About the author

I am a graduate student at the University of British Colombia. I completed my undergraduate from the Netaji Subhas University of Technology (NSUT), India, with my degree in the field of Electronics and Communication Engineering (ECE). I have a keen interest in Operating systems, Program Analysis, and compilers.

Github Profile link:

       LinkedIn Profile:   https://www.linkedin.com/in/udit-agarwal-517359125/

Personality type:    INFJ

~~ Career Timeline ~~

January, 2023
Intel Corporation, Canada
Software Intern

I will be working towards code generation in Intel's SYCL compiler.

July, 2022
LLTFI: Framework Agnostic
Fault Injection for Machine
Learning Applications
Sept, 2021
University of British Columbia,
Masters in Applied Science (MASc)

Advisor: Karthik Pattabiraman

Specialization: Error Resilient computing

August, 2021
Nekara: Generalized Concurrency
August, 2020
Microsoft Research, India
Research Intern

Advisor: Akash Lal.

Worked on the development and benchmarking of a language - agnostic, systematic concurrency testing framework.

Aug, 2019
IIIT-Delhi, New Delhi
Research Assistant

Advisor: Rahul Purandare.

Worked on aiding program comprehension through the extraction of object-oriented features like class relationships from X86 executables, DLLs and representing them as UML class diagrams.

May, 2019
University of Delhi, New Delhi

Graduated from the University of Delhi - First class with distinction 🙂

April, 2019
University of Delhi, New Delhi
Bachelor's thesis defense

Advisor: Kunwar Singh

Successfully did my  thesis defense, Yipieeee!!
Specialization: Neuromorphic CMOS-Memristor hybrid ICs.
My thesis: LINK

May-August, 2018
Google Summer of Code

Advisor: Gedare Bloom

* One of the 1268 students selected by Google worldwide to work with leading open source organizations;
* Worked on Real-Time Executive for multi-processor systems (RTEMS) and Flexible I/O tester (FIO) to develop a port and use it for benchmarking various In-memory, Flash and Block filesystems supported by RTEMS.

August, 2015
University of Delhi, New Delhi
Undergraduate Student

Specialization: Electronics and Communication Engineering.


[ISSRE’22]  LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications

Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2022. (Acceptance Rate: 29%) [ PDF | Talk (video) ] (Code)

Abstract: As machine learning (ML) has become more prevalent across many critical domains, so has the need to understand ML applications’ resilience. While prior work like TensorFI [1], MindFI [2], and PyTorchFI [3] has focused on building ML fault injectors for specific ML frameworks, there has been little work on performing fault injection (FI) for ML applications written in multiple frameworks. We present LLTFI, a Framework-Agnostic Fault Injection tool for ML applications, allowing users to run FI experiments on ML applications at the LLVM IR level. LLTFI provides users with finer FI granularity at the level of instructions and a better understanding of how faults manifest and propagate between different ML components. We evaluate LLTFI on six ML programs and compare it with TensorFI. We found significant differences in the Silent Data Corruption (SDC) rates for similar faults between the two tools. Finally, we use LLTFI to evaluate the efficacy of selective instruction duplication – an error mitigation technique – for ML programs.

[ASE’21]  Nekara: Generalized Consistency Testing

Udit Agarwal, Pantazis Deligiannis, Cheng Huang, Kumseok Jung, Akash Lal, Immad Naseer, Matthew Parkinson, Arun Thangamani, Jyothi Vedurada, Yunpeng Xiao, Proceedings of the ACM/IEEE International Conference on Automated Software Engineering (ASE), 2021. [ PDF | Talk Slides]

Abstract: Testing concurrent systems remains an uncomfortable problem for developers. The common industrial practice is to stress-test a system against large workloads, with the hope of triggering enough corner-case interleavings that reveal bugs. However, stress testing is often inefficient and its ability to get coverage of interleavings is unclear. In reaction, the research community has proposed the idea of systematic testing, where a tool takes over the scheduling of concurrent actions so that it can explore the space of interleavings.

We present an experience paper on the application of systematic testing to several case studies. We separate the algorithmic advancements in prior work (on searching the large space of interleavings) from the engineering of their tools. The latter was unsatisfactory; often the tools were limited to a small domain, hard to maintain, and hard to extend to other domains. We designed Nekara, an open-source cross-platform library for easily building custom systematic testing solutions.

We show that (1) Nekara can effectively encapsulate state-of-the-art exploration algorithms by evaluating on prior benchmarks, and (2) Nekara can be applied to a wide variety of scenarios, including existing open-source systems as well as cloud services of a major IT company. Nekara was easy to use, improved testing, and found multiple new bugs.