Goal-Directed Performance Tuning for Scientific Applications

ABSTRACT Goal-Directed Performance Tuning for Scientific Applications by Tien-Pao Shih Chair: Edward S. Davidson Performance tuning, as carried out by compiler designers and application programmers to close the performance gap between the achievable peak and delivered performance, becomes increasingly important and challenging as the microprocessor speeds and system sizes increase. However, although performance tuning on scientific codes usually deals with relatively small program regions, it is not generally known how to establish a reasonable performance objective and how to efficiently achieve this objective. We suggest a goal-directed approach and develop such an approach for each of three major system performance components: central processor unit (CPU) computation, memory accessing, and communication. For the CPU, we suggest using a machine-application performance model that characterizes workloads on four key function units (memory, floating-point, issue, and a virtual "dependence unit") to produce an upper bound performance objective, and derive a mechanism to approach this objective. A case study shows an average 1.79x speedup achieved by using this approach for the Livermore Fortran Kernels 1-12 running on the IBM RS/6000. For memory, as compulsory and capacity misses are relatively easy to characterize, we derive a method for building application-specific cache behavior models that report the number of misses for all three types of conflict misses: self, cross, and ping-pong. The method uses averaging concepts to determine the expected number of cache misses instead of attempting to count them exactly in each instance, which provides a more rapid, yet realistic assessment of expected cache behavior. For each type of conflict miss, we propose a reduction method that uses one or a combination of three techniques based on modifying or exploiting data layout: array padding, initial address adjustment, and access resequencing