FTXS 2024

Important Dates

• Paper submissions open: July 1, 2024

• Paper submission closes:  August 1, 2024 August 8, 2024 (EXTENDED, new submissions are not permitted after this deadline but submitted papers may continue to updated until August 11, 2024)

• Author notification: September 5, 2024

• Camera-ready papers: September 27, 2024

• Workshop: Friday, November 22, 2024 @ 8:30am - 12:00pm EST

All deadlines are Anywhere-on-earth (AoE), the workshop start and end time are Eastern Standard Time.

Workshop Details

• WHEN : Friday, November 22, 2024 @ 8:30am - 12:00pm EST

• WHERE : Atlanta, GA, USA

• VENUE : Georgia World Congress Center 

• REGISTRATION : Register to attend SC24 HERE  (registration opens July 10, 2024)

• SUBMISSION : Papers should be submitted at: https://submissions.supercomputing.org/ 

• UPDATES : Follow us on Twitter ( @ftxsworkshop ) for the latest news and updates on the workshop

• QUESTIONS : contact Scott Levy (sllevy@sandia.gov) or Bo Fang (bo.fang@pnnl.gov)

Abstract: Machine learning (ML) has increasingly been adopted in safety-critical systems such as Autonomous vehicles (AVs) and industrial robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. This also applies to ML systems deployed in the HPC context. On the other hand, soft errors are becoming more frequent in commodity computer systems due to the effects of technology scaling and reduced supply voltages. Further, traditional solutions for masking hardware faults such as Triple-Modular Redundancy (TMR) are prohibitively expensive in terms of their energy and performance overheads. Therefore, there is a compelling need to provide low-cost error resilience to ML applications on commodity HPC platforms. 

I will present three directions we have explored in my research group towards this goal. First, we experimentally assessed the resilience of ML applications to soft errors via fault injection. We found that even a single bit flip due to a soft error can lead to misclassification in Deep Neural Network (DNN) applications. Such misclassifications can result in safety violations. However, not all errors result in safety violations, and hence it is sufficient to protect the DNN from the ones that do. Unfortunately, finding all possible errors that result in safety violations is a very compute-intensive task. Second, we proposed BinFI, a fault injection approach that efficiently injects critical faults that are highly likely to result in safety violations, by leveraging the DNN’s properties. Finally, we proposed Ranger, an approach to protect DNNs from critical faults without causing any loss in their accuracies, and with minimal performance overheads. I will conclude by presenting some of our ongoing work as well as the future challenges in this area.

This is joint work with my students and colleagues at UBC, as well as industry collaborators.

Bio: Karthik Pattabiraman is a Professor of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC). He received a PhD in 2009 in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), an MS in Computer Science also from UIUC in 2004, and B. Tech. from the University of Madras, India, in 2001. Before joining UBC in 2010, he was a postdoctoral researcher at Microsoft Research (MSR), Redmond. Karthik’s research interests are in dependable computer systems, high-performance computing, cyber-physical systems and software security. Karthik has won awards such as the Inaugural IEEE Rising Star in Dependability Award, UIUC CS department’s early career alumni achievement award, UBC-wide Killam mentoring excellence award, UBC-wide Killam Faculty Research Prize and the Killam Faculty Research Fellowship, NSERC Discovery Accelerator Supplement (DAS) in Canada, and the William Carter PhD Dissertation Award. Karthik is the vice-chair of the IFIP Working Group (WG) 10.4 on Dependable Computing and Fault-tolerance, and a member of the steering committee of the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). He is a distinguished member of the ACM, a distinguished contributor of the IEEE Computer Society, and a professional engineer (P.Eng.) in Canada. More info at: https://blogs.ubc.ca/karthik/

Workshop Program

FTXS 2024 will be held on Friday, November 22, 2024.  The tentative schedule for the workshop is provided below.  All times are Eastern Standard Time (UTC-5): the time zone of Atlanta, GA, USA where the conference will be held.  Papers will be published by the IEEE.

• [8:30am] Opening remarks

• [8:30-9:30am] Featured Speaker

Error-Resilient Machine Learning for HPC: Challenges and Opportunities

Karthik Pattabiraman (Univ. of British Columbia)

• [9:30-10:00am] 

Achieving High-Performance Fault-Tolerant Routing in HyperX Interconnection Networks 

Camarero, Cano, Martínez, Beivide

• [10:00-10:30am] SC24 Morning break

• [10:30-11:00am] 

From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments

George, Wang, Hanley, Ransom, Bent, Zimmer

• [11:00-11:30am] 

Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Pan, Chard, Zhou, Kamatar, Vescovi, Hayot-Sasson, Bauer, Gonthier, Chard, Foster

• [11:30am-12:00pm] 

Checkpointing strategies for a fixed-length execution

Benoit, Perotin, Robert, Vivien

• [12:00pm] Closing remarks

Publication

Subject to publisher constraints, our workshop will publish all submissions accepted for inclusion in our workshop.

Reproducibility

Reproducibility is an important component of extreme-scale system research.  However, the goal of our workshop is to enourage and facilitate discussion of novel approaches and preliminary results.  As a result, it may not always be feasible to release reproducibility artifacts.  Moreover, to the greatest extent possible, we want to minimize unnecessary obstacles to socializing new ideas.  Therefore, while we encourage to make their work as public and reproducible as possible, we do not explicitly require it. 

Diversity & Inclusivity

As part of SC24, FTXS is fully committed to addressing diversity and inclusivity at our workshop and in the larger HPC fault tolerance community (see here for more information SC24's commitment to inclusivity and diversity).   As a first step, we used an anonymous survey to collect demographic information about our Program Committee to ensure that we can measure our progress and so that we can be held accountable by the HPC community.   The results of this survey are included below.   Survey respondents were allowed to decline to answer any of the questions.  Because our committee comprises a relatively small number of people, we are not releasing exact numbers in an effort to protect their privacy.

• Approximately 2/3 of our Program Committee completed our anonymous demographics survey

• GEOGRAPHY

  • Approximately 2/5 of respondents reported North America as their primary work location

  • Approximately 1/6 of respondents reported Europe as their primary work location

• GENDER

  • Approximately 9/10 of respondents identify as male

• RACIAL & ETHNIC GROUPS

  • Approximately 1/10 of respondents identified as a racial or ethnic minority where they work