Faculty Profiles

Islam, T. Z., Zaeed, M., Schutte, H., & Marathe, A. (2026). Data-Driven Analysis for Understanding the Impact of Optimizations on a Multi-objective Space. International Journal of High Performance Computing and Applications (IJHPCA). Retrieved from https://arxiv.org/html/2408.10143v1

Ahmed, A. N., Banday, B., Jones, T., & Islam, T. Z. (2025). Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC. In Workshop on ML for Systems in Conjuction with NeurIPS.

Dey, A., Antony, N., Dhakal, A. R., Thopalli, K., Thiagarajan, J. J., Patki, T., … Islam, T. Z. (2025). ModelX: A Novel Transfer Learning Approach Across Heterogeneous Datasets. In The 34th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC).

Zaeed, M., Islam, T. Z., & Inđić, V. (2025). Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs. In Arxiv. Retrieved from https://arxiv.org/abs/2510.00932

Fefey, E. G., & Islam, T. Z. (2025). Optimizing Deep Learning Inference on Heterogeneous Devices on the Edge: An ILP-Based Rate Monotonic Scheduling Approach. In 2025 IEEE Cloud Summit (pp. 23–28). https://doi.org/10.1109/Cloud-Summit64795.2025.00011

Lahiry, A., Banday, B., & Islam, T. Z. (2025). WANDER: An Explainable Decision-Support Framework for HPC. In Arxiv. Retrieved from https://arxiv.org/abs/2506.04049

Islam, T. Z., Marathe, A., Schutte, H., & Zaeed, M. (2025). Data-Driven Analysis to Understand GPU Hardware Resource Usage of Optimizations. Retrieved from https://arxiv.org/abs/2408.10143

Ramadan, T., Pinnow, N., Phelps, C. L., Thiagarajan, J. J., & Islam, T. Z. (2025). Structure-Aware Representation Learning for Effective Performance Prediction. Concurrency and Computation: Practice and Experience, 37(9–11), e70046. https://doi.org/https://doi.org/10.1002/cpe.70046

Sirvent, Ra\"ul, Carratal\’a-Saez, Roc\’\io, Gueroudji, A., Islam, T. Z., Pouchard, L., & Taufer, M. (2025). Reproducibility for HPC and Distributed Environments: Committees, Nondeterminism, Performance and Workflows. In Proceedings of the 3rd ACM Conference on Reproducibility and Replicability (pp. 29–40). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3736731.3746141

Lahiry, A., Pokharel, A., Banday, B., Ockerman, S., Gueroudji, A., Zaeed, M., … Pouchard, L. (2025). A Distributed Framework for Causal Modeling of Performance Variability in GPU Traces. Retrieved from https://arxiv.org/abs/2510.18300

McInnes, L. C., Arnold, D., Balaprakash, P., Bernhardt, M., Cerny, B., Dubey, A., … Wu, L. (2025). Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science. Retrieved from https://arxiv.org/abs/2510.03413

Maiterth, M., Brewer, W. H., Kuruvella, J. S., Dey, A., Islam, T. Z., Kabir, R., … Wang, F. (2025). HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling. In Proceedings of the SC ’25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1959–1969). ACM. https://doi.org/10.1145/3731599.3767559

Kelly, C., Xu, W., Pouchard, L. C., Van Dam, H., Islam, T. Z., Yoo, S., & Kleese Van Dam, K. (2025). Performance analysis and data reduction for exascale scientific workflows. The International Journal of High Performance Computing Applications, 39(4), 553–578. https://doi.org/10.1177/10943420251316253

Dhakal, A. R., Islam, T. Z., Dey, A., Nichols, D., Bhatele, A., Patki, T., … Yeom, J.-S. (2025). xAMM: “Attention” to Details Improves Cross-Platform Prediction Accuracy. In 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 01–10). IEEE. https://doi.org/10.1109/ccgrid64434.2025.00067

Banday, B., Thopalli, K., Islam, T. Z., & Thiagarajan, J. J. (2025). On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1–5). IEEE. https://doi.org/10.1109/icassp49660.2025.10888077

Fefey, E. G., & Islam, T. Z. (2024). Toward Efficient Deep Learning Inference: On-Node Heterogeneous Scheduling in Edge-Cloud Infrastructure. In 2024 IEEE Cloud Summit (pp. 73–78). https://doi.org/10.1109/Cloud-Summit61220.2024.00019

Gueroudji, A., Phelps, C., Islam, T. Z., Carns, P., Snyder, S., Dorier, M., … Pouchard, L. C. (2024). Performance Characterization and Provenance of Distributed Task-based Workflows on HPC Platforms. In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 2032–2039). IEEE. https://doi.org/10.1109/scw63240.2024.00254

Phelps, C., Lahiry, A., Islam, T. Z., & Pouchard, L. C. (2024). Reimagine Application Performance as a Graph: Novel Graph-Based Method for Performance Anomaly Classification in High-Performance Computing. In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) (Vol. 12, pp. 240–245). IEEE. https://doi.org/10.1109/compsac61105.2024.00041

Dey, A., Dhakal, A., Islam, T. Z., Yeom, J.-S., Patki, T., Nichols, D., … Bhatele, A. (2024). Relative Performance Prediction Using Few-Shot Learning. In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1764–1769). IEEE. https://doi.org/10.1109/compsac61105.2024.00278

Banday, B. H., Islam, T. Z., & Marathe, A. (2024). PERFGEN: A Synthesis and Evaluation Framework for Performance Data using Generative AI. In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) (Vol. 46, pp. 188–197). IEEE. https://doi.org/10.1109/compsac61105.2024.00035

Zaeed, M., Islam, T. Z., & Indict, V. (2024). Characterize and Compare the Performance of Deep Learning Optimizers in Recurrent Neural Network Architectures. In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) (Vol. 12, pp. 39–44). IEEE. https://doi.org/10.1109/compsac61105.2024.00016

Fefey, E., & Islam, T. Z. (n.d.). Toward Efficient Deep Learning Inference: On-Node Heterogeneous Scheduling in Edge-Cloud Infrastructure.

Islam, T. Z., & Alhajjar, E. (2024, June 3). SIAM Task Force Anticipates Future Directions of Computational Science. Retrieved from https://www.siam.org/publications/siam-news/articles/siam-task-force-anticipates-future-directions-of-computational-science/

Phelps, C. L., & Islam, T. Z. (2023). Automatic Parallelization of Cellular Automata for Heterogeneous Platforms. In 47th Annual Computers, Software, and Applications Conference (COMPSAC).

Dey, A., Phelps, C. L., Islam, T. Z., & Kelly, C. (2023). Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing. In 47th Annual Computers, Software, and Applications Conference (COMPSAC).

Ramadan, T., Lahiry, A., & Islam, T. Z. (2023). Novel Representation Learning Technique Using Graphs for Performance Analytics. In 2023 International Conference on Machine Learning and Applications (ICMLA). IEEE. https://doi.org/10.1109/icmla58977.2023.00198

Nicolae, B., Islam, T. Z., Ross, R., Van Dam, H., Assogba, K., Shpilker, P., … Pouchard, L. C. (2023). Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP. In 2023 IEEE 19th International Conference on e-Science (e-Science) (Vol. 11, pp. 1–7). IEEE. https://doi.org/10.1109/e-science58273.2023.10254808

Schutte, H., Phelps, C. L., Marathe, A., & Islam, T. Z. (2022). \textsclibNVCD: An Extendable and User-friendly Multi-GPU Performance Measurement Tool. In 46th Annual Computers, Software, and Applications Conference (COMPSAC).

Guite, A., Islam, T. Z., Kelley, C., & Xu, W. (2022). Interactive Visual Analysis Tool for Anomaly Provenance Data. IEEE. Retrieved from https://sc22.supercomputing.org/proceedings/tech_poster/poster_files/rpost159s3-file2.pdf

Zaeed, M., Islam, T. Z., Cho, Y., Li, S., Luo, H., & Liu, Y. (2022). Analysis and Visualization of Important Performance Counters To Enhance Interpretability of Autotuner Output. IEEE. Retrieved from https://sc22.supercomputing.org/proceedings/tech_poster/poster_files/rpost183s3-file2.pdf

Pouchard, L., Islam, T. Z., Nicolae, B., & Ross, R. (n.d.). A (Meta)data Framework for Reproducing Hybrid Workflows with FAIR.

Pouchard, L., Islam, T. Z., & Nicolae, B. (2022). RECUP: A (meta)data framework for reproducing hybrid workflows with FAIR. Retrieved from https://works-workshop.org/files/works22_pouchard.pdf

Daw, C., Barragan-Cruz, B., Majeske, N., Jagodzinski, F., Islam, T. Z., & Hutchinson, B. (2022). Chapter 5: Low Rank Sampling Methods for Identifying Impactful Pairwise Protein Mutations. In Part of the Computational Biology book series (COBO). Springer Nature. https://doi.org/https://doi.org/10.1007/978-3-031-05914-8_4

Islam, T. Z., & Zaeed, M. (2022, October). Dashing enabled GPTune Autotuner. Public git repository. Retrieved from https://gptune.lbl.gov

Dey, A., & Islam, T. Z. (2022). Performance Modeling Across Heterogenous Domains Using Few-Shot Learning.

Fefey, E., & Islam, T. Z. (2022). Characterization of Deep Learning Inference Workloads.

Zaeed, M., & Islam, T. Z. (2022). Analysis and Visualization of Important Performance Counters to Enhance Interpretability of Autotuner Output.

Pouchard, L., Islam, T. Z., & Nicolae, B. (2022). Challenges for Implementing FAIR Digital Objects with High Performance Workflows. Retrieved from https://riojournal.com/article/94835/instance/8003496/

Islam, T. Z., & Zaeed, M. (2022, September). libNVCD:An easy-to-use, performance measurement and analysis tool for NVIDIA GPUs. Public git repository. Retrieved from https://gptune.lbl.gov

Schutte, H., Islam, T. Z., Phelps, C., & Marathe, A. (2022). libNVCD: An Extendable and User-friendly Multi-GPU Performance Measurement Tool (pp. 73–82). IEEE. https://doi.org/10.1109/COMPSAC54236.2022.00019

Islam, T. Z., Schutt, H., Phelps, C., & Marathe, A. (n.d.). GPUPD: An Extendable and User-friendly Multi-GPU Performance Measurement Tool. IEEE.

Islam, T. Z., & Phelps, C. (2021). HPC@SCALE: A Hands-on Approach for Training Next-Gen HPC Software Architects. In 2021 IEEE 28th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) (pp. 29–34). IEEE. https://doi.org/10.1109/hipcw54834.2021.00011

Jensen, Q., Jagodzinski, F., & Islam, T. Z. (2021). FILCIO: Application Agnostic I/O Aggregation to Scale Scientific Workflows. IEEE. https://doi.org/10.1109/COMPSAC51774.2021.00236

Islam, T. Z., Wu Liang, P., Sweeney, F., Pragner, C., Thiagarajan, J. J., Sharmin, M., & Ahmed, S. (2021). College Life is Hard! - Shedding Light on Stress Prediction for Autistic College Students using Data-Driven Analysis. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) (Vol. 329, pp. 428–437). IEEE. https://doi.org/10.1109/compsac51774.2021.00066

Ramadan, T., Islam, T. Z., Phelps, C., Pinnow, N., & Thiagarajan, J. J. (2021). Comparative Code Structure Analysis using Deep Learning for Performance Prediction. In 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) (pp. 151–161). IEEE. https://doi.org/10.1109/ispass51385.2021.00032

Stratton, J., Albert, M., Jensen, Q., Ismailov, M., Jagodzinski, F., & Islam, T. Z. (2020). Towards Aggregation Based I/O Optimization for Scaling Bioinformatics Applications (pp. 1250--1255). IEEE. https://doi.org/10.1109/COMPSAC48688.2020.00-85

Islam, T. Z. (2020). Future Directions of the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Program. In NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI). Retrieved from https://arxiv.org/abs/2010.15584

Islam, T. Z., & Zaeed, M. (2020, August). Dashing: An extendable and programmable toolbox of interpretable ML models. Public git repository. Retrieved from https://gptune.lbl.gov

Islam, T. Z. (2020). Performance characterization data for AMReX applications developed by the DOE Exascale Computing Project (ECP). https://doi.org/10.5281/zenodo.3403037

Patki, T., Thiagarajan, J. J., Ayala, A., & Islam, T. Z. (2019). Performance optimality or reproducibility: that is the question (pp. 1--30). ACM/IEEE.

Islam, T. Z., Ayala, A., Jensen, Q., & Ibrahim, K. (2019). Toward a Programmable Analysis and Visualization Framework for Interactive Performance Analytics. In International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (pp. 70--77). IEEE. https://doi.org/10.1109/ProTools49597.2019.00015

Islam, T. Z. (2019, March). SCR: Scalable Checkpoint / Restart (SCR) Library. Retrieved from https://github.com/LLNL/scr

Patki, T., Thiagarajan, J. J., Ayala, A., & Islam, T. Z. (2019). Performance optimality or reproducibility. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–30). ACM. https://doi.org/10.1145/3295500.3356217

Islam, T. Z. (2019). On-node scaling dataset on HPC systems. https://doi.org/10.5281/zenodo.4315003

Islam, T. Z., & Phelps, C. (2019, March). PyPerfdump. Retrieved from https://github.com/RECUP-DOE/pyperfdump

Thiagarajan, J. J., Anirudh, R., Kailkhura, B., Jain, N., Islam, T. Z., Bhatele, A., … Gamblin, T. (2018). PADDLE: Performance Analysis using a Data-driven Learning Environment (pp. 784--793). IEEE. https://doi.org/10.1109/IPDPS.2018.00088

Islam, T. Z., Majeske, N., Jagodzinski, F., & Hutchinson, B. (2018). Low Rank Smoothed Sampling Methods for Identifying Impactful Pairwise Mutations (pp. 681–686). ACM. https://doi.org/https://doi.org/10.1145/3233547.3233714

Moody, L., Pinnow, N., Lam, M. O., Menon, H., Schordan, M., Lloyd, G. S., & Islam, T. Z. (2018). Automatic Generation of Mixed-Precision Programs. Retrieved from https://sc18.supercomputing.org/proceedings/tech_poster/tech_poster_pages/post219.html

Islam, T. Z., Yu, W., Sato, K., Mohror, K., Zhu, Y., Moody, A., & Wang, T. (2017). MetaKV: A Key-Value Store for Metadata Management of Distributed Burst Buffer (pp. 1174–1183). https://doi.org/10.1109/IPDPS.2017.39

Banerjee, T., Hackl, J., Shringarpure, M., Islam, T. Z., Balachandar, S., Jackson, T., & Ranka, S. (2017). A new proxy application for compressible multiphase turbulent flows. Sustainable Computing: Informatics and Systems, 16, 11--24.

Islam, T. Z., Banerjee, T., Hackl, J., Shringarpure, M., Balanchandar, S., Jackson, T., & Ranka, S. (2016). CMT-Bone — A Proxy Application for Compressible Multiphase Turbulent Flows (pp. 173–182).

Islam, T. Z., Mohror, K., & Schulz, M. (2016). Exploring the Capabilities of the New MPI_T Interface. The International Journal of High Performance Computing Applications (IJHPCA), 30(2), 212--222. https://doi.org/10.1145/2642769.2642781

Banerjee, T., Hackl, J., Shringarpure, M., Islam, T. Z., Balachandar, S., Jackson, T., & Ranka, S. (2016). CMT-Bone — A Proxy Application for Compressible Multiphase Turbulent Flows. In IEEE 23rd International Conference on High Performance Computing (HiPC) (pp. 173–182). https://doi.org/10.1109/HiPC.2016.029

Islam, T. Z., Thiagarajan, J. J., Bhatele, A., Schulz, M., & Gamblin, T. (2016). A Machine Learning Framework for Performance Coverage Analysis of Proxy Applications. In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 538–549). IEEE. https://doi.org/10.1109/sc.2016.45

Islam, T. Z., Mohror, K., Rountree, B., Schulz, M., Mohror, K., Supinski, B. R., … Sevoie, L. (2016). I/O Aware Power Shifting (pp. 740–749).

Fang, A., Laguna, I., Sato, K., Islam, T. Z., & Mohror, K. (2015). Fault Tolerance Assistant (FTA): An Exception Handling Approach for MPI Programs. ExaMPI15 Exascale MPI at Supercomputing 2015 (SC15). IEEE.

Ni, X., Kale, L., Islam, T. Z., Mohror, K., & Moody, A. (2014). Lossy Compression for Checkpointing: Fallible or Feasible? ACM/IEEE. Retrieved from http://sc14.supercomputing.org/sites/all/themes/sc14/files/archive/tech_poster/tech_poster_pages/post271.html

Islam, T. Z., Rodgers, G. P., Hacker, T., & Anup, A. (2014). Batchsubmit: A high-volume Batch Submission System for Earthquake Engineering Simulation, 26, 2240–2252.

Islam, T. Z., Bagchi, S., & Eigenmann, R. (2014). Reliable and Efficient Distributed Checkpointing System for Grid Environments (Vol. 12, pp. 593–613).

Islam, T. Z., Tramn, J., Siegel, A., & Schulz, M. (2014). XSBench-the Development and Verification of a Performance Abstraction for Monte Carlo Reactor Analysis. Retrieved from https://www.mcs.anl.gov/papers/P5064-0114.pdf

Islam, T. Z. (2014, May). Gyan: Performance Measurement Tool for MPI implementations. Retrieved from https://github.com/LLNL/mpi-tools

Islam, T. Z. (2014). Reliable and Efficient Checkpoint/Recovery in Shared Grid Environments. Journal of Grid Computing, 12, 593--613. https://doi.org/https://doi.org/10.1007/s10723-014-9297-4

Islam, T. Z., Mohror, K., Bagchi, S., Moody, A., de Supinski, B. R., & Eigenmann, R. (2012). MCRENGINE: A Scalable Checkpointing System Using Data-Aware Aggregation and Compression (pp. 10--pages). https://doi.org/10.1109/SC.2012.77

Islam, T. Z., Bagchi, S., & Eigenmann, R. (2009). FALCON: A System for Reliable Checkpoint Recovery in Shared Grid Environments (pp. 1–12). ACM. https://doi.org/https://doi.org/10.1145/1654059.1654110

Hossain, M. S., Islam, T. Z., Bagchi, S., & Raghunathan, V. (2009). Fast and Collaborative Interference Avoidance for Wireless Medical Devices.

Islam, T. Z., Hossain, H., Ahmed, M., Al-Nayeem, A., & Akbar, M. M. (2007). gpNoCSim - A General Purpose Simulator for Network-On-Chip (pp. 254–257). IEEE. https://doi.org/10.1109/ICICT.2007.375388