BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Nowadays, it is becoming increasingly important to use the most efficient and most suitable computational resources for algorithmic tools that extract meaningful information from big data and make smart decisions. In this paper, a comparative analysis is provided for performance measurements of various machine learning and bioinformatics software including scikit-learn, Tensorflow, WEKA, libSVM, ThunderSVM, GMTK, PSI-BLAST, and HHblits with big data applications on different high performance computer systems and workstations. The programs are executed in a wide range of conditions such as single-core central processing unit (CPU), multi-core CPU, and graphical processing unit (GPU) depending on the availability of implementation. The optimum number of CPU cores are obtained for selected software. It is found that the running times depend on many factors including the CPU/GPU version, available RAM, the number of CPU cores allocated, and the algorithm used. If parallel implementations are available for a given software, the best running times are typically obtained by GPU, followed by multi-core CPU, and single-core CPU. Though there is no best system that performs better than others in all applications studied, it is anticipated that the results obtained will help researchers and practitioners to select the most appropriate computational resources for their machine learning and bioinformatics projects.

/pdf/performance-analysis-of-machine-learning-and-bioinformatics-2atdxhaobn.pdf

Performance Analysis of Machine Learning and Bioinformatics Applications on High Performance Computing Systems

Next-generation sequencing (NGS) has drastically changed researches based on DNA sequencing with its high throughput and low costs. Mapping sequences generated by NGS sequences onto reference genomes is an indispensable step to find useful knowledge for biological researches or clinical applications. To accelerate genome mapping by using a new many-core processor Xeon Phi, two major mapping programs, BWA and Bowtie2, were ported to Xeon Phi in this study. Although vector operations of Xeon Phi are not compatible with those of x86 processors, these incompatibilities were successfully circumvented. In a computational experiment where the ported programs were evaluated, the performances of the ported BWA and Bowtie2 peaked when 120 and 60 threads were used, respectively. These results imply that performances of BWA and Bowtie2 can be improved by using tens of processing cores.

Genome Mapping by a 60-core Processor

Alignment of whole-genome sequences using draft genomes of OHSU_I isolates (un-cloned, C4 and C6) and OHSU_II isolates (un-cloned, C1 and C2).

The performance of the many-core Tile64 versus the multi-core Xeon x86 architecture on bioinformatics has been compared. We have used the pairwise algorithm MC64-NW/SW that we have previously developed to align nucleic acid (DNA and RNA) and peptide (protein) sequences for the benchmarking, being an enhanced and parallel implementation of the Needleman-Wunsch and Smith-Waterman algorithms. We have ported the MC64-NW/SW (originally developed for the Tile64 processor), to the x86 architecture (Intel Xeon Quad Core and Intel i7 Quad Core processors) with excellent results. Hence, the evolution of the x86-based architectures towards coprocessors like the Xeon Phi should represent significant performance improvements for bioinformatics.

/pdf/bioinformatics-performance-comparison-of-many-core-tile64-vs-50b2058bnt.pdf

Bioinformatics Performance Comparison of Many-core Tile64 vs. Multi-core Intel Xeon

The performance of the many-core Tile64 versus the multi-core Xeon x86 architecture on bioinformatics has been compared. We have used the pair- wise algorithm MC64-NW/SW that we have previously developed to align nu- cleic acid (DNA and RNA) and peptide (protein) sequences for the benchmark- ing, being an enhanced and parallel implementation of the Needleman-Wunsch and Smith-Waterman algorithms. We have ported the MC64-NW/SW (original- ly developed for the Tile64 processor), to the x86 architecture (Intel Xeon Quad Core and Intel i7 Quad Core processors) with excellent results. Hence, the evo- lution of the x86-based architectures towards coprocessors like the Xeon Phi should represent significant performance improvements for bioinformatics.

Myriam Kurtz

Papers

Bioinformatics Performance Comparison of Many-core Tile64 vs. Multi-core Intel Xeon

Many-core Tile64 vs. Multi-core Intel Xeon: Bioinformatics Performance Comparison 1