Muaaz Gul Awan
Computer Systems Engineer 4
National Energy Research Scientific Computing Center (NERSC)
Science Engagement & Workflows Dept.
Application Performance Group
Muaaz Awan is an application performance specialist at NERSC. His expertise includes bioinformatics software development, GPU porting, optimization and performance analysis. Currently, he is associated with the ExaBiome project, contributing as a GPU application developer in the metagenomics analysis software pipelines. Previously, he has worked as a postdoc scholar at NERSC, Lawrence Berkeley National Laboratory (Berkeley Lab), and as a GPU application developer at EMSL, Pacific Northwest National Lab (PNNL). He received his PhD in Computer Science from Western Michigan University under the supervision of Prof. Fahad Saeed in 2019. His doctoral thesis explored high performance computing strategies for LC-MS/MS-based proteomics workflows.
Education
PhD - Western Michigan University, United States
B.Sc. - University of Engineering and Technology Lahore, Pakistan
Recent Publications
Evaluating the potential of disaggregated memory systems for HPC applications
Authors: Ding, N; Maris, P; Nam, HA; Groves, T; Awan, MG; Lindsey, L
August 2024, Concurrency Computation Practice and Experience
MATLAB package for discrete dipole approximation by graphics processing unit: Fast Fourier Transform and Biconjugate Gradient
Authors: Shabaninezhad, M; Awan, MG; Ramakrishna, G
March 2021, Journal of Quantitative Spectroscopy and Radiative Transfer
Benchmarking mass spectrometry based proteomics algorithms using a simulated database.
Authors: Awan, M; Awan, A; Saeed, F
January 2021, Network Modeling Analysis in Health Informatics and Bioinformatics
ADEPT: a domain independent sequence alignment strategy for gpu architectures
Authors: Awan, MG; Deslippe, J; Buluc, A; Selvitopi, O; Hofmeyr, S; Oliker, L
December 2020, BMC Bioinformatics
Evolving to Find Optimizations Humans Miss: Using Evolutionary Computation to Improve GPU Code for Bioinformatics Applications
Authors: Liou, J-Y; Awan, M; Leyba, K; Šulc, P; Hofmeyr, S; Wu, C-J
December 2024, ACM Transactions on Evolutionary Learning and Optimization
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