"Design of self-adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution",
Concurrency and Computation: Practice and Experience, vol. 31, issue 4: Wiley, 02/2019.
Download: ccpe2018ravi.pdf (1.67 MB)
"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)
"A Comparative Study of Methods for Measurement of Energy of Computing",
Energies, vol. 12, issue 11: MDPI, pp. 42, 06/2019.
"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)
"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)
"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)
"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)
"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)
"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)
"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.
"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)
"Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Dynamic Energy Through Workload Distribution",
17th Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HeteroPar 2019), Gottingen, Germany, Lecture Notes in Computer Science, vol. 11997, Springer, 08/2019, 2020.
Download: Khaleghzadeh2020_Chapter_OptimizationOfData-ParallelApp.pdf (692.31 KB)
"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)
"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)
"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)
"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)
"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)
"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)
"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)