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Muaaz Awan

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Muaaz Gul Awan, Ph.D.
Application Performance Specialist
National Energy Research Scientific Computing Center (NERSC)
Lawrence Berkeley National Laboratory
1 Cyclotron Road
Bldg. 59, Room 4024N
Berkeley, California 94720 US

Biographical Sketch

Muaaz Awan is an application performance specialist at NERSC, his expertise include bioinformatics software development, GPU porting, optimization and performance analysis. Currently he is associated with the ExaBiome project where he contributes as a GPU application developer in the metagenomics analysis software pipelines. Previously, he has worked as a postdoc scholar at NERSC, LBNL (Lawrence Berkeley National Lab) and as GPU application developer at EMSL, PNNL (Pacific Northwest National Lab). 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. 


  • Ph.D., Computer Science, Western Michigan University, Kalamazoo, MI
  • B.Sc., Electrical Engineering, University of Engineering and Technology, Lahore


Journal Publications (peer-reviewed)

  1. Awan, M.G., Deslippe, J., Buluc, A. et al. "ADEPT: a domain independent sequence alignment strategy for gpu architectures"BMC Bioinformatics 21, 406 (2020).
  2. Yelick, Katherine, Aydın Buluç, Muaaz G. Awan, Ariful Azad, Benjamin Brock, Rob Egan, Saliya Ekanayake et al. "The parallelism motifs of genomic data analysis." Philosophical Transactions of the Royal Society A 378, no. 2166 (2020): 20190394
  3. Muaaz G. Awan and Fahad Saeed, "MaSS-Simulator: A highly con configurable simulator for generating MS/MS datasets for benchmarking of proteomics algorithms", Wiley Proteomics, September 2018
  4. Muaaz G. Awan, Taban Eslami and Fahad Saeed, "GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data", Elsevier Computers in Biology and Medicine, August 2018  Elsevier
  5. Muaaz G. Awan and Fahad Saeed, "MS-REDUCE: An ultrafast technique for reduction of Big Mass Spectrometry Data for high-throughput processing", Oxford Bioinformatics, Jan 2016 Tech Report | PubMed | Oxford

 Conference publications (peer-reviewed)

  1. Muaaz G. Awan and Fahad Saeed, "An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics", Proceedings of ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Boston MA, August 2017 Tech Report
  2. Taban Eslami, Muaaz G. Awan and Fahad Saeed, "GPU-PCC: A GPU based technique to compute pairwise Pearson's Correlation Coefficients for big fMRI data", Workshop on Parallel and Cloud-based Bioinformatics and Biomedicine (ParBio), Proceedings of ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Boston MA, August 2017 Tech Report
  3. Muaaz G. Awan and Fahad Saeed, "GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays", Proceedings of Workshop on High Performance Computing for Big Data, International Conference on Parallel Processing (ICPP-2016), Philadelphia PA, August 2016 Tech Report
  4. Muaaz G. Awan and Fahad Saeed, "On the sampling of Big Mass Spectrometry Data", Proceedings of Bioinformatics and Computational Biology (BICoB)Conference, Honolulu Hawaii, March 2015 Tech Report

Talks and Posters


  1. Accelerating biological sequence alignment using Graphics Processing Units, 2020, NERSC-CRD weekly seminar, Berkeley, California
  2. Perlmutter- A way point for ECP teams, 2020 Exascale Computing Project Annual Meeting, Houston, Texas.
  3. A GPU accelerated sequence alignment algorithm, 2019, Berkeley Lab CS postdoc symposium, Berkeley, California.
  4. Design of High-Performance Computational Techniques on Many Core Devices for LC-MS/MS Proteomics (2019), Lawrence Berkeley National Laboratory, Berkeley, California. 
  5. Enabling Massive Peptide Library Search Using GPU-FLASH, Cascadia Proteomics Symposium 2017, Seattle, Washington.


  1. Accelerating Metagenome analysis using Graphics Processing Units2020 Exascale Computing Project Annual Meeting, Houston, Texas.