Ahmad Hossein Yazdani

My name is Ahmad Hossein Yazdani. I got my bachelor’s in computer software engineering from the University of Tehran Iran in 2020. Currently, I’m doing my PhD at Virginia Tech. University in Blacksburg, VA, US since August 2020 in computer science under the supervision of Dr. Ali Butt at Distributed System and Storage Lab. I have also experienced doing intern at Oak Ridge National Laboratory, supervised by Feiyi Wang, Sarp Oral, Ahmad Maroof Karimi, and Arnab Kamur Paul. Additionally, I have served as an intern at Lawrence Berkeley National Laboratory in Berkeley, California, the United States under the supervision of Suren Byna and Jean Luca Bez. which is to extend my research on identifying the sources of I/O variance in HPC systems. I recently did another internship at NERSC, Lawrence Berkeley National Laboratory (LBNL) which was on finding directions to resolve the false warnings of Drishti, and I/O recommender tool for HPC applications, and to concretize its recommendations (Like predicting the I/O performance when an unseen configuration is proposed) under the supervision of Stephen Simms, Lisa Gerhardt, and Jean Luca Bez.

I’m interested in distributed systems optimization, resource management, cloud computing, and high-performance computing (HPC). My current research is on optimizing distributed systems for running various aspects of Machine Learning including training deep neural networks, federated learning, and boosting GPU scheduling algorithms to fit ML workloads. Moreover, I research how to improve the I/O performance of HPC workloads, or to reduce I/O contention in other words. I’m also enthusiastic about learning and researching cloud computing.

news

Oct 02, 2024	I’m thrilled to announce that our paper titled “User-based I/O Profiling for Leadership Scale HPC Workloads” has been accepted to ICDCN25 to be held in Haydarabad, India January 4th to 7th 2025.
Jun 12, 2024	I’m honored to announce that I started off another internship at NERSC, Lawrence Berkeley National Laboratory in this day.
May 26, 2024	I had the honor to present a poster about our project on I/O interference characterization with the Berkeley Lab (LBNL) at the IPDPS PhD forum.
Jan 24, 2024	I’m honored to announce that I received an offer from USENIX to support my travel to FAST’24 in Santa Clara, CA.
Nov 12, 2023	I will be on a volunteership duty at Supercomputing’23 (SC’23) conference from today until November 18th in Denver, CO.

selected publications

ICDCN’25
User-based I/O Profiling for Leadership Scale HPC Workloads

Ahmad Hossein Yazdani, Arnab K. Paul, Ahmad Maroof Karimi, and 2 more authors

In Proceedings of the 26th International Conference on Distributed Computing and Networking, Heydarabad, India, Jan 2025

Abs DOI Bib

I/O constitutes a significant portion of most of the application run-time. Spawning many such applications concurrently on an HPC system leads to severe I/O contention. Thus, understanding and subsequently reducing I/O contention induced by such multi-tenancy is critical for the efficient and reliable performance of the HPC system. In this study, we demonstrate that an application’s performance is influenced by the command line arguments passed to the job submission. We model an application’s I/O behavior based on two factors: past I/O behavior within a time window and user-configured I/O settings via command-line arguments. We conclude that I/O patterns for well-known HPC applications like E3SM and LAMMP are predictable, with an average uncertainty below 0.25 (A probability of 80%) and near zero (A probability of 100%) within a day. However, I/O pattern variance increases as the study time window lengthens. Additionally, we show that for 38 users and at least 50 applications constituting approximately 93000 job submissions, there is a high correlation between a submitted command line and the past command lines made within 1 to 10 days submitted by the user. We claim the length of this time window is unique per user.
@inproceedings{10.1145/3700838.3700865, author = {Yazdani, Ahmad Hossein and Paul, Arnab K. and Karimi, Ahmad Maroof and Wang, Feiyi and Butt, Ali}, title = {User-based I/O Profiling for Leadership Scale HPC Workloads}, year = {2025}, isbn = {9798400710629}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3700838.3700865}, doi = {10.1145/3700838.3700865}, booktitle = {Proceedings of the 26th International Conference on Distributed Computing and Networking}, pages = {181–190}, numpages = {10}, keywords = {High Performance Computing, I/O characterization, I/O scheduler, Darshan, I/O profiling}, location = {Heydarabad, India}, series = {ICDCN '25}, month = jan, }

FAST’23

SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training

Redwan Ibne Seraj Khan, Ahmad Hossein Yazdani, Yuqi Fu, and 5 more authors

In 21st USENIX Conference on File and Storage Technologies (FAST 23), Feb 2023

Bib PDF Code

@inproceedings{285774,
  author = {Khan, Redwan Ibne Seraj and Yazdani, Ahmad Hossein and Fu, Yuqi and Paul, Arnab K. and Ji, Bo and Jian, Xun and Cheng, Yue and Butt, Ali R.},
  title = {{SHADE}: Enable Fundamental Cacheability for Distributed Deep Learning Training},
  booktitle = {21st USENIX Conference on File and Storage Technologies (FAST 23)},
  year = {2023},
  isbn = {978-1-939133-32-8},
  address = {Santa Clara, CA},
  pages = {135--152},
  url = {https://www.usenix.org/conference/fast23/presentation/khan},
  publisher = {USENIX Association},
  month = feb,
}