Parallel Computing in Science and Engineering

1. Front Matter

   PDF

2. Introduction to the seminar

   • Hans-Martin Wacker

   Pages 1-11

3. Classification and evaluation of parallel computer systems

   • Roger W. Hockney

   Pages 13-25

4. Shared memory, vectors, message passing, and scalability

   • Burton J. Smith

   Pages 27-34

5. Communication techniques in parallel processing

   • Howard Jay Siegel, William Tsun-yuk Hsu, Menkae Jeng, Wayne G. Nation

   Pages 35-60

6. Two fundamental issues in multiprocessing

   • Arvind, Robert A. Iannucci

   Pages 61-88

7. Parallel programming support in ParaScope

   • David Callahan, Keith D. Cooper, Robert T. Hood, Ken Kennedy, Linda M. Torczon, Scott K. Warren

   Pages 89-106

8. Automatic restructuring of Fortran programs for parallel execution

   • Constantine D. Polychronopoulos

   Pages 107-130

9. Locating parallel numerical tasks in the solution of viscous fluid flow

   • Garry Rodrigue, A. Louise Perkins

   Pages 131-143

10. On the multigrid acceleration approach in computational fluid dynamics

    • Karl Solchenbach, Ulrich Trottenberg

    Pages 145-158

11. An algebraic approach to performance analysis

    • Dieter Müller-Wichards

    Pages 159-185

It was the aim of the conference to present issues in parallel computing to a community of potential engineering/scientific users. An overview of the state-of-the-art in several important research areas is given by leading scientists in their field. The classification question is taken up at various points, ranging from parametric characterizations, communication structure, and memory distribution to control and execution schemes. Central issues in multiprocessing hardware and operation, such as scalability, techniques of overcoming memory latency and synchronization overhead, as well as fault tolerance of communication networks are discussed. The problem of designing and debugging parallel programs in a user-friendly environment is addressed and a number of program transformations for enhancing vectorization and parallelization in a variety of program situations are described. Two different algorithmic techniques for the solution of certain classes of partial differential equations are discussed. The properties of domain-decomposition algorithms and their mapping onto a CRAY-XMP-type architecture are investigated and an overview is given of the merit of various approaches to exploiting the acceleration potential of multigrid methods. Finally, an abstract performance modeling technique for the behavior of applications on parallel and vector architectures is described.

• Debugging
• Potential
• Scala
• Transformation
• algorithm
• algorithms
• communication
• modeling
• parallel programming
• programming
• complexity

Policies and ethics