Part 1 Introduction: heterogeneous network computing trends in distributed computing PVM overview other packages. Part 2 The PVM system. Part 3 Using PVM: how to obtain the PVM software setup to use PVM setup summary starting PVM common startup problems running PVM programs PVM console details host file options. Part 4 Basic programming techniques: common parallel programming paradigms workload allocation porting existing applications to PVM. Part 5 PVM user interface: process control information dynamic configuration signalling setting and getting options message passing dynamic process groups. Part 6 Program examples: fork-join dot product failure matrix multiply one-dimensional heat equation. Part 7 How PVM works: components messages PVM daemon libpvm library protocols message routing task environment console program resource limitations multiprocessor systems. Part 8 Advanced topics: XPVM porting PVM to new architectures. Part 9 Troubleshooting: geting PVM installed getting PVM running compiling applications running applications debugging and tracing debugging the system. Appendices: history of PVM versions PVM 3 routines.

PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing

I. Parallelism 1. Introduction 2. Parallel Computer Architectures 3. Parallel Programming Considerations II. Applications 4. General Application Issues 5. Parallel Computing in CFD 6. Parallel Computing in Environment and Energy 7. Parallel Computational Chemistry 8. Application Overviews III. Software technologies 9. Software Technologies 10. Message Passing and Threads 11. Parallel I/O 12. Languages and Compilers 13. Parallel Object-Oriented Libraries 14. Problem-Solving Environments 15. Tools for Performance Tuning and Debugging 16. The 2-D Poisson Problem IV. Enabling Technologies and Algorithms 17. Reusable Software and Algorithms 18. Graph Partitioning for Scientific Simulations 19. Mesh Generation 20. Templates and Numerical Linear Algebra 21. Software for the Scalable Solutions of PDEs 22. Parallel Continuous Optimization 23. Path Following in Scientific Computing 24. Automatic Differentiation V. Conclusion 25. Wrap-up and Features

Sourcebook of parallel computing

Developers of application codes for massively parallel computer systems face daunting performance tuning and optimization problems that must be solved if massively parallel systems are to fulfill their promise. Recording and analyzing the dynamics of application program, system software, and hardware interactions is the key to understanding and the prerequisite to performance tuning, but this instrumentation and analysis must not unduly perturb program execution. Pablo is a performance analysis environment designed to provide unobtrusive performance data capture, analysis, and presentation across a wide variety of scalable parallel systems. Current efforts include dynamic statistical clustering to reduce the volume of data that must be captured and complete performance data immersion via head-mounted displays. >

Scalable performance analysis: the Pablo performance analysis environment

Rapid increases in computing and communication performance are exacerbating the long-standing problem of performance-limited input/output. Indeed, for many otherwise scalable parallel applications. input/output is emerging as a major performance bottleneck. The design of scalable input/output systems depends critically on the input/output requirements and access patterns for this emerging class of large-scale parallel applications. However, hard data on the behavior of such applications is only now becoming available. In this paper, we describe the input-output requirements of three scalable parallel applications (electron scattering, terrain rendering, and quantum chemistry, on the Intel Paragon XP/S. As part of an ongoing parallel input/output characterization effort, we used instrumented versions of the application codes to capture and analyze input/output volume, request size distributions, and temporal request structure. Because complete traces of individual application input/output requests were captured, in-depth, off-line analyses were possible. In addition, we conducted informal interviews of the application developers to understand the relation between the codes' current and desired input/output structure. The results of our studies show a wide variety of temporal and spatial access patterns, including highly read-intensive and write-intensive phases, extremely large and extremely small request sizes, and both sequential and highly irregular access patterns. We conclude with a discussion of the broad spectrum of access patterns and their profound implications for parallel file caching and prefetching schemes.

Input/Output Characteristics of Scalable Parallel Applications

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Goldfish acclimated either to 5 °C or to 25 °C were transferred to the opposite temperature and the changes in behavioral resistance to high temperature, the fluidity and fatty acid composition of isolated synaptosomal membranes were followed during acclimation to the new temperature with the purpose of establishing some correlation.




2.

In 25 °C-acclimated goldfish, hyperexcitability was induced at 34.5 °C, loss of equilibrium at 37.6 °C and coma at 39.0 °C. In 5 °C-acclimated goldfish the corresponding temperatures were 29.2 °C, 32.0 °C and 33.0 °C. Time to attain 75% of the final acclimated state after transfer was approximately 4 days at 25 °C and 28 days at 5 °C.




3.

Fluidity of synaptosomal membranes isolated from goldfish brains was estimated by use of the fluorescence polarization technique. Membrane viscosity decreased during acclimation to 5 °C, but increased during acclimation to 25 °C. The early stages of the transitions differed in time course from behavioral resistance acclimation but times to reach the new acclimated state were similar.




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Fatty acid composition of synaptosomal phospholipids showed increased unsaturation during cold-acclimation and decreased unsaturation during warm-acclimation.




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It is concluded that during acclimation, behavior shows changes in resistance to heat which are related to synaptic block. These are correlated in direction and overall time course with viscosity of synaptosomes as dictated by changes in the saturation of membrane phospholipids.

Correlations between behavioral temperature adaptations of goldfish and the viscosity and fatty acid composition of their synaptic membranes

As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already difficult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer afford to develop ad hoc, one-of-a-kind performance instrumentation software; we need scalable, portable performance analysis tools. We describe an environment prototype based on the lessons learned from two previous generations of performance data analysis software. Our environment prototype contains a set of performance data transformation modules that can be interconnected in user-specified ways. It is the responsibility of the environment infrastructure to hide details of module interconnection and data sharing. The environment is written in C++ with the graphical displays based on X windows and the Motif toolkit. It allows users to interconnect and configure modules graphically to form an acyclic, directed data analysis graph. Performance trace data are represented in a self-documenting stream format that includes internal definitions of data types, sizes, and names. The environment prototype supports the use of head-mounted displays and sonic data presentation in addition to the traditional use of visual techniques.

Scalable Performance Environments for Parallel Systems

The effect of temperature on transmission at the neuromuscular junction of the sartorius muscle of Rana pipiens

The National Center for Supercomputing Applications (NCSA) at the University of Illinois typifies current, high-performance computing centers that support a diverse user base of both academic and industrial accounts. The data requirements of this user community, coupled with resource constraints, mandate the use of an archival file system. This paper analyzes the spatial and temporal patterns of file archive transactions on the NCSA file archive. Analysis of archive transaction traces shows that the access patterns differ from those observed during studies of secondary storage systems: the files are larger, there are large temporal and spatial variations in transaction patterns, and a small number of research groups can easily account for twenty percent of the total traffic.

File archive activity in a supercomputing environment

Among the many techniques in computer graphics, ray tracing is prized because it can render realistic images, albeit at great computational expense. Ray tracing's large computation requirements, coupled with its inherently parallel nature, make ray tracing algorithms attractive candidates for parallel implementation. In this paper we illustrate the utility and the importance of a suite of performance analysis tools when exploring the performance of several approaches to ray tracing on a distributed memory parallel system. These ray tracing algorithm variations introduce parallelism based on both ray and object partitions.

Traditional timing analysis can quantify the performance effects of different algorithm choices (i.e. when an algorithm is best matched to a given problem), but it cannot identify the causes of these performance differences. We argue, by example, that a performance instrumentation system is needed that can trace the execution of distributed memory parallel programs by recording the occurrence of parallel program events. The resulting event traces can be used to compile summary stapistics that provide a global view of program performance. In addition, visualization tools permit the graphic display of event traces. Visual presentation of performance data is particularly useful, indeed, necessary for large-scale, parallel computations; assimilating the enormous volume of performance data mandates visual display.

A performance analysis exemplar: parallel ray tracing

Among the many techniques in computer graphics, ray tracing is prized because it can render realistic images, albeit at great computational expense. In this note we explore the performance of several approaches to ray tracing on a distributed memory parallel system. A set of performance instrumentation tools and their associated visualization software are used to identify the underlying causes of performance differences.

David W. Jensen

Papers

Scalable Performance Environments for Parallel Systems

The effect of temperature on transmission at the neuromuscular junction of the sartorius muscle of Rana pipiens

File archive activity in a supercomputing environment

A performance analysis exemplar: parallel ray tracing

Ray tracing on distributed memory parallel systems