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<table>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="KhaBal2020b">1</a>]
</td>
<td class="bibtexitem" align="justify">
S. Khairy and P. Balaprakash.
Challenges and opportunities for ai-enabled science applications over
5g.
In <em>5G Enabled Energy Innovation Workshop (5GEEIW)</em>, March 2020.
[ <a href="pbalapra-bibtex_bib.html#KhaBal2020b">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="KhaBal2020a">2</a>]
</td>
<td class="bibtexitem" align="justify">
S. Khairy and P. Balaprakash.
Edge intelligence meets cloud intelligence over 5g: Unmanned aerial
vehicle swarm for extremeenvironments.
In <em>5G Enabled Energy Innovation Workshop (5GEEIW)</em>, March 2020.
[ <a href="pbalapra-bibtex_bib.html#KhaBal2020a">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="osti_1606538">3</a>]
</td>
<td class="bibtexitem" align="justify">
P. Beckman, C. Catlett, M. Ahmed, M. Alawad, L. Bai, P. Balaprakash, K. Barker,
P. Beckman, R. Berry, A. Bhuyan, G. Brebner, K. Burkes, A. Butko,
F. Cappello, R. Chard, S. Collis, J. Cree, D. Dasgupta, A. Evdokimov, J. M.
Fields, P. Fuhr, C. Harper, Y. Jin, R. Kettimuthu, M. Kiran, R. Kozma, P. A.
Kumar, Y. Kumar, L. Luo, L. Mashayekhy, I. Monga, B. Nickless, T. Pappas,
E. Peterson, T. Pfeffer, S. Rakheja, V. R. Tribaldos, S. Rooke, S. Roy,
T. Saadawi, A. Sandy, R. Sankaran, N. Schwarz, S. Somnath, M. Stan,
C. Stuart, R. Sullivan, A. Sumant, G. Tchilinguirian, N. Tran, A. Veeramany,
A. Wang, B. Wang, A. Wiedlea, S. Wielandt, T. Windus, Y. Wu, X. Yang, Z. Yao,
R. Yu, Y. Zeng, and Y. Zhang.
5g enabled energy innovation: Advanced wireless networks for science,
workshop report.
2020.
[ <a href="pbalapra-bibtex_bib.html#osti_1606538">bib</a> |
<a href="http://dx.doi.org/10.2172/1606538">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="HeMicro2020">4</a>]
</td>
<td class="bibtexitem" align="justify">
Y. He, P. Balaprakash, and Y. Li.
FIdelity: Efficient Resilience Analysis Framework for Deep Learning
Accelerators.
In <em>53rd IEEE/ACM International Symposium on Microarchitecture
(MICRO)</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#HeMicro2020">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="madireddy2020multilayer">5</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, A. Yanguas-Gil, and P. Balaprakash.
Multilayer neuromodulated architectures for memory-constrained online
continual learning.
In <em>ICML Workshop on LifelongML</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#madireddy2020multilayer">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MallickTRB2020">6</a>]
</td>
<td class="bibtexitem" align="justify">
T. Mallick, P. Balaprakash, E. Rask, and J. Macfarlane.
Graph-partitioning-based diffusion convolutional recurrent neural
network for large-scale traffic forecasting.
<em>Transportation Research Record</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#MallickTRB2020">bib</a> ]
<blockquote><font size="-1">
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved state-of-the-art results in traffic forecasting by capturing the spatiotemporal dynamics of the traffic. Despite the promising results, however, applying DCRNNs for large highway networks still remains elusive because of computational and memory bottlenecks. This paper presents an approach for implementing a DCRNN for a large highway network that overcomes these limitations. This approach uses a graph-partitioning method to decompose a large highway network into smaller networks and trains them independently. The efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11,160 sensor locations is demonstrated. An overlapping-nodes approach for the graph-partitioning-based DCRNN is developed to include sensor locations from partitions that are geographically close to a given partition. Furthermore, it is demonstrated that the DCRNN model can be used to forecast the speed and flow simultaneously and that the forecasted values preserve fundamental traffic flow dynamics. This approach to developing DCRNN models that represent large highway networks can be a potential core capability in advanced highway traffic monitoring systems, where a trained DCRNN model forecasting traffic at all sensor locations can be used to adjust traffic management strategies proactively based on anticipated future conditions.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="maulik2020neural">7</a>]
</td>
<td class="bibtexitem" align="justify">
R. Maulik, N. A. Garland, J. W. Burby, X.-Z. Tang, and P. Balaprakash.
Neural network representability of fully ionized plasma fluid model
closures.
<em>Physics of Plasmas</em>, 27(7):072106, 2020.
[ <a href="pbalapra-bibtex_bib.html#maulik2020neural">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Maulik2020NonautoregressiveTM">8</a>]
</td>
<td class="bibtexitem" align="justify">
R. Maulik, B. Lusch, and P. Balaprakash.
Non-autoregressive time-series methods for stable parametric
reduced-order models.
<em>Physics of Fluids</em>, 32(8), 2020.
[ <a href="pbalapra-bibtex_bib.html#Maulik2020NonautoregressiveTM">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="KhaAAAI2020">9</a>]
</td>
<td class="bibtexitem" align="justify">
S. Khairy, R. Shaydulin, L. Cincio, Y. Alexeev, and P. Balaprakash.
Learning to optimize variational quantum circuits to solve
combinatorial problems.
In <em>AAAI Conference on Artificial Intelligence</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#KhaAAAI2020">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="KhaJSAC2020">10</a>]
</td>
<td class="bibtexitem" align="justify">
S. Khairy, P. Balaprakash, L. X. Cai, and Y. Cheng.
Constrained deep reinforcement learning for energy sustainable
multi-uav based random access IoT networks with NOMA.
<em>IEEE Journal on Selected Areas in Communications</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#KhaJSAC2020">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="IsaSC2020">11</a>]
</td>
<td class="bibtexitem" align="justify">
M. Isakov, E. Rosario, S. Madireddy, P. Balaprakash, P. Carns, R. Ross, and
M. Kinsy.
HPC I/O throughput bottleneck analysis with explainable local
models.
In <em>SC '20: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#IsaSC2020">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MauSC2020">12</a>]
</td>
<td class="bibtexitem" align="justify">
R. Maulik, R. Egele, B. Lusch, and P. Balaprakash.
Recurrent neural network architecture search for geophysical
emulation.
In <em>SC '20: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis</em>, 2020.
[ <a href="pbalapra-bibtex_bib.html#MauSC2020">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="maulik2019using">13</a>]
</td>
<td class="bibtexitem" align="justify">
R. Maulik, V. Rao, S. Madireddy, B. Lusch, and P. Balaprakash.
Using recurrent neural networks for nonlinear component computation
in advection-dominated reduced-order models.
In <em>NeurIPS Workshop on ML and the Physical Sciences</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#maulik2019using">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="maulik2019site">14</a>]
</td>
<td class="bibtexitem" align="justify">
R. Maulik and P. Balaprakash.
Site-specific graph neural network for predicting protonation energy
of oxygenate molecules.
In <em>NeurIPS Workshop on ML and the Physical Sciences</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#maulik2019site">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="madireddy2019modular">15</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, N. Li, N. Ramachandra, P. Balaprakash, and S. Habib.
Modular deep learning analysis of galaxy-scale strong lensing images.
In <em>NeurIPS Workshop on ML and the Physical Sciences</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#madireddy2019modular">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="jiang2019value">16</a>]
</td>
<td class="bibtexitem" align="justify">
P. Jiang, H. Doan, S. Madireddy, R. S. Assary, and P. Balaprakash.
Value-added chemical discovery using reinforcement learning.
In <em>NeurIPS Workshop on ML and the Physical Sciences</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#jiang2019value">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="khairy2019reinforcement">17</a>]
</td>
<td class="bibtexitem" align="justify">
S. Khairy, R. Shaydulin, L. Cincio, Y. Alexeev, and P. Balaprakash.
Reinforcement-learning-based variational quantum circuits
optimization for combinatorial problems.
In <em>NeurIPS Workshop on ML and the Physical Sciences</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#khairy2019reinforcement">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Maulik2019TimeseriesLO">18</a>]
</td>
<td class="bibtexitem" align="justify">
R. Maulik, A. Mohan, B. Lusch, S. Madireddy, P. Balaprakash, and D. Livescu.
Time-series learning of latent-space dynamics for reduced-order model
closure.
<em>Physica D: Nonlinear Phenomena</em>, 405, 2019.
[ <a href="pbalapra-bibtex_bib.html#Maulik2019TimeseriesLO">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="madireddy2019phase">19</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, D.-W. Chung, T. Loeffler, S. K. Sankaranarayanan, D. N. Seidman,
P. Balaprakash, and O. Heinonen.
Phase segmentation in atom-probe tomography using deep learning-based
edge detection.
<em>Scientific reports</em>, 9(1):1–10, 2019.
[ <a href="pbalapra-bibtex_bib.html#madireddy2019phase">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="lee2019improving">20</a>]
</td>
<td class="bibtexitem" align="justify">
S. Lee, Q. Kang, S. Madireddy, P. Balaprakash, A. Agrawal, A. Choudhary,
R. Archibald, and W.-k. Liao.
Improving scalability of parallel cnn training by adjusting
mini-batch size at run-time.
In <em>2019 IEEE International Conference on Big Data (Big Data)</em>,
pages 830–839. IEEE, 2019.
[ <a href="pbalapra-bibtex_bib.html#lee2019improving">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalSC2019">21</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, R. Egele, M. Salim, S. Wild, V. Vishwanath, F. Xia, T. Brettin,
and R. Stevens.
Scalable reinforcement-learning-based neural architecture search for
cancer deep learning research.
In <em>SC '19: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#BalSC2019">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="gmd-2019-79">22</a>]
</td>
<td class="bibtexitem" align="justify">
J. Wang, P. Balaprakash, and R. Kotamarthi.
Fast domain-aware neural network emulation of a planetary boundary
layer parameterization in a numerical weather forecast model.
<em>Geoscientific Model Development</em>, 2019:1–31, 2019.
[ <a href="pbalapra-bibtex_bib.html#gmd-2019-79">bib</a> |
<a href="http://dx.doi.org/10.5194/gmd-2019-79">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="wycoff2019neuromorphic">23</a>]
</td>
<td class="bibtexitem" align="justify">
N. Wycoff, P. Balaprakash, and F. Xia.
Neuromorphic acceleration for approximate bayesian inference on
neural networks via permanent dropout.
In <em>International Conference on Neuromorphic Computing</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#wycoff2019neuromorphic">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="madireddy2019neuromorphic">24</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, A. Yanguas-Gil, and P. Balaprakash.
Neuromorphic architecture optimization for task-specific dynamic
learning.
In <em>International Conference on Neuromorphic Computing</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#madireddy2019neuromorphic">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Aithal2019">25</a>]
</td>
<td class="bibtexitem" align="justify">
S. M. Aithal and P. Balaprakash.
MaLTESE: Large-Scale Simulation-Driven Machine Learning for
Transient Driving Cycles.
In M. Weiland, G. Juckeland, C. Trinitis, and P. Sadayappan, editors,
<em>High Performance Computing</em>, pages 186–205. Springer International
Publishing, 2019.
[ <a href="pbalapra-bibtex_bib.html#Aithal2019">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="kimgraph2019">26</a>]
</td>
<td class="bibtexitem" align="justify">
C. Kim, K. Kim, P. Balaprakash, and M. Anitescu.
Graph convolutional neural networks for optimal load shedding under
line contingency.
In <em>IEEE Power & Energy Society General Meeting (PESGM)</em>, 2019.
[ <a href="pbalapra-bibtex_bib.html#kimgraph2019">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MadireddyICPP2019">27</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, P. Balaprakash, P. Carns, R. Latham, G. K. Lockwood, R. Ross,
S. Snyder, and S. M. Wild.
Adaptive learning for concept drift in application performance
modeling.
In <em>Proceedings of the 48th International Conference on Parallel
Processing</em>, ICPP 2019, pages 79:1–79:11, New York, NY, USA, 2019. ACM.
[ <a href="pbalapra-bibtex_bib.html#MadireddyICPP2019">bib</a> |
<a href="http://dx.doi.org/10.1145/3337821.3337922">DOI</a> |
<a href="http://doi.acm.org/10.1145/3337821.3337922">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Sre2019">28</a>]
</td>
<td class="bibtexitem" align="justify">
V. Sreenivasan, R. Javali, M. Hall, P. Balaprakash, T. R. W. Scogland, and
B. R. de Supinski.
A framework for enabling openmp autotuning.
In X. Fan, B. R. de Supinski, O. Sinnen, and N. Giacaman, editors,
<em>OpenMP: Conquering the Full Hardware Spectrum</em>, pages 50–60. Springer
International Publishing, 2019.
[ <a href="pbalapra-bibtex_bib.html#Sre2019">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="WozBMC18">29</a>]
</td>
<td class="bibtexitem" align="justify">
J. M. Wozniak, R. Jain, P. Balaprakash, J. Ozik, N. T. Collier, J. Bauer,
F. Xia, T. S. Brettin, R. Stevens, J. Mohd-Yusof, C. Garcia-Cardona,
B. V. Essen, and M. Baughman.
Candle/supervisor: a workflow framework for machine learning applied
to cancer research.
<em>BMC Bioinformatics</em>, 19-S(18):59–69, 2018.
[ <a href="pbalapra-bibtex_bib.html#WozBMC18">bib</a> |
<a href="http://dx.doi.org/10.1186/s12859-018-2508-4">DOI</a> |
<a href="https://doi.org/10.1186/s12859-018-2508-4">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="LiuKetBalFos2018">30</a>]
</td>
<td class="bibtexitem" align="justify">
Z. Liu, R. Kettimuthu, P. Balaprakash, and I. Foster.
Building a wide-area data transfer performance predictor: An
empirical study.
In <em>the 1st International Conference on Machine Learning for
Networking</em>, MLN 2018. Springer, 2018.
[ <a href="pbalapra-bibtex_bib.html#LiuKetBalFos2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Madireddy_cluster2018">31</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. M. Wild.
Modeling I/O performance variability using conditional variational
autoencoders.
In <em>2018 IEEE International Conference on Cluster Computing
(CLUSTER)</em>, 2018.
[ <a href="pbalapra-bibtex_bib.html#Madireddy_cluster2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Madireddy_pdsw2018">32</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, P. Balaprakash, P. Carns, R. Latham, G. K. Lockwood, R. Ross,
S. Snyder, and S. M. Wild.
Online change point detection and adaptive predictive modeling of
I/O performance.
In <em>In Review</em>, 2018.
[ <a href="pbalapra-bibtex_bib.html#Madireddy_pdsw2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MalBalVis2018">33</a>]
</td>
<td class="bibtexitem" align="justify">
P. Malakar, P. Balaprakash, V. Vishwanath, V. Morozov, and K. Kumaran.
Benchmarking machine learning methods for performance modeling of
scientific applications.
In <em>PMBS 2018: Performance Modeling, Benchmarking and
Simulation of High Performance Computer Systems (held in conjunction with
SC18)</em>, 2018.
[ <a href="pbalapra-bibtex_bib.html#MalBalVis2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalLarWil2018">34</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, J. Larson, V. Vishwanath, and S. Wild.
Derivative-free mixed-integer optimization for automated predictive
modeling using machine learning.
In <em>SciML 2018: DOE ASCR Scientific Machine Learning Workshop</em>,
2018.
[ <a href="pbalapra-bibtex_bib.html#BalLarWil2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="SalUraChil2018">35</a>]
</td>
<td class="bibtexitem" align="justify">
M. Salim, T. Uram, J. Childers, P. Balaprakash, V. Vishwanath, and M. Papka.
Balsam: Automated scheduling and execution of dynamic, data-intensive
HPC workflows.
In <em>Python for High-Performance and Scientific Computing (held in
conjunction with SC18)</em>, 2018.
[ <a href="pbalapra-bibtex_bib.html#SalUraChil2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="LeeAgrBal2018">36</a>]
</td>
<td class="bibtexitem" align="justify">
S. Lee, A. Agrawal, P. Balaprakash, A. Choudhary, and W. Liao.
Communication-efficient parallelization strategy for deep
convolutional neural network training.
In <em>Machine Learning in HPC (held in conjunction with SC18)</em>,
2018.
[ <a href="pbalapra-bibtex_bib.html#LeeAgrBal2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MadBalCar2018">37</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. M. Wild.
Machine learning based parallel I/O predictive modeling: A case
study on Lustre file systems.
In <em>High Performance Computing</em>, pages 184–204. Springer
International Publishing, 2018.
[ <a href="pbalapra-bibtex_bib.html#MadBalCar2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="SUBASI2018277">38</a>]
</td>
<td class="bibtexitem" align="justify">
O. Subasi, S. Di, L. Bautista-Gomez, P. Balaprakash, O. Unsal, J. Labarta,
A. Cristal, S. Krishnamoorthy, and F. Cappello.
Exploring the capabilities of support vector machines in detecting
silent data corruptions.
<em>Sustainable Computing: Informatics and Systems</em>, 19:277 – 290,
2018.
[ <a href="pbalapra-bibtex_bib.html#SUBASI2018277">bib</a> |
<a href="https://doi.org/10.1016/j.suscom.2018.01.004">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalSalUra2018">39</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, M. Salim, T. Uram, V. Vishwanath, and S. M. Wild.
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural
Networks.
In <em>25th IEEE International Conference on High Performance
Computing, Data, and Analytics</em>. IEEE, 2018.
[ <a href="pbalapra-bibtex_bib.html#BalSalUra2018">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalDonGamHall2018">40</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. K. Hollingsworth,
B. Norris, and R. Vuduc.
Autotuning in high-performance computing applications.
<em>Proceedings of the IEEE</em>, pages 1–16, 2018.
[ <a href="pbalapra-bibtex_bib.html#BalDonGamHall2018">bib</a> |
<a href="http://dx.doi.org/10.1109/JPROC.2018.2841200">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Foster2017">41</a>]
</td>
<td class="bibtexitem" align="justify">
I. Foster, T. Lehman, N. Rao, B. Lyles, P. Balaprakash, K. Perumalla,
S. Prowell, and R. Vatsavi.
Towards new generation intelligent networking infrastructure for
distributed science environments.
Technical report, DOE ASCR Workshop Report, 2017.
[ <a href="pbalapra-bibtex_bib.html#Foster2017">bib</a> |
<a href="https://www.orau.gov/smarthp2016/Final-report.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MamBalCho2017">42</a>]
</td>
<td class="bibtexitem" align="justify">
A. Mametjanov, P. Balaprakash, C. Choudary, P. D. Hovland, S. M. Wild,
G. Sabin, and G. Wolfe.
Improving FPGA design parameter exploration: Timing, power, and
area.
2017.
[ <a href="pbalapra-bibtex_bib.html#MamBalCho2017">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="ChuBalMorVisKum2017">43</a>]
</td>
<td class="bibtexitem" align="justify">
S. Chunduri, P. Balaprakash, V. Morozov, V. Vishwanath, and K. Kumaran.
Analytical performance modeling and validation of intel's xeon phi
architecture.
In <em>Proceedings of the Computing Frontiers Conference</em>, CF'17,
pages 247–250, New York, NY, USA, 2017. ACM.
[ <a href="pbalapra-bibtex_bib.html#ChuBalMorVisKum2017">bib</a> |
<a href="http://dx.doi.org/10.1145/3075564.3075593">DOI</a> |
<a href="http://doi.acm.org/10.1145/3075564.3075593">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="LiuBalKetFos2017">44</a>]
</td>
<td class="bibtexitem" align="justify">
Z. Liu, P. Balaprakash, R. Kettimuthu, and I. Foster.
Explaining wide area data transfer performance.
In <em>Proceedings of the 26th International Symposium on
High-Performance Parallel and Distributed Computing</em>, HPDC '17, pages
167–178, New York, NY, USA, 2017. ACM.
Acceptance rate 19%.
[ <a href="pbalapra-bibtex_bib.html#LiuBalKetFos2017">bib</a> |
<a href="http://dx.doi.org/10.1145/3078597.3078605">DOI</a> |
<a href="http://doi.acm.org/10.1145/3078597.3078605">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MadiBalCarLatRossSnyWil2017">45</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. M. Wild.
Analysis and correlation of application I/O performance and
system-wide I/O activity.
In <em>2017 International Conference on Networking, Architecture,
and Storage (NAS)</em>, pages 1–10, Aug 2017.
Acceptance rate 33%.
[ <a href="pbalapra-bibtex_bib.html#MadiBalCarLatRossSnyWil2017">bib</a> |
<a href="http://dx.doi.org/10.1109/NAS.2017.8026844">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MadiBalCarLat2017">46</a>]
</td>
<td class="bibtexitem" align="justify">
S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and
S. Wild.
Modeling application I/O performance variability: A probabilistic
graphical model approach, 2017.
[ <a href="pbalapra-bibtex_bib.html#MadiBalCarLat2017">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="SubDiBal2017">47</a>]
</td>
<td class="bibtexitem" align="justify">
O. Subasi, S. Di, P. Balaprakash, O. Unsal, J. Labarta, A. Cristal,
S. Krishnamoorthy, and F. Cappello.
MACORD: online adaptive machine learning framework for silent error
detection.
In <em>In 3rd Workshop on Fault Tolerance Systems (FTS'17)</em>, 2017.
[ <a href="pbalapra-bibtex_bib.html#SubDiBal2017">bib</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MunBal2017">48</a>]
</td>
<td class="bibtexitem" align="justify">
T. Munson and P. Balaprakash.
Dynamic Adversarial Games in Complex Systems and Machine Learning.
8 2017.
[ <a href="pbalapra-bibtex_bib.html#MunBal2017">bib</a> |
<a href="http://dx.doi.org/10.6084/m9.figshare.5334208.v1">DOI</a> |
<a href="https://figshare.com/articles/Dynamic_Adversarial_Games_in_Complex_Systems_and_Machine_Learning/5334208">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="RoyBalHovWil2016">49</a>]
</td>
<td class="bibtexitem" align="justify">
A. Roy, P. Balaprakash, P. D. Hovland, and S. M. Wild.
Exploiting performance portability in search algorithms for
autotuning.
In <em>2016 IEEE International Parallel and Distributed Processing
Symposium Workshops (IPDPSW)</em>, pages 1535–1544, 2016.
[ <a href="pbalapra-bibtex_bib.html#RoyBalHovWil2016">bib</a> |
<a href="http://dx.doi.org/10.1109/IPDPSW.2016.85">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MoaBalRouWil2016">50</a>]
</td>
<td class="bibtexitem" align="justify">
A. Moawad, P. Balaprakash, A. Rousseau, and S. M. Wild.
Novel large scale simulation process to support DOT's CAFE
modeling system.
<em>International Journal of Automotive Technology</em>,
17(6):1067–1077, 2016.
[ <a href="pbalapra-bibtex_bib.html#MoaBalRouWil2016">bib</a> |
<a href="http://dx.doi.org/10.1007/s12239-016-0104-z">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalTiwWilHov2016">51</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, A. Tiwari, S. M. Wild, and P. D. Hovland.
AutoMOMML: Automatic Multi-objective Modeling with Machine
Learning.
In M. J. Kunkel, P. Balaji, and J. Dongarra, editors, <em>High
Performance Computing: 31st International Conference, ISC High Performance
2016, Frankfurt, Germany, June 19-23, 2016, Proceedings</em>, pages 219–239.
Springer International Publishing, 2016.
[ <a href="pbalapra-bibtex_bib.html#BalTiwWilHov2016">bib</a> |
<a href="http://dx.doi.org/10.1007/978-3-319-41321-1_12">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalMorKetKumFos2016">52</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, V. Morozov, R. Kettimuthu, K. Kumaran, and I. Foster.
Improving data transfer throughput with direct search optimization.
In <em>2016 45th International Conference on Parallel Processing
(ICPP)</em>, pages 248–257, 2016.
Acceptance rate 21.10%.
[ <a href="pbalapra-bibtex_bib.html#BalMorKetKumFos2016">bib</a> |
<a href="http://dx.doi.org/10.1109/ICPP.2016.36">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="SubDiBauBal2016">53</a>]
</td>
<td class="bibtexitem" align="justify">
O. Subasi, S. Di, L. Bautista-Gomez, P. Balaprakash, O. Unsal, J. Labarta,
A. Cristal, and F. Cappello.
Spatial support vector regression to detect silent errors in the
exascale era.
In <em>2016 16th IEEE/ACM International Symposium on Cluster, Cloud
and Grid Computing (CCGrid)</em>, pages 413–424, 2016.
Acceptance rate 20.0%.
[ <a href="pbalapra-bibtex_bib.html#SubDiBauBal2016">bib</a> |
<a href="http://dx.doi.org/10.1109/CCGrid.2016.33">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="berry2015ml">54</a>]
</td>
<td class="bibtexitem" align="justify">
M. Berry, T. E. Potok, P. Balaprakash, H. Hoffmann, R. Vatsavai, and Prabhat.
Machine learning and understanding for intelligent extreme scale
scientific computing and discovery.
Technical report, DOE ASCR Workshop Report, 2015.
[ <a href="pbalapra-bibtex_bib.html#berry2015ml">bib</a> |
<a href="http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/Machine_Learning_DOE_Workshop_Report.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalBirSto15">55</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, M. Birattari, T. Stützle, and M. Dorigo.
Estimation-based metaheuristics for the single vehicle routing
problem with stochastic demands and customers.
<em>Computational Optimization and Applications</em>, 61(2):463–487,
2015.
[ <a href="pbalapra-bibtex_bib.html#BalBirSto15">bib</a> |
<a href="http://dx.doi.org/10.1007/s10589-014-9719-z">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="MamBalCho2015">56</a>]
</td>
<td class="bibtexitem" align="justify">
A. Mametjanov, P. Balaprakash, C. Choudary, P. D. Hovland, S. M. Wild, and
G. Sabin.
Autotuning FPGA design parameters for performance and power.
In <em>2015 IEEE 23rd Annual International Symposium on
Field-Programmable Custom Computing Machines (FCCM)</em>, pages 84–91, 2015.
Acceptance rate 22.10%.
[ <a href="pbalapra-bibtex_bib.html#MamBalCho2015">bib</a> |
<a href="http://dx.doi.org/10.1109/FCCM.2015.54">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalGomBou15">57</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, L. A. B. Gomez, M. S. Bouguerra, S. M. Wild, F. Cappello, and
P. D. Hovland.
Analysis of the tradeoffs between energy and run time for multilevel
checkpointing.
In S. A. Jarvis, S. A. Wright, and S. D. Hammond, editors, <em>High
Performance Computing Systems. Performance Modeling, Benchmarking, and
Simulation – PMBS 2014</em>, volume 8966 of <em>Lecture Notes in Computer
Science</em>, pages 249–263. Springer International Publishing, 2015.
Acceptance rate 26%.
[ <a href="pbalapra-bibtex_bib.html#BalGomBou15">bib</a> |
<a href="http://dx.doi.org/10.1007/978-3-319-17248-4_13">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="NelRivBal2015">58</a>]
</td>
<td class="bibtexitem" align="justify">
T. Nelson, A. Rivera, P. Balaprakash, M. Hall, P. D. Hovland, E. Jessup, and
B. Norris.
Generating efficient tensor contractions for GPUs.
In <em>2015 44th International Conference on Parallel Processing
(ICPP)</em>, pages 969–978, 2015.
Acceptance rate 32.5%.
[ <a href="pbalapra-bibtex_bib.html#NelRivBal2015">bib</a> |
<a href="http://dx.doi.org/10.1109/ICPP.2015.106">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="BalAleMic15">59</a>]
</td>
<td class="bibtexitem" align="justify">
P. Balaprakash, Y. Alexeev, S. A. Mickelson, S. Leyffer, R. Jacob, and
A. Craig.
Machine-learning-based load balancing for community ice code
component in CESM.
In M. Daydé, O. Marques, and K. Nakajima, editors, <em>High
Performance Computing for Computational Science – VECPAR 2014, Revised
Selected Papers</em>, volume 8969 of <em>Lecture Notes in Computer Science</em>,
pages 79–91. Springer International Publishing, 2015.
[ <a href="pbalapra-bibtex_bib.html#BalAleMic15">bib</a> |
<a href="http://dx.doi.org/10.1007/978-3-319-17353-5_7">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="IsaBalWil2015">60</a>]
</td>
<td class="bibtexitem" align="justify">
F. Isaila, P. Balaprakash, S. M. Wild, D. Kimpe, R. Latham, R. Ross, and P. D.
Hovland.
Collective I/O tuning using analytical and machine learning models.
In <em>2015 IEEE International Conference on Cluster Computing
(CLUSTER)</em>, pages 128–137. IEEE, 2015.
Acceptance rate 24%.
[ <a href="pbalapra-bibtex_bib.html#IsaBalWil2015">bib</a> |
<a href="http://dx.doi.org/10.1109/CLUSTER.2015.29">DOI</a> ]