SPEED: A parallel platform for solving and predicting the performance of PDEs on distributed systems

Distributed systems such as networks of workstations are becoming an increasingly viable alternative to traditional supercomputer systems for running complex scientific applications. A large number of these applications require solving sets of partial differential equations (PDEs). In this paper, we describe the implementation and performance of SPEED (Scalable Partial differential Equation Environment on Distributed systems), a parallel platform which provides an efficient solution for time-dependent PDEs. SPEED allows the inclusion of a wide range of parameters and programming aids. PVM is employed as the underlying message-passing system. The parallel implementation has been performed using two algorithms. The first algorithm is a two-phase scheme which uses the conventional technique of alternating phases of computation and communication. The second algorithm employs a pre-computation technique that allows overlapping of computation and communication. Both methods yield significant speedups. The pre-computation technique reduces the communication time between the workstations but incurs additional overhead in buffer management. Hence, if the saving in communication time is larger than the overhead, the pre-computation technique outperforms the two-phase algorithm. SPEED also provides a performance prediction methodology that can accurately predict the performance of a given application on the system before running the application. This methodology allows the user to tune various parameters in order to identify system bottlenecks and maximize the performance.