No abstract

Abstract. This paper describes how the specific access structure of the Brusselator equation, a typical example for ordinary differential equations (ODEs) derived by the method of lines, can be exploited to obtain scalable distributed-memory implementations of explicit Runge-Kutta (RK) solvers. These implementations need less communication and therefore achieve better speed-ups than general explicit RK implementations. Particularly, we consider implementations based on a pipelining computation scheme leading to an improved locality behavior.

SUMMARYSince a static work distribution does not allow for satisfactory speed-ups of parallel irregular algorithms, there is a need for a dynamic distribution of work and data that can be adapted to the runtime behavior of the algorithm. Task pools are data structures which can distribute tasks dynamically to different processors where each task specifies computations to be performed and provides the data for these computations. This paper discusses the characteristics of task-based algorithms and describes the implementation of selected types of task pools for shared-memory multiprocessors. Several task pools have been implemented in C with POSIX threads and in Java. The task pools differ in the data structures to store the tasks, the mechanism to achieve load balance, and the memory manager used to store the tasks. Runtime experiments have been performed on three different shared-memory systems using a synthetic algorithm, the hierarchical radiosity method, and a volume rendering algorithm.

A task-based execution provides a universal approach to dynamic load balancing for irregular applications. Tasks are arbitrary units of work that are created dynamically at runtime and that are stored in a parallel data structure, the task pool, until they are scheduled onto a processor for execution. In this paper, we evaluate the performance of different task pool implementations for shared-memory computer systems using several realistic applications. We consider task pools with different data structures, different load balancing strategies and a specialized memory management. In particular, we use synchronization operations based on hardware support that is available on many modern microprocessors. We show that the resulting task pool implementations lead to a much better performance than implementations using Pthreads library calls for synchronization. The applications considered are parallel quicksort, volume rendering, ray tracing, and hierarchical radiosity. The target machines are an IBM p690 server and a SunFire 6800.

No abstract

No abstract

No abstract

Abstract