]
"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)
"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 (1).pdf (5.79 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)
"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)
"Recent Advances in Matrix Partitioning for Parallel Computing on Heterogeneous Platforms",
IEEE Transactions on Parallel and Distributed Systems, vol. 30, issue 1: IEEE, pp. 218-229, 01/2019.
Download: recent-advances-matrix.pdf (2.77 MB)
"A Survey of Communication Performance Models for High-Performance Computing",
ACM Computing Surveys, vol. 51, issue 6: ACM, 01/2019.
"Performance Optimization of Multithreaded 2D FFT on Multicore Processors: Challenges and Solution Approaches",
IEEE 25th International Conference on High Performance Computing Workshops (HiPCW), Bengaluru, India, IEEE, pp. 8-17, 17-20 Dec, 2018.
Download: paper.pdf (1.33 MB)
"Parallel Data Partitioning Algorithms for Optimization of Data-Parallel Applications on Modern Extreme-Scale Multicore Platforms for Performance and Energy",
IEEE Access, vol. 6: IEEE, pp. 69075-69106, 11/2018.
Download: IEEEAccess2018PDPA.pdf (3.04 MB)
"A Novel Data-Partitioning Algorithm for Performance Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms",
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 10: IEEE, pp. 2176-2190, 10/2018.
Download: paper_r2.pdf (1.49 MB); tpds2018hpoptasuppl.pdf (3.4 MB)
