Vertically Integrated Projects

Vertically Integrated Projects: High Performance Computing

A unique undergraduate level project-based course that enables students to work on research projects for more than just 1 semester.

Prof. Chandrasekaran has been teaching the VIP course since Spring 2017 to date.

  • Spring 2017 (ELEG467), 15 students

  • Fall 2017 (CISC467, ELEG467), 12 students

  • Spring 2018 (CISC467, ELEG467), 6 students

  • Fall 2018 (CISC467, ELEG467), 11 students

  • Spring 2019 (CISC467, ELEG467), 11 students

  • Fall 2019 (CISC 187/287/387/487, ELEG 487), 10 students

  • Spring 2020 (CISC 187/287/387/487, ELEG 487), 6 students

  • Fall 2020 Enrollement in progress

VIP-HPC Sample Project

A VIP project started in Spring 2017, under the supervision of Prof. Chandrasekaran, resulted in a publication in one of the top computational biology journals PLOS under Software Collection.

Collaborators: This is work in collaboration with Prof. Juan Perilla and Alex Bryer from the Department of Chemistry and Biochemistry at the University of Delaware.

Project Team: Eric Wright and Mauricio Ferrato who started off as VIP-HPC students in Spring 2017. They are now pursuing their PhD(s) in our lab. Robert Searles and Alex Bryer served as the senior mentors on the project.


Project Title: Accelerating Prediction of Chemical Shift of Protein Structures on GPUs using OpenACC

Project Abstract: Experimental chemical shifts (CS) from solution and solid state magic-angle-spinning nuclear magnetic resonance (NMR) spectra provide atomic level information for each amino acid within a protein or protein complex. However, structure determination of large complexes and assemblies based on NMR data alone remains challenging due to the complexity of the calculations. Here, we present a hardware accelerated strategy for the estimation of NMR chemical-shifts of large macromolecular complexes based on the previously published PPM_One software. The original code was not viable for computing large complexes, with our largest dataset taking approximately 14 hours to complete. Our results show that serial code refactoring and parallel acceleration brought down the time taken of the software running on an NVIDIA Volta 100 (V100) Graphic Processing Unit (GPU) to 46.71 seconds for our largest dataset of 11.3 million atoms. We use OpenACC, a directive-based programming model for porting the application to a heterogeneous system consisting of x86 processors and NVIDIA GPUs. Finally, we demonstrate the feasibility of our approach in systems of increasing complexity ranging from 100K to 11.3M atoms.

This project has won one of the best research posters in the International Supercomputing Conference (ISC), 2019, Frankfurt, Germany under the HPC category. The project also won the best poster in the VIP Mid-Altantic poster competition, Spring 2018. Prof. Chandrasekaran and team have presented this work at GTC 2019, SIAM CSE 2019 and PASC 2019.

Other VIP Projects include:

  • Building a database to rank Standard Performance Evaluation Corporation (SPEC) High Performance Group (HPG’s) results using the work in progress HPC2020 benchmark suite.

  • Updating the OpenACC Best Practices Guide, work in collaboration with NVIDIA

  • Parallelizing NASA parallel benchmarks (NPB) on multiple platforms thus learning parallel programming concepts

  • Learning to write scripts to run a large number of bioinformatics applications on large clusters