Khalid Ali Almarhabi

About

Khalid Ali Almarhabi is a researcher at the forefront of high-performance computing (HPC) and machine learning, with a focus on optimizing sparse linear algebra operations. His most cited work, "AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR" (2022, 11 citations), addresses the critical challenge of the sparse matrix–vector product (SpMV)—one of the "seven dwarfs" of numerical methods essential for large-scale scientific and analytical applications. Almarhabi’s key contribution lies in developing a machine learning-driven framework that automatically selects and optimizes the Block Compressed Sparse Row (CSR) format for parallel SpMV computations, significantly boosting performance on modern architectures. This work bridges the gap between algorithmic efficiency and real-world HPC demands, offering a practical tool for solving large sparse linear systems. With growing citation impact, Almarhabi’s research is shaping the future of performance optimization in scientific computing, making him a notable figure in the intersection of machine learning and parallel computing.

Research Focus

Key Achievements

Top Papers

1. 1
   AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR

   11 citations · 2022

Key Collaborators

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