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Todd Gamblin
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- affiliation: Lawrence Livermore National Laboratory, Livermore, CA, USA
- affiliation: University of North Carolina at Chapel Hill, Renaissance Computing Institute, NC, USA
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2020 – today
- 2024
- [j21]Michela Taufer
, Daniel Milroy
HPC and Cloud Convergence Beyond Technical Boundaries: Strategies for Economic Sustainability, Standardization, and Data Accessibility. Computer 57(6): 128-136 (2024) - [j20]James M. Willenbring
Providing a Flexible and Comprehensive Software Stack Via Spack, an Extreme-Scale Scientific Software Stack, and Software Development Kits. Comput. Sci. Eng. 26(1): 20-30 (2024) - [c85]Gregory Bolet, Giorgis Georgakoudis, Konstantinos Parasyris, Kirk W. Cameron, David Beckingsale, Todd Gamblin:
An Exploration of Global Optimization Strategies for Autotuning OpenMP-based Codes. IPDPS (Workshops) 2024: 741-750 - [c84]Harshitha Menon
Learning to Predict and Improve Build Successes in Package Ecosystems. MSR 2024: 531-542 - [c83]Daniel Nichols, Harshitha Menon, Todd Gamblin, Abhinav Bhatele:
A Probabilistic Approach To Selecting Build Configurations in Package Managers. SC 2024: 84 - [c82]Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele:
HPC-Coder: Modeling Parallel Programs using Large Language Models. ISC 2024: 1-12 - [i11]Daniel Nichols, Pranav Polasam, Harshitha Menon, Aniruddha Marathe, Todd Gamblin, Abhinav Bhatele:
Performance-Aligned LLMs for Generating Fast Code. CoRR abs/2404.18864 (2024) - 2023
- [j19]Suraj P. Kesavan
Scalable Comparative Visualization of Ensembles of Call Graphs. IEEE Trans. Vis. Comput. Graph. 29(3): 1691-1704 (2023) - [c81]Donald Pinckney, Federico Cassano, Arjun Guha
Flexible and Optimal Dependency Management via Max-SMT. ICSE 2023: 1418-1429 - [c80]Olga Pearce
Towards Collaborative Continuous Benchmarking for HPC. SC Workshops 2023: 627-635 - [i10]Giorgis Georgakoudis, Konstantinos Parasyris, Chunhua Liao, David Beckingsale, Todd Gamblin, Bronis R. de Supinski:
Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems. CoRR abs/2303.08873 (2023) - [i9]Todd Gamblin, Daniel S. Katz:
Overcoming Challenges to Continuous Integration in HPC. CoRR abs/2303.17034 (2023) - [i8]Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele:
Modeling Parallel Programs using Large Language Models. CoRR abs/2306.17281 (2023) - 2022
- [j18]Todd Gamblin
Overcoming Challenges to Continuous Integration in HPC. Comput. Sci. Eng. 24(6): 54-59 (2022) - [j17]Michael R. Wyatt II, Stephen Herbein
AI4IO: A suite of AI-based tools for IO-aware scheduling. Int. J. High Perform. Comput. Appl. 36(3): 370-387 (2022) - [c79]Daniel Nichols
Resource Utilization Aware Job Scheduling to Mitigate Performance Variability. IPDPS 2022: 335-345 - [c78]Harshitha Menon, Konstantinos Parasyris, Tom Scogland, Todd Gamblin:
Searching for High-Fidelity Builds Using Active Learning. MSR 2022: 179-190 - [c77]Farid Zakaria, Thomas R. W. Scogland, Todd Gamblin, Carlos Maltzahn:
Mapping Out the HPC Dependency Chaos. SC 2022: 34:1-34:12 - [c76]Todd Gamblin, Massimiliano Culpo, Gregory Becker, Sergei Shudler:
Using Answer Set Programming for HPC Dependency Solving. SC 2022: 35:1-35:15 - [i7]Harshitha Menon, Konstantinos Parasyris, Tom Scogland, Todd Gamblin:
Reliabuild: Searching for High-Fidelity Builds Using Active Learning. CoRR abs/2202.05223 (2022) - [i6]Donald Pinckney, Arjun Guha, Massimiliano Culpo, Todd Gamblin:
Using Solver-Aided Languages to Build Package Managers. CoRR abs/2203.13737 (2022) - [i5]Todd Gamblin, Massimiliano Culpo, Gregory Becker, Sergei Shudler:
Using Answer Set Programming for HPC Dependency Solving. CoRR abs/2210.08404 (2022) - [i4]Farid Zakaria, Thomas R. W. Scogland, Todd Gamblin, Carlos Maltzahn:
Mapping Out the HPC Dependency Chaos. CoRR abs/2211.05118 (2022) - 2021
- [j16]Huu Tan Nguyen
Visualizing Hierarchical Performance Profiles of Parallel Codes Using CallFlow. IEEE Trans. Vis. Comput. Graph. 27(4): 2455-2468 (2021) - [c75]Ivy Peng, Ian Karlin, Maya B. Gokhale, Kathleen Shoga, Matthew P. LeGendre, Todd Gamblin:
A Holistic View of Memory Utilization on HPC Systems: Current and Future Trends. MEMSYS 2021: 14:1-14:11 - [c74]Chad Wood, Giorgis Georgakoudis
Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning. ISC 2021: 453-472 - [c73]Chunhua Liao
Extending OpenMP for Machine Learning-Driven Adaptation. WACCPD@SC 2021: 49-69 - [e2]Bronis R. de Supinski, Mary W. Hall, Todd Gamblin:
International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2021, St. Louis, Missouri, USA, November 14-19, 2021. ACM 2021, ISBN 978-1-4503-8442-1 [contents] - 2020
- [c72]Tao Wang
CodeSeer: input-dependent code variants selection via machine learning. ICS 2020: 43:1-43:11 - [c71]Michael R. Wyatt II, Stephen Herbein, Kathleen Shoga, Todd Gamblin, Michela Taufer
CanarIO: Sounding the Alarm on IO-Related Performance Degradation. IPDPS 2020: 73-83 - [c70]Harshitha Menon, Abhinav Bhatele, Todd Gamblin:
Auto-tuning Parameter Choices in HPC Applications using Bayesian Optimization. IPDPS 2020: 831-840 - [c69]Massimiliano Culpo
archspec: A library for detecting, labeling, and reasoning about microarchitectures. CANOPIE-HPC@SC 2020: 45-52 - [c68]Stephanie Brink, Ian Lumsden, Connor Scully-Allison, Katy Williams, Olga Pearce, Todd Gamblin, Michela Taufer
Usability and Performance Improvements in Hatchet. HUST/ProTools@SC 2020: 49-58 - [c67]Tal Ben-Nun, Todd Gamblin, Daisy S. Hollman, Hari Krishnan, Chris J. Newburn:
Workflows are the New Applications: Challenges in Performance, Portability, and Productivity. P3HPC@SC 2020: 57-69 - [i3]Suraj P. Kesavan
Scalable Comparative Visualization of Ensembles of Call Graphs. CoRR abs/2007.01395 (2020)
2010 – 2019
- 2019
- [j15]Katherine E. Isaacs
Preserving Command Line Workflow for a Package Management System Using ASCII DAG Visualization. IEEE Trans. Vis. Comput. Graph. 25(9): 2804-2820 (2019) - [c66]Tao Wang
FuncyTuner: Auto-tuning Scientific Applications With Per-loop Compilation. ICPP 2019: 91:1-91:10 - [c65]Abhinav Bhatele, Nikhil Jain, Misbah Mubarak, Todd Gamblin:
Analyzing Cost-Performance Tradeoffs of HPC Network Designs under Different Constraints using Simulations. SIGSIM-PADS 2019: 1-12 - [c64]Abhinav Bhatele, Stephanie Brink, Todd Gamblin:
Hatchet: pruning the overgrowth in parallel profiles. SC 2019: 20:1-20:21 - [c63]Samuel Knight, Jeremiah J. Wilke, Todd Gamblin:
Using Malleable Task Scheduling to Accelerate Package Manager Installations. HUST/SE-HER/WIHPC@SC 2019: 28-48 - [c62]Ian Karlin, Yoonho Park, Bronis R. de Supinski, Peng Wang, Bert Still, David Beckingsale, Robert Blake, Tong Chen, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher
Preparation and optimization of a diverse workload for a large-scale heterogeneous system. SC 2019: 32:1-32:17 - [i2]Katherine E. Isaacs, Todd Gamblin:
Preserving Command Line Workflow for a Package Management System using ASCII DAG Visualization. CoRR abs/1908.07544 (2019) - 2018
- [j14]Prasanna Balaprakash
Autotuning in High-Performance Computing Applications. Proc. IEEE 106(11): 2068-2083 (2018) - [j13]Alfredo Giménez
MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors. IEEE Trans. Vis. Comput. Graph. 24(7): 2180-2193 (2018) - [c61]Michael R. Wyatt II, Stephen Herbein, Todd Gamblin, Adam Moody, Dong H. Ahn, Michela Taufer
PRIONN: Predicting Runtime and IO using Neural Networks. ICPP 2018: 46:1-46:12 - [c60]Jayaraman J. Thiagarajan, Nikhil Jain, Rushil Anirudh, Alfredo Giménez, Rahul Sridhar, Aniruddha Marathe
Bootstrapping Parameter Space Exploration for Fast Tuning. ICS 2018: 385-395 - [c59]Jayaraman J. Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, Nikhil Jain, Tanzima Z. Islam
PADDLE: Performance Analysis Using a Data-Driven Learning Environment. IPDPS 2018: 784-793 - 2017
- [j12]Roscoe A. Bartlett, Irina Demeshko, Todd Gamblin, Glenn Hammond
xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit. Supercomput. Front. Innov. 4(1): 69-82 (2017) - [c58]David Beckingsale, Olga Pearce, Ignacio Laguna, Todd Gamblin:
Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code. IPDPS 2017: 307-316 - [c57]Hao Xu
DR-BW: Identifying Bandwidth Contention in NUMA Architectures with Supervised Learning. IPDPS 2017: 367-376 - [c56]Nikhil Jain, Abhinav Bhatele, Xiang Ni, Todd Gamblin, Laxmikant V. Kalé:
Partitioning Low-Diameter Networks to Eliminate Inter-Job Interference. IPDPS 2017: 439-448 - [c55]Todd Gamblin:
REPPAR Keynote. IPDPS Workshops 2017: 1560 - [c54]Aniruddha Marathe
Performance modeling under resource constraints using deep transfer learning. SC 2017: 31 - [c53]Alfredo Giménez, Todd Gamblin, Abhinav Bhatele, Chad Wood, Kathleen Shoga, Aniruddha Marathe
ScrubJay: deriving knowledge from the disarray of HPC performance data. SC 2017: 35 - [c52]Nikhil Jain, Abhinav Bhatele, Louis H. Howell, David Böhme
Predicting the performance impact of different fat-tree configurations. SC 2017: 50 - [c51]Chad Wood, Matthew Larsen, Alfredo Giménez, Kevin A. Huck
Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINE. ESPT/VPA@SC 2017: 201-218 - [i1]Roscoe A. Bartlett, Irina Demeshko, Todd Gamblin, Glenn Hammond, Michael A. Heroux, Jeffrey Johnson, Alicia M. Klinvex, Xiaoye S. Li, Lois Curfman McInnes, J. David Moulton, Daniel Osei-Kuffuor, Jason Sarich, Barry Smith, James M. Willenbring, Ulrike Meier Yang:
xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit. CoRR abs/1702.08425 (2017) - 2016
- [j11]Ignacio Laguna, David F. Richards, Todd Gamblin, Martin Schulz
Evaluating and extending user-level fault tolerance in MPI applications. Int. J. High Perform. Comput. Appl. 30(3): 305-319 (2016) - [j10]Katherine E. Isaacs
Ordering Traces Logically to Identify Lateness in Message Passing Programs. IEEE Trans. Parallel Distributed Syst. 27(3): 829-840 (2016) - [c50]Ryan McKenna, Stephen Herbein, Adam Moody, Todd Gamblin, Michela Taufer
Machine Learning Predictions of Runtime and IO Traffic on High-End Clusters. CLUSTER 2016: 255-258 - [c49]Olga Pearce, Todd Gamblin, Bronis R. de Supinski, Martin Schulz
MPMD Framework for Offloading Load Balance Computation. IPDPS 2016: 943-952 - [c48]Kirk W. Cameron
VarSys Introduction. IPDPS Workshops 2016: 1068 - [c47]Shuaiwen Leon Song, Todd Gamblin:
IPDRM Introduction and Committees. IPDPS Workshops 2016: 1726 - [c46]Subrata Mitra, Suhas Javagal, Amiya Kumar Maji, Todd Gamblin, Adam Moody, Stephen Lien Harrell
A Study of Failures in Community Clusters: The Case of Conte. ISSRE Workshops 2016: 189-196 - [c45]Gregory Becker
Managing Combinatorial Software Installations with Spack. HUST@SC 2016: 14-23 - [c44]Huu Tan Nguyen, Lai Wei, Abhinav Bhatele, Todd Gamblin, David Böhme
VIPACT: A Visualization Interface for Analyzing Calling Context Trees. VPA@SC 2016: 25-28 - [c43]Chad Wood, Sudhanshu Sane, Daniel A. Ellsworth, Alfredo Giménez, Kevin A. Huck
A Scalable Observation System for Introspection and In Situ Analytics. ESPT@SC 2016: 42-49 - [c42]Nikhil Jain, Abhinav Bhatele, Sam White, Todd Gamblin, Laxmikant V. Kalé:
Evaluating HPC networks via simulation of parallel workloads. SC 2016: 154-165 - [c41]Tanzima Z. Islam
A machine learning framework for performance coverage analysis of proxy applications. SC 2016: 538-549 - [c40]David Böhme
Caliper: performance introspection for HPC software stacks. SC 2016: 550-560 - 2015
- [j9]Ignacio Laguna, Dong H. Ahn, Bronis R. de Supinski, Todd Gamblin, Gregory L. Lee, Martin Schulz
Debugging high-performance computing applications at massive scales. Commun. ACM 58(9): 72-81 (2015) - [j8]Peer-Timo Bremer, Bernd Mohr
Connecting Performance Analysis and Visualization (Dagstuhl Perspectives Workshop 14022). Dagstuhl Manifestos 5(1): 1-24 (2015) - [j7]Ignacio Laguna, Dong H. Ahn, Bronis R. de Supinski, Saurabh Bagchi, Todd Gamblin:
Diagnosis of Performance Faults in LargeScale MPI Applications via Probabilistic Progress-Dependence Inference. IEEE Trans. Parallel Distributed Syst. 26(5): 1280-1289 (2015) - [c39]Abhinav Bhatele, Andrew R. Titus, Jayaraman J. Thiagarajan, Nikhil Jain, Todd Gamblin, Peer-Timo Bremer, Martin Schulz
Identifying the Culprits Behind Network Congestion. IPDPS 2015: 113-122 - [c38]Olga Pearce, Todd Gamblin, Bronis R. de Supinski, Martin Schulz
Decoupled load balancing. PPoPP 2015: 267-268 - [c37]Benafsh Husain, Alfredo Giménez, Joshua A. Levine, Todd Gamblin, Peer-Timo Bremer:
Relating memory performance data to application domain data using an integration API. VPA@SC 2015: 5:1-5:8 - [c36]Todd Gamblin, Matthew P. LeGendre, Michael R. Collette, Gregory L. Lee, Adam Moody, Bronis R. de Supinski, Scott Futral:
The Spack package manager: bringing order to HPC software chaos. SC 2015: 40:1-40:12 - [c35]Katherine E. Isaacs, Abhinav Bhatele, Jonathan Lifflander, David Böhme, Todd Gamblin, Martin Schulz
Recovering logical structure from Charm++ event traces. SC 2015: 49:1-49:12 - [e1]Chris Bording, Todd Gamblin, Vera Hansper:
Proceedings of the Second International Workshop on HPC User Support Tools, HUST 2015, Austin, Texas, USA, November 15, 2015. ACM 2015, ISBN 978-1-4503-4000-7 [contents] - 2014
- [j6]Katherine E. Isaacs
Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time. IEEE Trans. Vis. Comput. Graph. 20(12): 2349-2358 (2014) - [c34]Kento Sato, Kathryn M. Mohror
A User-Level InfiniBand-Based File System and Checkpoint Strategy for Burst Buffers. CCGRID 2014: 21-30 - [c33]Abhinav Bhatele, Nikhil Jain, Katherine E. Isaacs, Ronak Buch, Todd Gamblin, Steven H. Langer, Laxmikant V. Kalé:
Optimizing the performance of parallel applications on a 5D torus via task mapping. HiPC 2014: 1-10 - [c32]Olga Pearce, Todd Gamblin, Bronis R. de Supinski, Tom Arsenlis, Nancy M. Amato:
Load balancing n-body simulations with highly non-uniform density. ICS 2014: 113-122 - [c31]Kento Sato, Adam Moody, Kathryn M. Mohror
FMI: Fault Tolerant Messaging Interface for Fast and Transparent Recovery. IPDPS 2014: 1225-1234 - [c30]Subrata Mitra, Ignacio Laguna, Dong H. Ahn, Saurabh Bagchi, Martin Schulz
Accurate application progress analysis for large-scale parallel debugging. PLDI 2014: 193-203 - [c29]Katherine E. Isaacs, Todd Gamblin, Abhinav Bhatele, Peer-Timo Bremer
Extracting logical structure and identifying stragglers in parallel execution traces. PPoPP 2014: 397-398 - [c28]Ignacio Laguna, David F. Richards, Todd Gamblin, Martin Schulz
Evaluating User-Level Fault Tolerance for MPI Applications. EuroMPI/ASIA 2014: 57 - [c27]Alfredo Giménez, Todd Gamblin, Barry Rountree, Abhinav Bhatele, Ilir Jusufi
Dissecting On-Node Memory Access Performance: A Semantic Approach. SC 2014: 166-176 - [c26]Katherine E. Isaacs, Alfredo Giménez, Ilir Jusufi, Todd Gamblin, Abhinav Bhatele, Martin Schulz
State of the Art of Performance Visualization. EuroVis (STARs) 2014 - 2013
- [j5]Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron
Trellis: Portability across architectures with a high-level framework. J. Parallel Distributed Comput. 73(10): 1400-1413 (2013) - [j4]Barry Rountree, Todd Gamblin, Bronis R. de Supinski, Martin Schulz
Parallelizing heavyweight debugging tools with mpiecho. Parallel Comput. 39(3): 156-166 (2013) - [c25]Wolfgang Frings, Dong H. Ahn, Matthew P. LeGendre, Todd Gamblin, Bronis R. de Supinski, Felix Wolf:
Massively parallel loading. ICS 2013: 389-398 - [c24]Dong H. Ahn, Michael J. Brim
Efficient and Scalable Retrieval Techniques for Global File Properties. IPDPS 2013: 369-380 - [c23]Martin Schulz
Performance Analysis Techniques for the Exascale Co-Design Process. PARCO 2013: 19-32 - [c22]Nikhil Jain, Abhinav Bhatele, Michael P. Robson, Todd Gamblin, Laxmikant V. Kalé:
Predicting application performance using supervised learning on communication features. SC 2013: 95:1-95:12 - 2012
- [j3]Sandeep Budanur, Frank Mueller, Todd Gamblin:
Memory Trace Compression and Replay for SPMD Systems Using Extended PRSDs. Comput. J. 55(2): 206-217 (2012) - [j2]Aaditya G. Landge, Joshua A. Levine, Abhinav Bhatele, Katherine E. Isaacs
Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations. IEEE Trans. Vis. Comput. Graph. 18(12): 2467-2476 (2012) - [c21]Ignacio Laguna, Dong H. Ahn, Bronis R. de Supinski, Saurabh Bagchi, Todd Gamblin:
Probabilistic diagnosis of performance faults in large-scale parallel applications. PACT 2012: 213-222 - [c20]Olga Pearce, Todd Gamblin, Bronis R. de Supinski, Martin Schulz
Quantifying the effectiveness of load balance algorithms. ICS 2012: 185-194 - [c19]Spyros Lyberis, Polyvios Pratikakis, Dimitrios S. Nikolopoulos
The myrmics memory allocator: hierarchical, message-passing allocation for global address spaces. ISMM 2012: 15-24 - [c18]Kento Sato, Naoya Maruyama, Kathryn M. Mohror
Design and modeling of a non-blocking checkpointing system. SC 2012: 19 - [c17]Abhinav Bhatele, Todd Gamblin, Katherine E. Isaacs, Brian T. N. Gunney, Martin Schulz
Novel views of performance data to analyze large-scale adaptive applications. SC 2012: 31 - [c16]Abhinav Bhatele, Todd Gamblin, Steve H. Langer, Peer-Timo Bremer, Erik W. Draeger, Bernd Hamann, Katherine E. Isaacs, Aaditya G. Landge, Joshua A. Levine, Valerio Pascucci, Martin Schulz
Mapping applications with collectives over sub-communicators on torus networks. SC 2012: 97 - [c15]Anshu Arya, Todd Gamblin, Bronis R. de Supinski, Laxmikant V. Kalé:
Abstract: Evaluating Topology Mapping via Graph Partitioning. SC Companion 2012: 1371 - [c14]Anshu Arya, Todd Gamblin, Bronis R. de Supinski, Laxmikant V. Kalé:
Poster: Evaluation Topology Mapping via Graph Partitioning. SC Companion 2012: 1372 - [c13]Katherine E. Isaacs, Aaditya G. Landge, Todd Gamblin, Peer-Timo Bremer, Valerio Pascucci, Bernd Hamann:
Abstract: Exploring Performance Data with Boxfish. SC Companion 2012: 1380-1381 - [c12]Vivek Kale, Todd Gamblin, Torsten Hoefler, Bronis R. de Supinski, William D. Gropp:
Abstract: Slack-Conscious Lightweight Loop Scheduling for Improving Scalability of Bulk-synchronous MPI Applications. SC Companion 2012: 1392 - 2011
- [j1]Sandeep Budanur, Frank Mueller, Todd Gamblin:
Memory Trace Compression and Replay for SPMD Systems using Extended PRSDs? SIGMETRICS Perform. Evaluation Rev. 38(4): 30-36 (2011) - [c11]Martin Schulz
Interpreting Performance Data across Intuitive Domains. ICPP 2011: 206-215 - [c10]Allison H. Baker, Todd Gamblin, Martin Schulz
Challenges of Scaling Algebraic Multigrid Across Modern Multicore Architectures. IPDPS 2011: 275-286 - [c9]Zoltán Szebenyi, Todd Gamblin, Martin Schulz
Reconciling Sampling and Direct Instrumentation for Unintrusive Call-Path Profiling of MPI Programs. IPDPS 2011: 640-651 - [c8]Martin Schulz
Creating a Tool Set for Optimizing Topology-Aware Node Mappings. Parallel Tools Workshop 2011: 1-12 - [c7]Ignacio Laguna, Todd Gamblin, Bronis R. de Supinski, Saurabh Bagchi, Greg Bronevetsky, Dong H. Ahn, Martin Schulz
Large scale debugging of parallel tasks with AutomaDeD. SC 2011: 50:1-50:10 - 2010
- [c6]Allison H. Baker, Robert D. Falgout, Todd Gamblin, Tzanio V. Kolev
Scaling Algebraic Multigrid Solvers: On the Road to Exascale. CHPC 2010: 215-226 - [c5]Todd Gamblin, Bronis R. de Supinski, Martin Schulz
Clustering performance data efficiently at massive scales. ICS 2010: 243-252 - [c4]Frank Mueller, Xing Wu, Martin Schulz
ScalaTrace: Tracing, Analysis and Modeling of HPC Codes at Scale. PARA (2) 2010: 410-418
2000 – 2009
- 2009
- [b1]Todd Gamblin:
Scalable performance measurement and analysis. University of North Carolina, Chapel Hill, USA, 2009 - 2008
- [c3]Todd Gamblin, Robert J. Fowler, Daniel A. Reed:
Scalable methods for monitoring and detecting behavioral equivalence classes in scientific codes. IPDPS 2008: 1-12 - [c2]Todd Gamblin, Bronis R. de Supinski, Martin Schulz
Scalable load-balance measurement for SPMD codes. SC 2008: 46 - 2006
- [c1]Robert J. Fowler, Todd Gamblin, Gopi Kandaswamy, Anirban Mandal, Allan Porterfield, Lavanya Ramakrishnan, Daniel A. Reed:
Challenges of Scale: When All Computing Becomes Grid Computing. High Performance Computing Workshop 2006: 186-206
Coauthor Index
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