Hello World! I'm David Krasowska.
david:~$ summary
David Krasowska is a Ph.D. candidate at Northwestern University, advised by Dr. Peter Dinda. His research
journey began during his undergraduate studies at Clemson University where he collaborated with Argonne
National Laboratory to explore lossy compression for optimizations in HPC scientific applications. He received the
DOE Computational Science Graduate Fellowship to fund his graduate studies. Currently, he is exploring
scheduling applications across distributed heterogeneous systems with Dr. Pat McCormick and Dr. Li Tang at Los
Alamos National Laboratory.
david:~$ resume
david:~$ location
Chicago, IL
david:~$ education
Northwestern University (2023-Current) | Computer Science Ph.D. Student
Clemson University (2019-2022) | Computer Engineering BS | GPA: 3.7
Clemson University (2019-2022) | Computer Engineering BS | GPA: 3.7
david:~$ interests
Running. Biking. Hiking. Outdoors. Coding. Friendship. Achievement.
david:~$ email
david:~$ number
+1 (843) 283-7758
Experience
Visiting Student
Los Alamos National Laboratory
June 2024 - August 2024
(Current) Task based heterogeneous runtime research for UPMEM processing in memory (PIM).
Adding backend support for UPMEM into Legion.
In addition, working on automated dynamic application kernel mappings for distributed heterogeneous systems.
Research Assistant
Argonne National Laboratory
June 2022 - June 2023
Continuation of prior work at Clemson University to create black-box regression model with 3D dataset capabilities.
Led to multiple publications and awards.
Contributed to Libpressio, an Argonne library for compression.
Undergraduate Student Researcher
Clemson University
May 2021 - May 2022
Lossy compression research with collaboration Argonne National Laboratory and Clemson University FTHPC using the Palmetto Cluster.
Analyzing statistical correlations within datasets in comparison to compression performance. First publication: DRBSD-7.
Projects
High Performance Computing Creative Inquiry
Clemson University
June 2021 - December 2022
Participant in the Student Cluster Competition (SCC) at SC '21 and INDY SCC at SC '22.
Collaboration with Dell and Intel to build a cluster with greatest performance per watt.
Set up a distributed cluster with package managers, applications, and benchmarks. Gained knowledge of parallel computing with MPI.
Peer-Reviewed Publications
2023
PDF
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/CLUSTER52292.2023.00028
2023
PDF
R. 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
Unix
MPI
CUDA
C
C++
Python
LLVM
VHDL
Databases
RISC-V