This paper presents our first experiences in mapping and optimizing genomic sequence search onto the massively parallel IBM Blue Gene/P (BG/P) platform. Specifically, we performed our work on mpiBLAST, a parallel sequence-search code that has been optimized on numerous supercomputing environments. In doing so, we identify several critical performance issues. Consequently, we propose and study different approaches for mapping sequence-search and parallel I/O tasks on such massively parallel architectures.We demonstrate that our optimizations can deliver nearly linear scaling (93% efficiency) on up to 32,768 cores of BG/P. In addition, we show that such scalability enables us to complete a large-scale bioinformatics problem - sequence searching a microbial genome database against itself to support the discovery of missing genes in genomes - in only a few hours on BG/P. Previously, this problem was viewed as computationally intractable in practice.

Bioinformatics databases used for sequence comparison and sequence alignment are growing exponentially. This has popularized programs that carry out database searches. Current implementations of sequence alignment methods based on hidden Markov models (HMM) have proven to be computationally intensive and, hence, amenable to architectures with multiple processors. In this paper, we describe a modified version of the original parallel implementation of HMMs on a massively parallel system. This is part of the HMMER bioinformatics code. HMMER 2.3.2 uses profile HMMs for sensitive database searching based on statistical descriptions of a sequence family's consensus (Durbin et al., 1998), Two of the nine programs were further parallelized to take advantage of the large number of processors, namely, hmmsearch and hmmpfam. For our study, we start by porting the parallel virtual machine (PVM) versions of these two programs currently available as part of the HMMER suite of programs. We report the performance of these nonoptimized versions as baselines. Our work also includes the introduction of an alternate sequence file indexing, multiple-master configuration, dynamic data collection and, finally, load balancing via the indexed sequence files. This set of optimizations constitutes our modified version for massively parallel systems. Our results show parallel performance improvements of more than one order of magnitude (16 times) for hmmsearch and hmmpfam.

Bayesian phylogenetic inference is an important alternative to maximum likelihood-based phylogenetic method. However, inferring large trees using the Bayesian approach is computationally demanding - requiring huge amounts of memory and months of computational time. With a combination of novel parallel algorithms and latest system technology, terascale phylogenetic tools provide biologists the computational power necessary to conduct experiments on very large dataset, and thus aid construction of the tree of life. In this work we evaluate the performance of PBPI, a parallel application that reconstructs phylogenetic trees using MCMC-based Bayesian methods, on two terascale systems, Blue Gene/L at IBM Rochester and System X at Virginia Tech. Our results confirm that for a benchmark dataset with 218 taxa and 10000 characters, PBPI can achieve linear speedup on 1024 or more processors for both systems.