The floating point performance of superscalar SPARC processors is evaluated based on empirical data horn 12 benchmarks.This evaluation is done in the context of two software instruction scheduling optimization, loop unrolling and software pipelining, and for three machine models we term, 1-scalar, 2-scalar and 4-scalar.We also consider the effect of the memory system on the performance improvements.Superscalar hardware alone exhibit little performance improvement without software optimization.Of the two scheduling methods we study, software pipelining more effectively takes advantage of increased hardware parallelism, and achieves near optimal speedup on the 4-scalar machine model.The performance of loop unrolling is restricted by the limited number of floating point registers in the SPARC architecture.The best performance level is obtained by applying both optimization techniques.A superscalar SPARC processor can provide improved tloating point performance but with significant software and hardware development costs.

The development of performance prediction tools for highspeed machine organizations has been recognized as a key problem facing the research community in parallel computing [BrAI84].This paper presents a survey of the tools which has been developed for performance prediction of the Cedar multiproce~or supercomputer of the University of Illinois.The system is deterministic, modular, and automatic.The hierarchical organization of the system provides the user with the ability to choose from a set of alternatives for predicting the performance with different levels of accuracy and cost.Using 22 programs, we measure the performance degradation due to conflicts in the shared memory, delay in the Cedar interconnection network, and synchronization overhead.The results confirm that the architecture of Cedar as detailed in [GLYZ84] is balanced.The performance of the Cedar interconnectiou network is very close to a crossbar.Synchronization overhead and shared memory conflicts could degrade performance for some programs considerably.