Publications
Asynchronous SGD for DNN Training on Shared-Memory Parallel Architectures,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-04: University of Tennessee, Knoxville, March 2020.
(188.51 KB)
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heFFTe: Highly Efficient FFT for Exascale,”
International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, June 2020.
DOI: 10.1007/978-3-030-50371-0_19
(2.62 MB)
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High-Order Finite Element Method using Standard and Device-Level Batch GEMM on GPUs,”
2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA): IEEE, November 2020.
(1.3 MB)
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Reducing the Amount of out-of-core Data Access for GPU-Accelerated Randomized SVD,”
Concurrency and Computation: Practice and Experience, April 2020.
DOI: 10.1002/cpe.5754
(1.43 MB)
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GPUDirect MPI Communications and Optimizations to Accelerate FFTs on Exascale Systems,”
EuroMPI'19 Posters, Zurich, Switzerland, no. icl-ut-19-06: ICL, September 2019.
(2.25 MB)
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Parallel Selection on GPUs,”
Parallel Computing, vol. 91, March 2020, 2019.
DOI: 10.1016/j.parco.2019.102588
(1.43 MB)
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Accelerating the SVD Two Stage Bidiagonal Reduction and Divide and Conquer Using GPUs,”
Parallel Computing, vol. 74, pp. 3–18, May 2018.
DOI: 10.1016/j.parco.2017.10.004
(1.34 MB)
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Computational Benefit of GPU Optimization for Atmospheric Chemistry Modeling,”
Journal of Advances in Modeling Earth Systems, vol. 10, issue 8, pp. 1952–1969, August 2018.
DOI: 10.1029/2018MS001276
(3.4 MB)
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Optimization and Performance Evaluation of the IDR Iterative Krylov Solver on GPUs,”
The International Journal of High Performance Computing Applications, vol. 32, no. 2, pp. 220–230, March 2018.
DOI: 10.1177/1094342016646844
(2.08 MB)
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Preconditioned Krylov Solvers on GPUs,”
Parallel Computing, June 2017.
DOI: 10.1016/j.parco.2017.05.006
(1.19 MB)
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Efficiency of General Krylov Methods on GPUs – An Experimental Study,”
2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 683-691, May 2016.
DOI: 10.1109/IPDPSW.2016.45
“Efficiency of General Krylov Methods on GPUs – An Experimental Study,”
The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), Chicago, IL, IEEE, May 2016.
DOI: 10.1109/IPDPSW.2016.45
(285.28 KB)
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GPU-Aware Non-contiguous Data Movement In Open MPI,”
25th International Symposium on High-Performance Parallel and Distributed Computing (HPDC'16), Kyoto, Japan, ACM, June 2016.
DOI: http://dx.doi.org/10.1145/2907294.2907317
(482.32 KB)
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High-Performance Tensor Contractions for GPUs,”
International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.
(2.36 MB)
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Hierarchical DAG scheduling for Hybrid Distributed Systems,”
29th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Hyderabad, India, IEEE, May 2015.
(1.11 MB)
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Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems,”
Supercomputing Frontiers and Innovations, vol. 2, no. 4, October 2015.
DOI: 10.14529/jsfi1504
(3.68 MB)
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A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks,”
International Journal of High Performance Computing Applications, vol. 28, issue 2, pp. 196-209, May 2014.
DOI: 10.1177/1094342013502097
(1.74 MB)
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Taking Advantage of Hybrid Systems for Sparse Direct Solvers via Task-Based Runtimes,”
23rd International Heterogeneity in Computing Workshop, IPDPS 2014, Phoenix, AZ, IEEE, May 2014.
(807.33 KB)
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LU Factorization with Partial Pivoting for a Multicore System with Accelerators,”
IEEE Transactions on Parallel and Distributed Computing, vol. 24, issue 8, pp. 1613-1621, August 2013.
DOI: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.242
(1.08 MB)
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Soft Error Resilient QR Factorization for Hybrid System with GPGPU,”
Journal of Computational Science, vol. 4, issue 6, pp. 457–464, November 2013.
DOI: http://dx.doi.org/10.1016/j.jocs.2013.01.004
(995.45 KB)
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