ScalaBLAST: A Scalable Implementation of BLAST for High-Performance Data-Intensive Bioinformatics Analysis. Genes in an organism’s DNA (genome) have embedded in them information about proteins, which are the molecules that do most of a cell’s work. A typical bacterial genome contains on the order of 5,000 genes. Mammalian genomes can contain tens of thousands of genes. For each genome sequenced, the challenge is to identify protein components (proteome) being actively used for a given set of conditions. Fundamentally, sequence alignment is a sequence matching problem focused on unlocking protein information embedded in the genetic code, making it possible to assemble a ”tree of life” by comparing new sequences against all sequences from known organisms. But, the memory footprint of sequence data is growing more rapidly than per-node core memory. Despite years of research and development, high-performance sequence alignment applications either do not scale well, cannot accommodate very large databases in core, or require special hardware. We have developed a high-performance sequence alignment application, ScalaBLAST, which accommodates very large databases and which scales linearly to as many as thousands of processors on both distributed memory and shared memory architectures, representing a substantial improvement over the current state-of-the-art in high-performance sequence alignment with scaling and portability. ScalaBLAST relies on a collection of techniques - distributing the target database over available memory, multilevel parallelism to exploit concurrency, parallel I/O, and latency hiding through data prefetching - to achieve high-performance and scalability. This demonstrated approach of database sharing combined with effective task scheduling should have broad ranging applications to other informatics-driven sciences

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  1. Yu, Jaehong; Zhong, Hua; Kim, Seoung Bum: An ensemble feature ranking algorithm for clustering analysis (2020)
  2. Yu, Jaehong; Kim, Seoung Bum: Consensus rate-based label propagation for semi-supervised classification (2018)
  3. Philip Chen, C. L.; Zhang, Chun-Yang: Data-intensive applications, challenges, techniques and technologies: a survey on big data (2014) ioport
  4. Schmidt, Andrew G.; Datta, Siddhartha; Mendon, Ashwin A.; Sass, Ron: Investigation into scaling I/O bound streaming applications productively with an all-FPGA cluster (2012) ioport
  5. Sousa, Marcelo S.; Melo, Alba C. M. A.; Boukerche, Azzedine: An adaptive multi-policy grid service for biological sequence comparison (2010)
  6. Webb-Robertson, Bobbie-Jo M.; Oehmen, Christopher S.; Shah, Anuj R.: A feature vector integration approach for a generalized support vector machine pairwise homology algorithm (2008)
  7. Oehmen, Christopher; Nieplocha, Jarek: ScalaBLAST: A Scalable Implementation of BLAST for High-Performance Data-Intensive Bioinformatics Analysis (2006) ioport