The research focus of Parallel Computing and Data Science (PCDS) Lab is at the intersection of high performance computing and real-world applications, especially in computational biology. We are particularly interested in High Performance Computing (HPC) solutions to Big Data problems in high-throughput proteomics and genomics using variety of high-performance architectures and algorithms. More info about the lab can be found at http://www.saeedfahad.com
Reports from 2018
Similarity based classification of ADHD using Singular Value Decomposition, Taban Eslami and Fahad Saeed
Reports from 2017
An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics, Muaaz Awan and Fahad Saeed
GPU-PCC: A GPU Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Big fMRI Data, Taban Eslami, Muaaz Gul Awan, and Fahad Saeed
Power-Efficient and Highly Scalable Parallel Graph Sampling using FPGAs, Usman Tariq, Umer Cheema, and Fahad Saeed
A Hybrid MPI-OpenMP Strategy to Speedup the Compression of Big Next-Generation Sequencing Datasets, Sandino Vargas-Perez and Fahad Saeed
Scalable Data Structure to Compress Next-Generation Sequencing Files and its Application to Compressive Genomics, Sandino Vargas-Perez and Fahad Saeed
Reports from 2016
MS-REDUCE: An ultrafast technique for reduction of Big Mass Spectrometry Data for high-throughput processing, Muaaz Gul Awan and Fahad Saeed
GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays, Muaaz Awan and Fahad Saeed
Reports from 2015
Design and Implementation of Network Transfer Protocol for Big Genomic Data, Mohammed Aledhari and Fahad Saeed
Big Data Proteogenomics and High Performance Computing: Challenges and Opportunities, Fahad Saeed
A Parallel Algorithm for Compression of Big Next-Generation Sequencing Datasets, Sandino N. Vargas Perez and Fahad Saeed