After just a couple of weeks since its introduction, the Tesla Bio Workbench community pages have been a hive of activity with more than 10,000 visitors in a matter of days. Today we added more codes to the program, specifically codes focused in the field of bio-informatics, such as BLASTP and HMMER, giving an even wider range of scientists and researchers the ability to increase the pace of their research using GPUs. Bio-Informatics codes are used heavily in pharmaceutical companies and in genomics research.
To be a CCOE, a university must demonstrate extensive use of GPU computing and the CUDA programming model in its research and teaching efforts across multiple science and engineering departments. University of Maryland has been one of the strongest supporters of GPU Computing since its introduction:
“Maryland was one of the first universities to start integrating the use of GPUs and the CUDA architecture into our courses and research,” said Amitabh Varshney, Professor of Computer Science at University of Maryland. “The CUDA programming model is an extremely effective educational tool for students learning parallel programming and no other technology available today provides as powerful and affordable platform for our research as the GPU.”
University of Maryland joins 9 other CCOEs in the U.S. and abroad including Cambridge University, Chinese Academy of Sciences, Harvard University, National Taiwan University and University of Illinois at Urbana-Champaign. In addition to these centers of excellence, more than 300 universities worldwide teach the CUDA programming model within their curriculum today.