PREFACE: Special Issue on High-Level Parallel Programming and Applications

Abstract

Many applications of computing require performance levels attainable only on parallel architectures. Such systems are now readily available as their price/performance ratio continues to improve. Despite this, corresponding software has not developed to the same levels of capability, price, and flexibility as those of sequential software. There are deep technical reasons for this, which research has been addressing for two decades. Sequential programming has long benefited from high-level programming techniques and tools that have made today's immense range of software economically viable. Research into high-level parallel programming is producing methods and tools that improve the price/performance ratio of parallel software, and broaden the range of target applications. This special issue of Parallel Processing Letters presents recent work of researchers in this field. One promising approach to developing portable parallel programs lies in the use of algorithmic skeletons templates for common patterns of parallel computation. Jocelyn SEROT demonstrates the use of dataflow techniques for efficient implementation of nested dynamic skeletons. Andrea ZAVANELLA applies the BSP cost model to data-parallel skeletons, allowing architecture-independent optimizations. The BSP computation model is also the subject of contributions by Alexandre TISKIN, who shows how threads may be used to allow divide-and-conquer algorithms to be expressed in BSP. Frederic LOULERGUE, who presents a functional calculus of BSP programs ; and Yifeng CHEN & J. W. SANDERS who define a refinement calculus for deriving BSP programs from specifications. Paul H. J. KELLY, Olav BECKMANN, Tony FIELD & Scott B. BADEN present the THEMIS model for dynamic optimizations of run-time parallel program behavior. Hans-Wolfgang LOIDL, Philip W. TRINDER & Carsten BUTZ introduce a way of improving task granularity and data locality in parallel Haskell programs based on systematic transformations of a data class. Zineb H ABB AS, Michael KRAJECKI & Daniel SINGER show a simple decomposition strategy for distributing a constraint satisfaction problem among machines. These papers were first presented at the International Workshop on High-Level Parallel Programming and Applications in Orleans in March 2001. This special