Publications

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Luszczek, P., J. Kurzak, I. Yamazaki, and J. Dongarra, “Towards Numerical Benchmark for Half-Precision Floating Point Arithmetic,” 2017 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, IEEE, September 2017. DOI: 10.1109/HPEC.2017.8091031 (1.67 MB)
Luszczek, P., and C. Brown, “Surrogate ML/AI Model Benchmarking for FAIR Principles' Conformance,” 2022 IEEE High Performance Extreme Computing Conference (HPEC): IEEE, September 2022. DOI: 10.1109/HPEC55821.2022.9926401
Luszczek, P., “Parallel Programming in MATLAB,” The International Journal of High Performance Computing Applications, vol. 23, no. 3, pp. 277-283, July 2009.  (215.71 KB)
Luszczek, P., I. Yamazaki, and J. Dongarra, “Increasing Accuracy of Iterative Refinement in Limited Floating-Point Arithmetic on Half-Precision Accelerators,” IEEE High Performance Extreme Computing Conference (HPEC 2019), Best Paper Finalist, Waltham, MA, IEEE, September 2019.  (470.21 KB)
Luszczek, P., and J. Dongarra, The PLASMA Library on CORAL Systems and Beyond (Poster) , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.  (550.86 KB)
Luszczek, P., J. Kurzak, I. Yamazaki, D. Keffer, V. Maroulas, and J. Dongarra, “Autotuning Techniques for Performance-Portable Point Set Registration in 3D,” Supercomputing Frontiers and Innovations, vol. 5, no. 4, December 2018. DOI: 10.14529/jsfi180404 (720.15 KB)
Luszczek, P., Y. Tsai, N. Lindquist, H. Anzt, and J. Dongarra, “Scalable Data Generation for Evaluating Mixed-Precision Solvers,” 2020 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, IEEE, September 2020. DOI: 10.1109/HPEC43674.2020.9286145 (1.3 MB)
Luszczek, P., J. Kurzak, I. Yamazaki, D. Keffer, and J. Dongarra, “Scaling Point Set Registration in 3D Across Thread Counts on Multicore and Hardware Accelerator Platforms through Autotuning for Large Scale Analysis of Scientific Point Clouds,” IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2017), Boston, MA, IEEE, December 2017. DOI: 10.1109/BigData.2017.8258258 (6.71 MB)