Faculty Profiles

Banday, B., Islam, T. Z., Thopalli, K., & Thiagarajan, J. J. (n.d.). On the role of prompt construction in enhancing efficacy and efficiency of llm-based tabular data generation.

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/

Kelly, C., Xu, W., Pouchard, L., Van Dam, H., Islam, T. Z., Yoo, S., & Van Dam, K. K. (n.d.). Performance Analysis and Data Reduction for Exascale Scientific Workflows. International Journal of High Performance Computing Applications.

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

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. https://doi.org/10.1109/HiPCW54834.2021.00011

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. L., 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).

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

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

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. 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