Abstract
The first digital computers consisted of a single processor acting on a single stream of data. In this so-called "von Neumann" architecture, computation speed is limited mainly by the time required to transfer data between the processor and memory. This limiting factor has been referred to as the "von Neumann bottleneck". The concern that the miniaturization of silicon-based integrated circuits will soon reach theoretical limits of size and gate times has led to increased interest in parallel architectures and also spurred research into alternatives to silicon-based implementations of processors. Meanwhile, sequential processors continue to be produced that have increased clock rates and an increase in memory locally available to a processor, and an increase in the rate at which data can be transferred to and from memories, networks, and remote storage. The efficiency of compilers and operating systems is also improving over time. Although such characteristics limit maximum performance, a large improvement in the speed of scientific computations can often be achieved by utilizing more efficient algorithms, particularly those that support parallel computation. This work discusses experiences with two tools for large grain (or "macro task") parallelism.