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Paul D. Hovland
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2020 – today
- 2025
- [j11]Shreyas Sunil Gaikwad
, Sri Hari Krishna Narayanan, Laurent Hascoët, Jean-Michel Campin, Helen Pillar, An Nguyen, Jan Hückelheim, Paul D. Hovland, Patrick Heimbach:
MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade. Future Gener. Comput. Syst. 163: 107512 (2025) - 2024
- [c62]Peiyi Li, Ji Liu, Alvin Gonzales, Zain H. Saleem, Huiyang Zhou, Paul D. Hovland:
QuTracer: Mitigating Quantum Gate and Measurement Errors by Tracing Subsets of Qubits. ISCA 2024: 103-117 - [i18]Thomas Randall, Jaehoon Koo, Brice Videau, Michael Kruse, Xingfu Wu
Transfer-Learning-Based Autotuning Using Gaussian Copula. CoRR abs/2401.04669 (2024) - [i17]Xingfu Wu
Integrating ytopt and libEnsemble to Autotune OpenMC. CoRR abs/2402.09222 (2024) - [i16]Paul D. Hovland, Jan Hückelheim:
Differentiating Through Linear Solvers. CoRR abs/2404.17039 (2024) - [i15]Ji Liu, Alvin Gonzales, Benchen Huang, Zain H. Saleem, Paul D. Hovland:
QuCLEAR: Clifford Extraction and Absorption for Significant Reduction in Quantum Circuit Size. CoRR abs/2408.13316 (2024) - 2023
- [c61]Wenhao Wu
Model Checking Race-Freedom When "Sequential Consistency for Data-Race-Free Programs" is Guaranteed. CAV (2) 2023: 265-287 - [c60]Peiyi Li, Ji Liu
Enhancing Virtual Distillation with Circuit Cutting for Quantum Error Mitigation. ICCD 2023: 94-101 - [c59]Xinpeng Li, Ji Liu, Ethan H. Hansen, Shuai Xu, Paul D. Hovland, Vipin Chaudhary:
Accelerating VQE Algorithm via Parameters and Measurement Reuse. ICRC 2023: 1-5 - [c58]Thomas Randall
Transfer-learning-based Autotuning using Gaussian Copula. ICS 2023: 37-49 - [c57]Ji Liu
QContext: Context-Aware Decomposition for Quantum Gates. ISCAS 2023: 1-5 - [c56]Ji Liu
Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis. QCE 2023: 745-756 - [i14]Ji Liu, Max Bowman, Pranav Gokhale, Siddharth Dangwal, Jeffrey Larson, Frederic T. Chong, Paul D. Hovland:
QContext: Context-Aware Decomposition for Quantum Gates. CoRR abs/2302.02003 (2023) - [i13]Xingfu Wu
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales. CoRR abs/2303.16245 (2023) - [i12]Ji Liu, Ed Younis, Mathias Weiden, Paul D. Hovland, John Kubiatowicz, Costin Iancu:
Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis. CoRR abs/2305.02939 (2023) - [i11]Jan Hückelheim, Harshitha Menon, William S. Moses, Bruce Christianson, Paul D. Hovland, Laurent Hascoët:
Understanding Automatic Differentiation Pitfalls. CoRR abs/2305.07546 (2023) - [i10]Wenhao Wu, Jan Hückelheim, Paul D. Hovland, Ziqing Luo, Stephen F. Siegel:
Model Checking Race-freedom When "Sequential Consistency for Data-race-free Programs" is Guaranteed. CoRR abs/2305.18198 (2023) - 2022
- [j10]Xingfu Wu
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization. Concurr. Comput. Pract. Exp. 34(20) (2022) - [c55]Sri Hari Krishna Narayanan, Thomas Propson, Marcelo Bongarti, Jan Hückelheim, Paul D. Hovland
Reducing Memory Requirements of Quantum Optimal Control. ICCS (4) 2022: 129-142 - [c54]Richard J. Clancy, Matt Menickelly, Jan Hückelheim, Paul D. Hovland
TROPHY: Trust Region Optimization Using a Precision Hierarchy. ICCS (1) 2022: 445-459 - [c53]William S. Moses, Sri Hari Krishna Narayanan
Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation. SC 2022: 60:1-60:18 - [c52]Robert Jackson, Rebecca Gjini, Sri Hari Krishna Narayanan, Matt Menickelly, Paul D. Hovland
Improving PyDDA's atmospheric wind retrievals using automatic differentiation and Augmented Lagrangian methods. SciPy 2022: 210-216 - [c51]Wenhao Wu
Verifying Fortran Programs with CIVL. TACAS (1) 2022: 106-124 - 2021
- [c50]Paul D. Hovland
Error Estimation and Correction Using the Forward CENA Method. ICCS (1) 2021: 765-778 - [c49]Jaehoon Koo
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. PMBS 2021: 82-93 - [i9]Xingfu Wu, Michael Kruse, Prasanna Balaprakash, Hal Finkel, Paul D. Hovland, Valerie Taylor, Mary W. Hall:
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version). CoRR abs/2104.13242 (2021) - [i8]Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul D. Hovland, Mary W. Hall:
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations. CoRR abs/2105.04555 (2021) - 2020
- [c48]Jan Hückelheim, Michel Schanen
Vector Forward Mode Automatic Differentiation on SIMD/SIMT architectures. ICPP 2020: 39:1-39:11 - [c47]Xingfu Wu
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. PMBS@SC 2020: 61-70 - [i7]Xingfu Wu, Michael Kruse
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization. CoRR abs/2010.08040 (2020)
2010 – 2019
- 2019
- [j9]Jan Hückelheim
Reverse-mode algorithmic differentiation of an OpenMP-parallel compressible flow solver. Int. J. High Perform. Comput. Appl. 33(1) (2019) - [c46]Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, Paul D. Hovland, Gerard Gorman:
Combining Checkpointing and Data Compression to Accelerate Adjoint-Based Optimization Problems. Euro-Par 2019: 87-100 - [c45]Jan Hückelheim, Navjot Kukreja, Sri Hari Krishna Narayanan
Automatic Differentiation for Adjoint Stencil Loops. ICPP 2019: 83:1-83:10 - [c44]Navjot Kukreja, Alena Shilova, Olivier Beaumont
Training on the Edge: The why and the how. IPDPS Workshops 2019: 899-903 - [i6]Navjot Kukreja, Alena Shilova, Olivier Beaumont, Jan Hückelheim, Nicola J. Ferrier, Paul D. Hovland, Gerard Gorman:
Training on the Edge: The why and the how. CoRR abs/1903.03051 (2019) - [i5]Jan Hückelheim, Navjot Kukreja, Sri Hari Krishna Narayanan, Fabio Luporini, Gerard Gorman, Paul D. Hovland:
Automatic Differentiation for Adjoint Stencil Loops. CoRR abs/1907.02818 (2019) - [i4]Joseph G. Wallwork, Paul D. Hovland, Hong Zhang, Oana Marin:
Computing Derivatives for PETSc Adjoint Solvers using Algorithmic Differentiation. CoRR abs/1909.02836 (2019) - 2018
- [j8]Jan Christian Hückelheim
Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation. Optim. Methods Softw. 33(4-6): 672-693 (2018) - [c43]Jan Hückelheim
Vectorised Computation of Diverging Ensembles. ICPP 2018: 51:1-51:10 - [c42]Jan Hückelheim
Verifying Properties of Differentiable Programs. SAS 2018: 205-222 - [i3]Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, Kaiyuan Hou, Fabio Luporini, Paul D. Hovland, Gerard Gorman:
Combining checkpointing and data compression for large scale seismic inversion. CoRR abs/1810.05268 (2018) - 2017
- [c41]Jan Hückelheim
Towards Self-Verification in Finite Difference Code Generation. CORRECTNESS@SC 2017: 42-49 - [i2]Ganesh Gopalakrishnan, Paul D. Hovland, Costin Iancu, Sriram Krishnamoorthy, Ignacio Laguna, Richard A. Lethin, Koushik Sen, Stephen F. Siegel, Armando Solar-Lezama:
Report of the HPC Correctness Summit, Jan 25-26, 2017, Washington, DC. CoRR abs/1705.07478 (2017) - 2016
- [j7]Guillaume Aupy, Julien Herrmann, Paul D. Hovland
Optimal Multistage Algorithm for Adjoint Computation. SIAM J. Sci. Comput. 38(3) (2016) - [c40]Amit Roy, Prasanna Balaprakash
Exploiting Performance Portability in Search Algorithms for Autotuning. IPDPS Workshops 2016: 1535-1544 - [c39]Mu Wang, Alex Pothen, Paul D. Hovland
Edge Pushing is Equivalent to Vertex Elimination for Computing Hessians. CSC 2016: 102-111 - [c38]Prasanna Balaprakash
AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. ISC 2016: 219-239 - 2015
- [c37]Florin Isaila, Prasanna Balaprakash
Collective I/O Tuning Using Analytical and Machine Learning Models. CLUSTER 2015: 128-137 - [c36]Azamat Mametjanov
Autotuning FPGA Design Parameters for Performance and Power. FCCM 2015: 84-91 - [c35]Thomas Nelson, Axel Rivera, Prasanna Balaprakash
Generating Efficient Tensor Contractions for GPUs. ICPP 2015: 969-978 - 2014
- [c34]Leonardo Arturo Bautista-Gomez
Energy-performance tradeoffs in multilevel checkpoint strategies. CLUSTER 2014: 278-279 - [c33]Prasanna Balaprakash
Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing. PMBS@SC 2014: 249-263 - 2013
- [c32]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland
Exascale workload characterization and architecture implications. ISPASS 2013: 120-121 - [c31]Prasanna Balaprakash
Empirical performance modeling of GPU kernels using active learning. PARCO 2013: 646-655 - [c30]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland, Boyana Norris:
Exascale workload characterization and architecture implications. SpringSim (HPC) 2013: 5 - [i1]Anshu Dubey, Steven R. Brandt, Richard C. Brower, M. Giles, Paul D. Hovland, Don Q. Lamb, Frank Löffler, Boyana Norris, Brian W. O'Shea, Claudio Rebbi, Marc Snir, Rajeev Thakur:
Software Abstractions and Methodologies for HPC Simulation Codes on Future Architectures. CoRR abs/1309.1780 (2013) - 2012
- [c29]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland
Abstract: An Exascale Workload Study. SC Companion 2012: 1463-1464 - [c28]Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, Sri Hari Krishna Narayanan, Andrew A. Chien, Paul D. Hovland
Poster: An Exascale Workload Study. SC Companion 2012: 1465 - [c27]Prasanna Balaprakash
An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. VECPAR 2012: 261-269 - 2011
- [c26]Sri Hari Krishna Narayanan
Sparse Jacobian Computation Using ADIC2 and ColPack. ICCS 2011: 2115-2123 - [c25]Prasanna Balaprakash
Can search algorithms save large-scale automatic performance tuning? ICCS 2011: 2136-2145 - 2010
- [c24]Sri Hari Krishna Narayanan
Generating Performance Bounds from Source Code. ICPP Workshops 2010: 197-206 - [c23]Jaewook Shin, Mary W. Hall
Speeding up Nek5000 with autotuning and specialization. ICS 2010: 253-262 - [p1]Jaewook Shin, Mary W. Hall
Autotuning and Specialization: Speeding up Matrix Multiply for Small Matrices with Compiler Technology. Software Automatic Tuning, From Concepts to State-of-the-Art Results 2010: 353-370
2000 – 2009
- 2009
- [c22]Ilya Safro, Paul D. Hovland, Jaewook Shin, Michelle Mills Strout:
Improving Random Walk Performance. CSC 2009: 108-112 - [c21]Michelle Mills Strout, Nissa Osheim, Dave Rostron, Paul D. Hovland
Evaluation of Hierarchical Mesh Reorderings. ICCS (1) 2009: 540-549 - [c20]Jean Utke, Laurent Hascoët, Patrick Heimbach
Toward adjoinable MPI. IPDPS 2009: 1-8 - [r1]Christian H. Bischof, Paul D. Hovland
Automatic Differentiation: Parallel Computation. Encyclopedia of Optimization 2009: 159-165 - 2008
- [j6]Christian H. Bischof, Paul D. Hovland
On the implementation of automatic differentiation tools. High. Order Symb. Comput. 21(3): 311-331 (2008) - [c19]Sanjukta Bhowmick, Paul D. Hovland
Improving the Performance of Graph Coloring Algorithms through Backtracking. ICCS (1) 2008: 873-882 - [c18]Uwe Naumann, Laurent Hascoët, Chris Hill, Paul D. Hovland
A Framework for Proving Correctness of Adjoint Message-Passing Programs. PVM/MPI 2008: 316-321 - 2007
- [c17]Jaewook Shin, Paul D. Hovland
Comparison of two activity analyses for automatic differentiation: context-sensitive flow-insensitive vs. context-insensitive flow-sensitive. SAC 2007: 1323-1329 - 2006
- [c16]Michelle Mills Strout, Paul D. Hovland
Linearity Analysis for Automatic Differentiation. International Conference on Computational Science (4) 2006: 574-581 - [c15]Barbara Kreaseck, Luis Ramos, Scott Easterday, Michelle Mills Strout, Paul D. Hovland
Hybrid Static/Dynamic Activity Analysis. International Conference on Computational Science (4) 2006: 582-590 - [c14]Michelle Mills Strout, Barbara Kreaseck, Paul D. Hovland
Data-Flow Analysis for MPI Programs. ICPP 2006: 175-184 - [c13]Paul D. Hovland
Term Graphs for Computing Derivatives in Imperative Languages. TERMGRAPH@ETAPS 2006: 99-111 - 2005
- [j5]Paul D. Hovland
Making automatic differentiation truly automatic: coupling PETSc with ADIC. Future Gener. Comput. Syst. 21(8): 1426-1438 (2005) - [c12]Michelle Mills Strout, John M. Mellor-Crummey
Representation-independent program analysis. PASTE 2005: 67-74 - 2004
- [c11]Michelle Mills Strout, Paul D. Hovland
Metrics and models for reordering transformations. Memory System Performance 2004: 23-34 - 2002
- [j4]Boyana Norris
Parallel components for PDEs and optimization: some issues and experiences. Parallel Comput. 28(12): 1811-1831 (2002) - [c10]Paul D. Hovland
Making Automatic Differentiation Truly Automatic: Coupling PETSc with ADIC. International Conference on Computational Science (2) 2002: 1087-1096 - [c9]Christian H. Bischof, Paul D. Hovland
Implementation of automatic differentiation tools. PEPM 2002: 98-107 - 2001
- [j3]Paul D. Hovland
Parallel simulation of compressible flow using automatic differentiation and PETSc. Parallel Comput. 27(4): 503-519 (2001) - [c8]Boyana Norris, Paul D. Hovland
A Distributed Application Server for Automatic Differentiation. IPDPS 2001: 176 - 2000
- [c7]Christian H. Bischof, H. Martin Bücker, Paul D. Hovland
On Combining Computational Differentiation and Toolkits for Parallel Scientific Computing. Euro-Par 2000: 86-94
1990 – 1999
- 1999
- [c6]Lori A. Freitag, William Gropp, Paul D. Hovland, Lois C. McInnes, Barry F. Smith:
Infrastructure and Interfaces for Large-Scale Numerical Software. PDPTA 1999: 2657-2664 - [c5]Paul D. Hovland, Lois C. McInnes, Barry F. Smith:
Solving Nonlinear PDEs Using PETSc and Automatic Differentiation. PP 1999 - 1998
- [c4]Paul D. Hovland
Automatic Differentiation for Message-Passing Parallel Programs. IPPS/SPDP 1998: 98-104 - 1997
- [b1]Paul D. Hovland:
Automatic Differentiation of Parallel Programs. University of Illinois Urbana-Champaign, USA, 1997 - [j2]Paul D. Hovland
Efficient Derivative Codes through Automatic Differentiation and Interface Contraction: An Application in Biostatistics. SIAM J. Sci. Comput. 18(4): 1056-1066 (1997) - [c3]Paul D. Hovland, Christian H. Bischof, Lucas Roh:
Automatic Differentiation of a Parallel Molecular Dynamics Application. PP 1997 - 1993
- [c2]William F. Punch III, Erik D. Goodman, Min Pei, Lai Chia-Shun, Paul D. Hovland, Richard J. Enbody:
Further Research on Feature Selection and Classification Using Genetic Algorithms. ICGA 1993: 557-564 - [c1]Paul D. Hovland
A Model for Automatic Dta Partitioning. ICPP (2) 1993: 251-259 - 1992
- [j1]Christian H. Bischof, Alan Carle, George F. Corliss, Andreas Griewank
ADIFOR - Generating Derivative Codes form Fortran Programs. Sci. Program. 1(1): 11-29 (1992)
Coauthor Index
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