PSDG is a parallel synthetic data generator designed to generate "industrial sized" data sets quickly using cluster computing. PSDG depends on SDDL, a synthetic data description language that provides flexibility in the types of data we can generate.

We present detailed experimental work involving a commercially available large scale shared memory multiple instruction stream-multiple data stream (MIMD) parallel computer having a software controlled cache coherence mechanism. To make effective use of such an architecture, the programmer is responsible for designing the program's structure to match the underlying multiprocessors capabilities. We describe the techniques used to exploit our multiprocessor (the BBN TC2000) on a network simulation program, showing the resulting performance gains and the associated programming costs. We show that an efficient implementation relies heavily on the user's ability to explicitly manage the memory system.

PSDG is a parallel synthetic data generator designed to generate "industrial sized" data sets quickly using cluster computing. PSDG depends on SDDL, a synthetic data description language that provides flexibility in the types of data we can generate.

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We present detailed experimental work involving a commercially available large scale shared memory MIMD paraliel computer having a software controlled cache coherence mechanism. The implementation of a scalable MIMD computer with hardware-