Srinivas Aluru is a professor in the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He serves as a co-director of the Georgia Tech Strategic Initiative in Data Engineering and Science. Aluru conducts research in high performance computing, bioinformatics, systems biology, and combinatorial scientific computing. He pioneered the development of parallel methods in computational genomics and systems biology. He is a Fellow of the American Association for the Advancement of Science (AAAS) and the Institute of Electrical and Electronic Engineers (IEEE).
The Intel® Parallel Computing Center (Intel® PCC) on Big Data in Biosciences and Public Health is focused on developing and optimizing parallel algorithms and software on Intel Xeon® processor and Intel Xeon Phi™ Coprocessor systems for handling high-throughput DNA sequencing data and gene expression data. Advances in high-throughput sequencing technologies permit massively parallel sequencing of DNA at a low cost, leading to the creation of big data sets even in routine investigations by small research laboratories. Rapid analysis of such large-scale data is of critical importance in many applications, and is the foundation of modern genomics. This is currently an area underserved by high performance computing, but has great economic potential and societal prominence.
Research under this Intel® PCC will be focused on two large-scale projects: The first project is a comprehensive effort to identify core index structures and fundamental building blocks for the numerous applications of high-throughput DNA sequencing, develop parallel algorithms for them, and release them as software libraries to enable application parallelization. The Intel® PCC will support development of novel algorithms optimized for the Intel® Xeon Phi™ coprocessors, and development and release of software libraries specifically optimized for Intel® Xeon® processors and Intel® Xeon Phi™ coprocessors. The second project concerns the development of systems biology tools for biological researchers. Under the Intel® PCC, two objectives will be pursued: Large-scale Intel based clusters and supercomputers will be used to build whole-genome networks for important model organisms and make them widely available to researchers. A second objective is to put mid-scale network capabilities in the hands of individual biology researchers. The project will leverage other collaborative projects that support experimental research, allowing direct experimental verification of some of the tools generated under the Intel® PCC.
The work will lead to the release of open source software optimized for Intel Xeon® processors and Intel Xeon Phi™ coprocessors in the important areas of computational genomics and systems biology. These will be used in applications with the potential to impact many important fields including viral and microbial genomics, agricultural biotechnology, and precision medicine. The research is expected to inform future Intel® architectural designs regarding suitability in the important area of bioinformatics.
- Yongchao Liu, Tony Pan, Oded Green, Srinivas Aluru, 4/12/2017, Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors, Journal of Parallel and Distributed Computing
- Nagakishore Jammula, Sriram P. Chockalingam, Srinivas Aluru, 8/20/2017, Distributed Memory Partitioning of High-Throughput Sequencing Datasets for Enabling Parallel Genomics Analyses, Proceedings of the 8th ACM International Conference on Bioinformatics, Computati
- Chirag Jain,Alexander Dilthey,Sergey Koren,Srinivas Aluru,Adam M. Phillippy, 4/12/2017, A fast approximate algorithm for mapping long reads to large reference databases, International Conference on Research in Computational Molecular Biology
- Patrick Flick, Srinivas Aluru, 5/29/2017, Parallel Construction of Suffix Trees and the All-Nearest-Smaller-Values Problem, Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International