Experiences in Data-Parallel Programming

Experiences in Data-Parallel Programming

To eeciently parallelize a scientiic application with a data-parallel compiler requires certain structural properties in the source program, and conversely, the absence of others. A recent parallelization eeort of ours reinforced this observation and motivated this correspondence. Speciically, we have transformed a Fortran 77 version of Gromos, a popular dusty-deck program for molecular dynamic...

Experiences on Data-parallel Programming

Data Parallel Programming in Scala

To more fully utilize the potential offered by multi-core processors, programming languages must have features for expressing parallelism. Data parallel programming features allow high-level collection-oriented operations to be easily expressed by the programmer, and then implemented by the runtime system in a parallel fashion for improved performance. This paper describes a data parallel class...

Parallel Dynamic Programming using Data - Parallel Skeletons ∗

For demonstrating the powerfulness of skeletal programming we have developed a parallelization technique toward one class of optimization problems called maximum marking problems, which can be recognized as one class of dynamic programming. This paper, as in the course of an ongoing research, focuses on problems of dynamic programming in general, and examines how the skeletal approach successfu...

Programming Data-parallel { Executing Process-parallel

Most theoretical work is based on the PRAM-model which has a block of shared memory and executes in a synchronous lock-step mode. Real hardware usually executes asynchronously and uses local memory and message passing. The recent LogP-model reeects these architectural properties. We show that for a practically important subclass of PRAM-programs it is possible to transform them into LogP-progra...