Adel A. Bahadded
Papers
1
Total Citations
11
H-Index
1
About
Adel A. Bahaddad is a leading researcher in high-performance computing and machine learning optimization, with a focus on accelerating sparse matrix operations critical to scientific and engineering simulations. 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 sparse matrix–vector product (SpMV)—one of the "seven dwarfs" of numerical computing—by developing a machine learning framework that automatically selects optimal block CSR formats for parallel execution. This contribution significantly reduces the performance gap between theoretical peak and actual throughput in solving large sparse linear systems, impacting fields from computational fluid dynamics to network analysis. Bahaddad’s research bridges algorithmic innovation and practical deployment, offering tools that adapt to diverse hardware architectures. His work has been recognized for its potential to streamline high-performance computing workflows, making him a valuable contributor to the intersection of AI and numerical methods.
Research Focus
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Top Papers
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