Characterizing and Improving the Performance of Bioinformatics Workloads on the POWER5 Architecture

This paper examines several mechanisms to improve the performance of life science applications on high-performance computer architectures typically designed for more traditional supercomputing tasks. In particular, we look at the detailed performance characteristics of some of the most popular sequence alignment and homology applications on the POWERS architecture offering from IBM. Through detailed analysis of performance counter information collected from the hardware, we identify the main performance bottleneck in the current POWER5 architecture for these applications is the high branch misprediction penalty of the most time-consuming kernels of these codes. Utilizing our PowerPC full system simulation environment, we show the performance improvement afforded by adding conditional assignments to the PowerPC ISA. We also show the impact of changing the number of functional units to a more appropriate mix for the characteristics of bioinformatics applications. Finally, we examine the benefit of removing the two-cycle penalty currently in the POWERS architecture for taken branches due to the lack of a branch target buffer. Addressing these three performance-limiting aspects provides an average 64% improvement in application performance.