Muhammad Fahad

Orcid: 0000-0002-3595-8484

Affiliations:
  • University College Dublin, Ireland


According to our database1, Muhammad Fahad authored at least 12 papers between 2017 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Bibliography

2021
Bi-Objective Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Performance and Energy Through Workload Distribution.
IEEE Trans. Parallel Distributed Syst., 2021

Improving the accuracy of energy predictive models for multicore CPUs by combining utilization and performance events model variables.
J. Parallel Distributed Comput., 2021

Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors.
IEEE Access, 2021

2020
A novel data partitioning algorithm for dynamic energy optimization on heterogeneous high-performance computing platforms.
Concurr. Comput. Pract. Exp., 2020

A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs.
IEEE Access, 2020

Accurate Energy Modelling of Hybrid Parallel Applications on Modern Heterogeneous Computing Platforms Using System-Level Measurements.
IEEE Access, 2020

2019
Bi-objective Optimisation of Data-parallel Applications on Heterogeneous Platforms for Performance and Energy via Workload Distribution.
CoRR, 2019

Energy of Computing on Multicore CPUs: Predictive Models and Energy Conservation Law.
CoRR, 2019

Improving the Accuracy of Energy Predictive Models for Multicore CPUs Using Additivity of Performance Monitoring Counters.
Proceedings of the Parallel Computing Technologies, 2019

Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Dynamic Energy Through Workload Distribution.
Proceedings of the Euro-Par 2019: Parallel Processing Workshops, 2019

2018
How Pre-multicore Methods and Algorithms Perform in Multicore Era.
Proceedings of the High Performance Computing, 2018

2017
Additivity: A Selection Criterion for Performance Events for Reliable Energy Predictive Modeling.
Supercomput. Front. Innov., 2017


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