Parallel and distributed algorithms for computational biology

Abstract

The computational biology (CB) research area is now faced with an obstacle of everincreasing genome data. The amount, complexity and increased need for the rigorous postprocessing of this data requires an increased role for high performance computing (HPC).Dynamic programming (DP) is an important algorithm design technique in scientific computing. It has been widely applied to solve CB problems. Typical applications using this technique are compute-intensive and suffer from long runtimes on sequential architectures. In this thesis, we are proposing a tunable coarse-grained partitioning and communication scheme for DP algorithms. By introducing two performance-related parameters division and rowwidth, we can tradeoff between computation time and communication time by tuning these two parameters and thus obtain the maximum possible performance. We will demonstrate how this scheme leads to substantial performance gains for both regular and irregular DP applications.Genetic algorithms (GAs) are efficient search methods based on Darwinian evolution.They have powerful characteristics such as robustness and flexibility to capture global solutions of complex optimization problems. The fundamental nature of GAs relies on heuristics to quickly search large numbers of candidate results in order to achieve better solutions over time. Unfortunately, the more likely a good solution can be found, the more computational resources are needed by GAs. This leads to high runtimes on sequential architectures. Because of their inherent parallelism, GAs are promising candidates for efficient and scalable parallel implementations. In this thesis, we present the design of a new hierarchical parallel genetic algorithm (HPGA) for the protein folding problem (PFP) on PC clusters and computational grids. Our hierarchical approach unites the inter-cluster and intra-cluster parallelism in an efficient way by using a combination of two communication models, i.e. the stepping stone model and the island model. This i