Advanced parallel computing systems have been used to transform, enhance, and accelerate different applications in various disciplines. These computing systems are characterized by exploiting the following types of parallelism: fine-grained, coarse-grained, thread-level, data-level, and request-level.1 Each parallelism adds a different level of support and acceleration to a different discipline’s application. One of the disciplines that have been enhanced and accelerated is bioinformatics. The discipline of bioinformatics is the application of tools of both analysis and computation to the interpretation and capturing of biological data.2 Bioinformatics is an evolving discipline, and complex computing systems are needed to sort, analyze, predict, and store biological data. This data is often needed rapidly, and advanced parallel computing systems can meet and exceed this need.
Advanced parallel computing can accelerate the many bioinformatics applications and algorithms that range from computer-intensive to data-intensive.1 This form of computing contains different types of systems in a wide range, from supercomputers to laptops. As a result of this range, advanced parallel computing systems propose the massive acceleration of bioinformatics applications and algorithms to produce high-level output. In addition, these parallel computing systems may use different technologies, architectures, and configurations to solve various issues and problems within bioinformatics applications.1 Advanced parallel computing usage and designs also create a challenge that opens up a new realm of possibilities that can further accelerate bioinformatics applications and algorithms. This acceleration greatly benefits the field of bioinformatics.
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Download “Accelerating Bioinformatics Applications via Emerging Parallel Computing Systems”
The IEEE/ACM Transactions on Computational Biology and Bioinformatics volume 12, number 5 journal has a section entitled, “Accelerating Bioinformatics Applications via Emerging Parallel Computing Systems,” which provides information on the impact and role advanced parallel computing systems have on bioinformatics applications and algorithms. The section of this article provides a forum where you have access to eight original articles that focus on the practical aspects of the efficient design and implementation application of hardware architectures to accelerate bioinformatics issues.1 The following eight original articles highlighted in this section are:
- “FHAST: FPGA-Based Acceleration of BOWTIE in Hardware” by Edward B. Fernandez, Jason Villarreal, Stefano Lonardi, and Walid A. Najjar
- “Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems” by Jorge Gonzalez-Dominguez, Lars Wienbrandt, Jan Christian Kassens, David Ellinghaus, Manfred Schimmler, and Bertil Schmidt
- “Concurrent and Accurate Short Read Mapping on Multicore Processors” by Hector Martınez, Joaquın Tarraga, Ignacio Medina, Sergio Barrachina, Maribel Castillo, Joaquın Dopazo, and Enrique S. QuintanaOrtı
- “Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor” by Sanchit Misra, Kiran Pamnany, and Srinivas Aluru
- “Large-Scale Tissue Morphogenesis Simulation on Heterogenous Systems Based on a Flexible Biomechanical Cell Model” by Anne Jeannin-Girardon, Pascal Ballet, and Vincent Rodin
- “An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics” by Yao Xin, Will X.Y. Li, Zhaorui Zhang, Ray C.C. Cheung, Dong Song, and Theodore W. Berger
- “Boosting the FM-Index on the GPU: Effective Techniques to Mitigate Random Memory Access” by Alejandro Chacon, Santiago Marco-Sola, Antonio Espinosa, Paolo Ribeca, and Juan Carlos Moure
- “Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting” by Thuy-Diem Nguyen, Bertil Schmidt, Zejun Zheng, and Chee-Keong Kwoh
Each of the articles mentioned provides a different level of insight into the acceleration of bioinformatics through advanced parallel computing.
