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

The past few years have seen a rise in popularity of massively parallel architectures that use fat-trees as their interconnection networks. In this paper we study the communication performance of a parametric family of fat-trees, the k-ary n-trees, built with constant arity switches interconnected in a regular topology. Through simulation on a 4-ary 4-tree with 256 nodes, we analyze some variants of an adaptive algorithm that utilize wormhole routing with one, two and four virtual channels. The experimental results show that the uniform, bit reversal and transpose traffic patterns are very sensitive to the flow control strategy. In all these cases, the saturation points are between 35-40% of the network capacity with one virtual channel, 55-60% with two virtual channels and around 75% with four virtual channels. The complement traffic, a representative of the class of the congestion-free communication patterns, reaches an optimal performance, with a saturation point at 97% of the capacity for all flow control strategies.

k-ary n-trees: high performance networks for massively parallel architectures

Writing large-scale parallel and distributed scientific applications that make optimum use of the multiprocessor is a challenging problem. Typically, computational resources are underused due to performance failures in the application being executed. Performance-tuning tools are essential for exposing these performance failures and for suggesting ways to improve program performance. In this paper, we first address fundamental issues in building useful performance-tuning tools and then describe our experience with the AIMS toolkit for tuning parallel and distributed programs on a variety of platforms. AIMS supports source-code instrumentation, run-time monitoring, graphical execution profiles, performance indices and automated modeling techniques as ways to expose performance problems of programs. Using several examples representing a broad range of scientific applications, we illustrate AIMS' effectiveness in exposing performance problems in parallel and distributed programs.

Performance measurement, visualization and modeling of parallel and distributed programs using the AIMS toolkit

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 Thinking Machines CM-5 is one of the first of a new generation of massively parallel systems. To assess the scalability of the CM-5's computation and interprocessor communication rates, a series of benchmarks was used to measure the performance of the CM-5 data and control networks, the node vector units, and the balance of computation and communication. At the application level, the achievable communication bandwidth and processing rates were found to be roughly 50% and 40%, respectively, of the corresponding theoretical peak rates. The early assessment is that the CM-5 is scalable but that a better balance of communication and processing rates would increase its effectiveness.

Communication and computation performance of the CM-5

In distributed-memory multiprocessors, remote memory accesses incur larger delays than local accesses. Hence, insightful allocation and access of distributed data can yield substantial performance gains. The authors argue for the use of dynamic data management policies encapsulated within individual distributed data structures. Distributed data structures offer performance, flexibility, abstraction, and system independence. This approach is supported by data from a trace-driven simulation study of parallel scientific benchmarks. Experimental data on memory locality, message count, message volume, and communication delay, suggest that data-structure-specific data management is superior to a single, system-imposed policy. >

B. K. Totty

Papers

Scalable Performance Environments for Parallel Systems

Communication and computation performance of the CM-5

Dynamic object management for distributed data structures