]
"Efficient and accurate selection of optimal MPI collective algorithms using analytical performance modelling",
School of Computer Science, Dublin, University College Dublin, pp. 130, 06/2021.
Download: thesis.pdf (2.21 MB)
"Improving the accuracy of energy predictive models for multicore CPUs by combining utilization and performance events model variables",
Journal of Parallel and Distributed Computing, vol. 151: Elsevier, pp. 38-51, 05/2021.
Download: jpdc-2021-151.pdf (1.4 MB)
"Energy Predictive Models of Computing: Theory, Practical Implications and Experimental Analysis on Multicore Processors",
IEEE Access, vol. 9: IEEE, pp. 63149 - 63172, 04/2021.
Download: IEEE_Access_2021_Energy_theory.pdf (2.11 MB)
"Bi-Objective Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Performance and Energy Through Workload Distribution",
IEEE Transactions on Parallel and Distributed Systems, vol. 32, issue 3: IEEE, pp. 543-560, 03/2021.
Download: tpds-2021-32-3-09207974.pdf (1.58 MB)
"Towards Optimal Matrix Partitioning for Data Parallel Computing on a Hybrid Heterogeneous Server",
IEEE Access, vol. 9: IEEE, pp. 17229 - 17244, 02/2021.
Download: IEEE-Access-09328411.pdf (3.76 MB)
"Optimal Matrix Partitioning for Data Parallel Computing on Hybrid Heterogeneous Platforms",
19th International Symposium on Parallel and Distributed Computing (ISPDC), Warsaw, Poland, IEEE, 5-8 July, 2020.
Download: ispdc2020.pdf (367.16 KB)
"Accurate Component-level Energy Modelling of Parallel Applications on Modern Heterogeneous Hybrid Computing Platforms using System-level Measurements",
School of Computer Science, Dublin, University College Dublin, pp. 198, 12/2020.
Download: thesis-fahad.pdf (3.49 MB)
"A Comparative Study of Techniques for Energy Predictive Modeling Using Performance Monitoring Counters on Modern Multicore CPUs",
IEEE Access, vol. 8: IEEE, pp. 143306 - 143332, 08/2020.
Download: IEEE-Access-09154439.pdf (2.37 MB)
"A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms",
Energies, vol. 13, issue 15: MDPI, pp. 22, 08/2020.
Download: energies-13-03944.pdf (4.08 MB); supplemental.pdf (188.52 KB)
"Towards Reliable and Accurate Energy Predictive Modelling using Performance Events on Modern Computing Platforms",
School of Computer Science, Dublin, University College Dublin, pp. 237, 08/2020.
Download: [[Final]] Towards Reliable and Accurate Energy Predictive Modelling using Performance Events on Modern Computing Platforms.pdf (2.37 MB)
"Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Dynamic Energy Through Workload Distribution",
Euro-Par 2019 Workshops, Lecture Notes in Computer Science, vol. 11997, Gottingen, Germany, Springer, 08/2019, 2020.
Download: Khaleghzadeh2020_Chapter_OptimizationOfData-ParallelApp.pdf (692.31 KB)
"A novel data partitioning algorithm for dynamic energy optimization on heterogeneous high-performance computing platforms",
Concurrency and Computation: Practice and Experience, vol. 33, issue 21: Wiley, pp. e5928, 07/2020.
Download: CCPE-2020-dynamic-energy.pdf (1.34 MB)
"Accurate Energy Modelling of Hybrid Parallel Applications on Modern Heterogeneous Computing Platforms using System-Level Measurements",
IEEE Access, vol. 8, pp. 93793 - 93829, 06/2020.
Download: 09094309.pdf (2.89 MB)
"Multicore processor computing is not energy proportional: An opportunity for bi-objective optimization for energy and performance",
Applied Energy, vol. 268, pp. 18, 06/2020.
Download: paper_r2.pdf (1.38 MB)
"A tool to assess the communication cost of parallel kernels on heterogeneous platforms",
The Journal of Supercomputing, vol. 76: Springer, pp. 4629–4644, 06/2020.
"The 27th International Heterogeneity in Computing Workshop and the 16th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms",
Concurrency and Computation: Practice and Experience, vol. 32, issue 15: Wiley, pp. 3, 03/2020.
Download: cpe.5736.pdf (169.99 KB)
"A Hierarchical Data-Partitioning Algorithm for Performance Optimization of Data-Parallel Applications on Heterogeneous Multi-Accelerator NUMA Nodes",
IEEE Access, vol. 8: IEEE, pp. 7861 - 7876, 01/2020.
Download: 08933138.pdf (3.4 MB)
"How Pre-multicore Methods and Algorithms Perform in Multicore Era",
High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science, vol 11203, Frankfurt, Springer Nature, pp. 527-539, 24-26 June, 2018, 2019.
Download: nesus-isc-paper.pdf (574.34 KB)
"SummaGen: Parallel Matrix-Matrix Multiplication Based on Non-rectangular Partitions for Heterogeneous HPC Platforms",
28th Heterogeneity in Computing Workshop (HCW 2019), Rio de Janeiro, Brazil, IEEE, 20/05/2019.
Download: hcw2019.pdf (673.25 KB)
"Optimization of Multithreaded Data-parallel Applications on Modern Multicore CPUs For Performance and Energy Using Application-level Decision Variables",
School of Computer Science, Dublin, University College Dublin, pp. 181, 09/2019.
Download: PhD_Thesis_Semyon_Khokhriakov.pdf (8.7 MB); thesis-summary.pdf (621.98 KB)
"Improving the Accuracy of Energy Predictive Models for Multicore CPUs Using Additivity of Performance Monitoring Counters",
15th International Conference on Parallel Computing Technologies (PaCT-2019), Almaty, Kazakhstan, Lecture Notes in Computer Science 11657, Springer, pp. 51-66, 08/2019.
Download: PaCT2019.pdf (370.4 KB)
"A Comparative Study of Methods for Measurement of Energy of Computing",
Energies, vol. 12, issue 11: MDPI, pp. 42, 06/2019.
"Energy aware ultrascale systems",
Ultrascale computing systems: IET, 03/2019.
Download: chap5.pdf (3.2 MB)
"Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms",
School of Computer Science, Dublin, University College Dublin, pp. 264, 03/2019.
Download: thesis-hamid.pdf (5.9 MB)
"Programming models and runtimes",
Ultrascale computing systems: IET, 03/2019.
Download: nesus-book-chap2.pdf (3.84 MB); nesus-book-chap2-summary.pdf (123.29 KB)
