This grant will acquire, install, and maintain Proteus++, which is composed of Graphical Processing Unit (GPU) nodes and a high memory node. This project brings data-intensive computing (computing requiring large memory and high-throughput) hardware to Drexel University. The computing will strengthen research requiring large datasets in precision medicine (genomics, mapping the brain, and simulating molecules), advancing manufacturing, environmental modeling, and many other applications. The computing will also help train students in data-intensive computing through classes and co-operative work experiences and broaden participation in computing by engaging students in computing research as well as undergraduate institutions (including Historically Black Colleges and Universities and women's colleges) throughout the region to use the resources and enhance computing education. The research and training that results will not only advance fundamental research but enable innovation that will benefit the tech, health, and biology-enabled industries in the Philadelphia region.
Proteus++ will enable computational discovery that is data-intensive (needing large memory and high-throughput) and impact over 200 users by offering hybrid architectures that provide new scientific capabilities and enable faster computations. The intellectual merit of this project derives from a large collection of research topics that will be greatly enhanced by the sheer computational power and large-data processing that Proteus++ provides. Thanks to hybrid and large-memory computing, high-throughput genomics can finally enter the regime of searching simultaneously tens of thousands of microbial genomes from a large volume and diversity of queries, not least which involves discovery of currently unknown microorganisms in a variety of environments. Enhanced-sampling molecular simulations and hybrid molecular-dynamics/docking methods will significantly increase in speed under the hybrid architecture of Proteus++, enabling precise views of rare biomolecular event processes and investigation of an unprecedentedly large number of complex protein targets. Also, the prediction and simulation of material behavior and impurity benefits from co-processor architectures improving materials design for textiles, medicine, and energy. Finally, understanding brain activity is now within reach through GPU-accelerated Monte Carlo simulations, which will improve technologies that exploit light tissue interaction in the brain. Besides its direct impact on Drexel, the enhancement of URCF capabilities enables the creation of the Philadelphia High Performance Computing Consortium (PHPCC), a partnership among Drexel and a number of local/regional undergraduate-only institutions, for the purposes of exposing larger numbers of Consortium members, including Drexel, faculty, postdocs and students to the power and possibilities of computational discovery techniques. Capacity-building training in the form of workshops and courses will help new and existing PHPCC users learn not only about local University Research Computing Facility resources but also about advanced computational resources available at other institutions within the US. Furthermore, up to 2 million core-hours will be made available for use by non-Drexel PHPCC faculty and students.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
