Biological Sequence Comparison on Hybrid Platforms with Dynamic Workload Adjustment

This paper proposes and evaluates a strategy to run Biological Sequence Comparison applications on hybrid platforms composed of GPUs and multicores with SIMD extensions. Our strategy provides multiple task allocation policies and the user can choose the one which is more appropriate to his/her problem. We also propose a workload adjustment mechanism that tackles situations that arise when slow nodes receive the last tasks. The results obtained comparing query sequences to 5 public genomic databases in a platform composed of 4 GPUs and 2 multicores show that we are able to reduce the execution time with hybrid platforms, when compared to the GPU-only solution. We also show that our workload adjustment technique can provide significant performance gains in our target platforms.