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)
(188.51 KB)
“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)
(2.62 MB)
“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)
(1.43 MB)
“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)
(2.25 MB)
“Parallel Selection on GPUs,”
Parallel Computing, November 2019.
DOI: 10.1016/j.parco.2019.102588 (1.43 MB)
(1.43 MB)
“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)
(1.34 MB)
“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)
(3.4 MB)
“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)
(2.08 MB)
“Preconditioned Krylov Solvers on GPUs,”
Parallel Computing, June 2017.
DOI: 10.1016/j.parco.2017.05.006 (1.19 MB)
(1.19 MB)
“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)
(285.28 KB)
“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)
(482.32 KB)
“High-Performance Tensor Contractions for GPUs,”
International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.
(2.36 MB)
(2.36 MB)
“Hierarchical DAG scheduling for Hybrid Distributed Systems,”
29th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Hyderabad, India, IEEE, May 2015.
(1.11 MB)
(1.11 MB)
“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)
(3.68 MB)
“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)
(1.74 MB)
“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)
(807.33 KB)
“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)
(1.08 MB)
“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)
(995.45 KB)
