We use the information derived with FAST to evaluate and analyze the relative performance of the algorithm on different interconnection topologies.
Performance is critical in parallel computing.
Gaining an understanding of performance issues for challenging parallel algorithms like the FMM, however, requires the description and analysis of their parallel execution on realistic sets of data. This is a difficult task for many reasons, including the high complexity of these algorithms, the great volume of data needed to describe computations and communications performed during parallel execution, and the low availability of high-end multiprocessor systems. This approach has its obvious merits as it addresses the performance assessment of programs running on existing systems. Experimentation with different interconnection topologies requires the porting of applications to different multiprocessors, thus incurring a very high cost. Moreover, it is practically impossible to study the scalability of parallel algorithms and architectures on top of existing systems.