Testing the performance scalability of parallel programs can be a time consuming task, involving many performance runs for different computer configurations, processor numbers, and problem sizes. Ideally, scalability issues would be addressed during parallel program design, but tools are not presently available that allow program developers to study the impact of algorithmic choices under different problem and system scenarios. Hence, scalability analysis is often reserved to existing (and available) parallel machines as well as implemented algorithms. In this paper we propose techniques for analyzing scaled parallel programs using stochastic modeling approaches. Although allowing more generality and flexibility in analysis, stochastic modeling of large parallel

A method for analyzing the functional behavior and the performance of programs in distributed systems is presented. We use hybrid monitoring, a technique which combines advantages of both software monitoring and hardware monitoring. The paper contains a description of a hardware monitor and a software package (ZM4/SIMPLE) which make our concepts available to programmers, assisting them in debugging and tuning of their code. A short survey of related monitor systems highlights the distinguishing features of our implementation. As an application of our monitoring and evaluation system, the analysis of a parallel ray tracing program running on the SUPRENUM multiprocessor is described. It is shown that monitoring and modeling both rely on a common abstraction of a system's dynamic behavior and therefore can be integrated to one comprehensive methodology. This methodology is supported by a set of tools.>

Most parallel applications (e.g. image processing, mtdtigrid algorithms) in a Transputer-network require a lot of communication between the processing nodes. For such applications the communication system TRACOS was developed to support data transfer between random Transputers in the network. To marimize the performance of the parallel system, its dynamic internal behavior has to be analyzed. For this purpose event-driven monitoring is an appropriate technique. It reduces the dynamic behavior of the system to sequences of events. They are recor&d by a monitor system and stored as event traces. In this paper the communication system TRACOS and its performance evaluation based on monitored event traces are presented. First a synthetic workload was instrumented and monitored with the distributed hardware monitor ZM4. The results showed that the performance of TRACOS is poor for packets smaller than 4 Kbyte. Therefore, TRACOS itself was instrumented and monitored to get insight into the interactions and interdependencies of all TRACOS processes. Based on the monitoring resuhs, TRACOS could be improved which led to a performance increase of 25T0.