Ali Butt

About

Ali Butt is a Professor and Associate Department Head for Faculty Development in the Department of Computer Science at Virginia Tech. He is also the Director of the stack@cs Center for Computer Systems. His research interests include cloud and high-performance computing systems, systems support for machine and deep learning applications, file, I/O, and storage systems, distributed systems, and large-scale experimental computer systems. He holds a Ph.D. in electrical and computer engineering from Purdue University, obtained in 2006. His professional location includes Gilbert Place RM 4108 at Virginia Tech, and he is involved in various research and academic activities related to computer systems and infrastructure.

Research topics

• Computer Science
• Operating system
• Machine Learning
• Distributed computing
• Data Mining
• Algorithm
• Artificial Intelligence
• Parallel computing
• Mathematics
• Geometry

Selected publications

• Large-Scale Analysis of Docker Images and Performance Implications for Container Storage Systems

  IEEE Transactions on Parallel and Distributed Systems · 2020 · 59 citations

  Senior authorCorresponding

  Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the application’s execution environment. Containers are created from images which are shared between users via a registry. The amount of data registries store is massive. For example, Docker Hub, a popular public registry, stores at least half a million public images. In this article, we analyze over 167 TB of…

  DOI

• An Analysis of System Balance and Architectural Trends Based on Top500 Supercomputers

  2021 · 33 citations

  Supercomputer design is a complex, multi-dimensional optimization process, wherein several subsystems need to be reconciled to meet a desired figure of merit performance for a portfolio of applications and a budget constraint. However, overall, the HPC community has been gravitating towards ever more Flops, at the expense of many other subsystems. To draw attention to overall system balance, in this paper, we analyze balance ratios and architectural trends in the world’s most powerful supercompu…

  DOI

• On the Use of Containers in High Performance Computing Environments

  2020 · 30 citations

  Senior authorCorresponding

  The lightweight nature, application portability, and deployment flexibility of containers is driving their widespread adoption in cloud solutions. Data analysis and deep learning (DL)/machine learning (ML) applications have especially benefited from containerization. As such data analysis is adopted in high performance computing (HPC), the need for container support in HPC has become paramount. However, containers face crucial performance and I/O challenges in HPC. One obstacle is that while the…

  DOI

• A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning

  IEEE Access · 2023-01-01 · 28 citations

  articleOpen access

  Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to adversarial attacks, which can make the DL techniques perform poorly. Developing such attacks and their countermeasures is the prerequisite for making artificial intelligence techniques robust, secure, and deployable. Previous survey papers only fo…

  PublisherOA PDFDOI

• FLOAT: Federated Learning Optimizations with Automated Tuning

  2024-04-18 · 18 citations

  articleOpen access

  Federated Learning (FL) has emerged as a powerful approach that enables collaborative distributed model training without the need for data sharing. However, FL grapples with inherent heterogeneity challenges leading to issues such as stragglers, dropouts, and performance variations. Selection of clients to run an FL instance is crucial, but existing strategies introduce biases and participation issues and do not consider resource efficiency. Communication and training acceleration solutions prop…

  PublisherDOI

Recent grants

• CSR: Small: Towards Realizing Cloud HPC: An Adaptive Programming Model for Accelerator-based Clusters

  NSF · $442k · 2010–2014

• SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications

  NSF · $969k · 2019–2024

• CSR: Small: Collaborative Research: Scalable Fine-Grained Cloud Monitoring for Empowering IoT

  NSF · $258k · 2016–2020

Frequent coauthors

• Ali Anwar

  37 shared

• Yue Cheng

  University of Virginia

  32 shared

• M. Mustafa Rafique

  Rochester Institute of Technology

  26 shared

• Sudharshan S. Vazhkudai

  Micron (United States)

  21 shared

• Kirk W. Cameron

  Virginia Tech

  19 shared

• Arnab K. Paul

  Birla Institute of Technology and Science, Pilani

  17 shared

• Guanying Wang

  16 shared

• Thomas Lux

  14 shared

Similar researchers at Virginia Tech

• Wenjing Louh-77
• Doug Bowmanh-56
• Wu-chun Fengh-46