David Krasowska
- Program Year: 1
- Academic Institution: Northwestern University
- Field of Study: Computer Science
- Academic Advisor: Peter Dinda
- Practicum(s): Practicum Not Yet Completed
- Degree(s):
B.S. Computer Engineering, Clemson University, 2022 - Personal URL: http://krasow.dev
Summary of Research
I have an interest in designing a processing in memory (PIM) system for HPC domain specific applications. I would like to focus on HPC infrastructures; however, this approach would generalize to other infrastructures, such as data centers. Data centers are used all over the world and utilize lots of valuable resources/energy. Improving data center architecture and programmability can reduce the environmental footprint. Northwestern has multiple labs working towards the goal of optimizing performance in computing systems. I am currently working with the Prescience lab that focuses on optimizing higher level code to reduce programmer intervention. My goal is to take high level languages that are popular within a specific domain and automatically detect parallel code to generate PIM instructions. Using today’s PIM hardware, I expect to be able to develop a software approach to achieve this goal. I can leverage my group's compiler and language expertise in pursuit of the goal. This approach would allow domain scientists to keep their high level code and not spend time rewriting it for the new system. Based on my experience in the software approach, I will next consider improvements to hardware design. This plan will hopefully lead to increased performance and allow for less energy consumption, thus improving efficiency and environmental impact.Publications
A. Ganguli, R. Underwood, J. Bessac, D. Krasowska, J. C. Calhoun, S. Di, and F. Cappello. "A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression," IEEE International Conference on Cluster Computing (CLUSTER), Santa Fe, NM, 2023, pp. 247-258, doi:10.1109/CLUSTER52292.2023.00028R. Underwood, J. Bessac, D. Krasowska, J. C. Calhoun, S. Di, and F. Cappello. "Black-box statistical prediction of lossy compression ratios for scientific data," The International Journal of High Performance Computing Applications (IJHPCA), 2023, pp. 412-433, doi:10.1177/10943420231179417
D. Krasowska, J. Bessac, R. Underwood, J. C. Calhoun, S. Di, and F. Cappello. "Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets," 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7), St. Louis, MO, USA, 2021, pp. 47-53, doi:10.1109/DRBSD754563.2021.00011
Awards
First Place for ACM Student Research Competition: Undergraduate Poster at Supercomputing '22Best Poster IndySCC22 at Supercomputing '22
Dean's List (Clemson University 3.5 GPA) Fall '20 and Spring '22
President's List (Clemson University 4.0 GPA) Fall '19, Spring '20, and Fall '22