Data Parallel Programming

Another area in which object-oriented programming has had success is data parallel programming. Rob Collins (personal communication), from the University of California, Los Angeles, has built an efficient C++ library for the Thinking Machines Corporation CM-2, called CM++. It gives full access to the C/Paris functionality without any loss in performance. This allows writing in a higher-level abstraction without sacrificing speed. Collins and Steve Pope of the Advanced Computing Laboratory at Los Alamos National Laboratory (personal communication) have been working on porting this library to a more general environment, called DPAR. Thus, the data-parallel paradigm has been abstracted and now runs on a workstation with good optimization. We are optimistic that this library could be made to work on a Cray Research, Inc., supercomputer, with comparable efficiency to that provided on the CM-2.

We are also trying to combine this data-parallel programming paradigm with the distributed environment we mentioned before, following some of the ideas of Guy Steele. In a paper entitled "Making Asynchronous Parallelism Safe for the World," Steele (1990) describes a programming style that tries to unify SIMD and MIMD computing. The essence of the proposal is to allow asynchronous threads to have only a restricted shared-memory access providing only commutative operations. This removes the dependence of the order of sibling parallel threads.

Threads then communicate (synchronize) only on their death. Complex operations are provided by a hierarchy of communications. This is the style we have used in our distributed particle code mentioned earlier. In one sense, this style is the simplest extension of the SIMD programming model.