MPI-IO implementation must ensure that a process has exclusive access to the portion of the file itneeds to access [13]. Many POSIX-compatible file systems support atomicity for contiguous reads and writes, such as those issued by a single read or writefunction call, but some high-performance parallel file systems, such as PVFS [1]