Publications
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Filters: Author is Azzam Haidar [Clear All Filters]
Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,”
Submitted to Concurrency and Computations: Practice and Experience, November 2010.
(1.65 MB)
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Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,”
University of Tennessee Computer Science Technical Report, UT-CS-11-666, (also Lawn 243), March 2011.
(1.65 MB)
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Parallel Reduction to Condensed Forms for Symmetric Eigenvalue Problems using Aggregated Fine-Grained and Memory-Aware Kernels,”
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC11), Seattle, WA, November 2011.
(636.01 KB)
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An Improved Parallel Singular Value Algorithm and Its Implementation for Multicore Hardware,”
University of Tennessee Computer Science Technical Report (also LAWN 283), no. ut-eecs-13-720: University of Tennessee, October 2013.
(1.23 MB)
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Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi,”
2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Best Paper Finalist, Waltham, MA, IEEE, September 2017.
DOI: 10.1109/HPEC.2017.8091085
(908.84 KB)
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Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers,”
ScalA17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Denver, CO, ACM.
(766.35 KB)
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Out of Memory SVD Solver for Big Data,”
2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Waltham, MA, IEEE, September 2017.
(1.33 MB)
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High-performance Cholesky Factorization for GPU-only Execution,”
Proceedings of the General Purpose GPUs (GPGPU-10), Austin, TX, ACM, February 2017.
DOI: 10.1145/3038228.3038237
(872.18 KB)
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Investigating Power Capping toward Energy-Efficient Scientific Applications,”
Concurrency Computation: Practice and Experience, vol. 2018, issue e4485, pp. 1-14, April 2018.
DOI: 10.1002/cpe.4485
(1.2 MB)
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A Guide for Achieving High Performance with Very Small Matrices on GPUs: A Case Study of Batched LU and Cholesky Factorizations,”
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 5, pp. 973–984, May 2018.
DOI: 10.1109/TPDS.2017.2783929
(832.92 KB)
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The Design of Fast and Energy-Efficient Linear Solvers: On the Potential of Half-Precision Arithmetic and Iterative Refinement Techniques,”
International Conference on Computational Science (ICCS 2018), vol. 10860, Wuxi, China, Springer, pp. 586–600, June 2018.
DOI: 10.1007/978-3-319-93698-7_45
(487.88 KB)
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Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers,”
The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Dallas, TX, IEEE, November 2018.
DOI: 10.1109/SC.2018.00050
(642.51 KB)
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Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption,”
ISC High Performance (ISC'18), Best Poster, Frankfurt, Germany, June 2018.
(3.01 MB)
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Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption
, Frankfurt, Germany, ISC High Performance (ISC18), Best Poster Award, June 2018.
(3.01 MB)

MAtrix, TEnsor, and Deep-learning Optimized Routines (MATEDOR)
, Washington, DC, NSF PI Meeting, Poster, April 2018.
DOI: 10.6084/m9.figshare.6174143.v3
(2.4 MB)

Harnessing GPU's Tensor Cores Fast FP16 Arithmetic to Speedup Mixed-Precision Iterative Refinement Solvers and Achieve 74 Gflops/Watt on Nvidia V100
, San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
(2.96 MB)

Power-Aware HPC on Intel Xeon Phi KNL Processors
, Frankfurt, Germany, ISC High Performance (ISC17), Intel Booth Presentation, June 2017.
(5.87 MB)

Accelerating Tensor Contractions for High-Order FEM on CPUs, GPUs, and KNLs
, Gatlinburg, TN, moky Mountains Computational Sciences and Engineering Conference (SMC16), Poster, September 2016.
(4.29 MB)

Mixed-Precision Solution of Linear Systems Using Accelerator-Based Computing,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-05: University of Tennessee, May 2020.
(1.03 MB)
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Mixed-Precision Iterative Refinement using Tensor Cores on GPUs to Accelerate Solution of Linear Systems,”
Proceedings of the Royal Society A, vol. 476, issue 2243, November 2020.
DOI: 10.1098/rspa.2020.0110
(2.24 MB)
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