DPF: A Data Parallel Fortran Benchmark Suite

We present the Data Parallel Fortran (DPF) benchmark suite, a set of data parallel Fortran codes for evaluating data parallel compilers appropriate for any target parallel architecture, with shared or distributed memory. The codes are provided in basic, optimized and several library versions. The functionality of the benchmarks cover collective communication functions, scientific software library functions, and application kernels that reflect the computational structure and communication patterns in fluid dynamic simulations, fundamental physics and molecular studies in chemistry or biology. The DPF benchmark suite assumes the language model of High Performance Fortran, and provides performance evaluation metrics of busy and elapsed times and FLOP rates, FLOP count, memory usage, communication patterns, local memory access, and arithmetic efficiency as well as operation and communication counts per iteration. An instance of the benchmark suite was fully implemented in CM–Fortran and tested on the CM–5.Engineering and Applied Science

High-Performance Computing and Four-Dimensional Data Assimilation: The Impact on Future and Current Problems

This is the final technical report for the project entitled: "High-Performance Computing and Four-Dimensional Data Assimilation: The Impact on Future and Current Problems", funded at NPAC by the DAO at NASA/GSFC. First, the motivation for the project is given in the introductory section, followed by the executive summary of major accomplishments and the list of project-related publications. Detailed analysis and description of research results is given in subsequent chapters and in the Appendix

[Activity of Institute for Computer Applications in Science and Engineering]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science

Exploiting Locality and Parallelism with Hierarchically Tiled Arrays

The importance of tiles or blocks in mathematics and thus computer science cannot be overstated. From a high level point of view, they are the natural way to express many algorithms, both in iterative and recursive forms. Tiles or sub-tiles are used as basic units in the algorithm description. From a low level point of view, tiling, either as the unit maintained by the algorithm, or as a class of data layouts, is one of the most effective ways to exploit locality, which is a must to achieve good performance in current computers given the growing gap between memory and processor speed. Finally, tiles and operations on them are also basic to express data distribution and parallelism. Despite the importance of this concept, which makes inevitable its widespread usage, most languages do not support it directly. Programmers have to understand and manage the low-level details along with the introduction of tiling. This gives place to bloated potentially error-prone programs in which opportunities for performance are lost. On the other hand, the disparity between the algorithm and the actual implementation enlarges. This thesis illustrates the power of Hierarchically Tiled Arrays (HTAs), a data type which enables the easy manipulation of tiles in object-oriented languages. The objective is to evolve this data type in order to make the representation of all classes for algorithms with a high degree of parallelism and/or locality as natural as possible. We show in the thesis a set of tile operations which leads to a natural and easy implementation of different algorithms in parallel and in sequential with higher clarity and smaller size. In particular, two new language constructs dynamic partitioning and overlapped tiling are discussed in detail. They are extensions of the HTA data type to improve its capabilities to express algorithms with a high abstraction and free programmers from programming tedious low-level tasks. To prove the claims, two popular languages, C++ and MATLAB are extended with our HTA data type. In addition, several important dense linear algebra kernels, stencil computation kernels, as well as some benchmarks in NAS benchmark suite were implemented. We show that the HTA codes needs less programming effort with a negligible effect on performance

HPCCP/CAS Workshop Proceedings 1998

This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey

Compiler Techniques for Optimizing Communication and Data Distribution for Distributed-Memory Computers

Advanced Research Projects Agency (ARPA)National Aeronautics and Space AdministrationOpe

Runtime Support for In-Core and Out-of-Core Data-Parallel Programs

Distributed memory parallel computers or distributed computer systems are widely recognized as the only cost-effective means of achieving teraflops performance in the near future. However, the fact remains that they are difficult to program and advances in software for these machines have not kept pace with advances in hardware. This thesis addresses several issues in providing runtime support for in-core as well as out-of-core programs on distributed memory parallel computers. This runtime support can be directly used in application programs for greater efficiency, portability and ease of programming. It can also be used together with a compiler to translate programs written in a high-level data-parallel language like High Performance Fortran (HPF) to node programs for distributed memory machines. In distributed memory programs, it is often necessary to change the distribution of arrays during program execution. This thesis presents efficient and portable algorithms for runtime array redistribution. The algorithms have been implemented on the Intel Touchstone Delta and are found to scale well with the number of processors and array size. This thesis also presents algorithms for all to all collective communication on fat tree and two dimensional mesh interconnection topologies. The performance of these algorithms on the CM 5 and Touchstone Delta is studied extensively. A model for estimating the time taken by these algorithms on the basis of system parameters is developed and validated by comparing with experimental results. A number of applications deal with very large data sets which cannot fit in main memory, and hence have to be stored in files on disks, resulting in out of core programs. This thesis also describes the design and implementation of efficient runtime support for out of core computations. Several optimizations for accessing out of core data are presented. An extended Two Phase Method is proposed for accessing sections of out of core arrays efficiently. This method uses collective I/O and the I/O workload is divided among processors dynamically, depending on the access requests. Performance results obtained using this runtime support for out of core programs on the Touchstone Delta are presented

[Research activities in applied mathematics, fluid mechanics, and computer science]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period April 1, 1995 through September 30, 1995