Bioinformatics is among the most active research areas in computer science. In this study, we investigate a suite of workloads in bioinformatics on two multiprocessor systems with different configurations, and examine the effects of the configurations on the performance of the workloads. Our result indicates that the configurations of the multiprocessor systems have significant impact on the performance and scalability of the workloads. For example, a number of workloads have significantly higher scalability on one of the systems, but poorer absolute performance than on the other system. However, traditional scalability failed to capture the impacts of the system configurations on the workloads. We present insights on what kinds of workloads will run faster on which systems and propose new metrics to capture the impacts of multiple processor configurations on the workloads. These findings not only provide an easy way to compare results running on different systems, but also enable re-configuration of the underlying systems to run specific workloads efficiently. We also show how processor mapping and loop spreading may help map the workoads to the underlining multiprocessor configuration and achieve consistent scalability for these workloads.