A Novel Approach for Web Mining Taxonomy for High-Performance Computing

2021 ◽  
pp. 425-432
Author(s):  
Debabrata Samanta ◽  
Soumi Dutta ◽  
Mohammad Gouse Galety ◽  
Sabyasachi Pramanik
2020 ◽  
Author(s):  
Ambarish Kumar ◽  
Ali Haider Bangash

AbstractGenomics has emerged as one of the major sources of big data. The task of augmenting data-driven challenges into bioinformatics can be met using technologies of parallel and distributed computing. GATK4 tools for genomic variants detection are enabled for high-performance computing platforms – SPARK Map Reduce framework. GATK4+WDL+CROMWELL+SPARK+DOCKER is proposed as the way forward in achieving automation, reproducibility, reusability, customization, portability and scalability. SPARK-based tools perform equally well in genomic variants detection with that of standard implementation of GATK4 tools over a command-line interface. Implementation of workflows over cloud-based high-performance computing platforms will enhance usability and will be a way forward in community research and infrastructure development for genomic variant discovery.


MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.


2001 ◽  
Author(s):  
Donald J. Fabozzi ◽  
Barney II ◽  
Fugler Blaise ◽  
Koligman Joe ◽  
Jackett Mike ◽  
...  

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