Abhinav Jangda

I am a Senior Researcher in the Research in Software Engineering (RiSE) group at Microsoft Research. I obtained my PhD in Computer Science from College of Information and Computer Sciences, University of Massachusetts Amherst. I was advised by Prof. Arjun Guha and collaborated with Prof. Emery Berger and Prof. Marco Serafini from UMass Amherst, and the Parasail Team at Microsoft Research. I work at the intersection of Programming Languages, Systems, and High Performance Computing.

Email: ajangda <at> microsoft <dot> com

Public Profiles:

DBLP: http://dblp.uni-trier.de/pers/hc/j/Jangda:Abhinav

Google Scholar: https://scholar.google.com/citations?user=T1bu2_IAAAAJ&hl=en

LinkedIn: https://www.linkedin.com/in/abhijangda

Publications

Abhinav Jangda and Mohit Yadav. Fast Kronecker Matrix Multiplication on GPUs. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP) 2024.
Abhinav Jangda, Saeed Maleki, Maryam Mehri Dehnavi, Madan Musuvathi, Olli Saarikivi. A Framework for Fine-Grained Synchronization of Dependent GPU Kernels. IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2024).
Abhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet, Saeed Maleki, Youshan Miao, Madanlal Musuvathi, Todd Mytkowicz, Olli Sarikivi. Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads. 27th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2022).
Abhinav Jangda, Sandeep Polisetty, Arjun Guha, and Marco Serafini. Accelerating Graph Sampling for Graph Machine Learning using GPUs. 16th European Conference on Systems (EuroSys 2021). Artifact Functional and Results Reproduced. [code][12min talk][20min talk]
Abhinav Jangda and Arjun Guha. Model based Warp Overlapped Tiling for Image Processing Programs on GPUs. International Conference on Parallel Architecture and Compilation Techniques 2020 (PACT 2020). Best Paper Award [code]
Abhinav Jangda and Uday Bondhugula. An Effective Fusion and Tile Size Model for PolyMage. ACM Transactions on Programming Languages and Systems (TOPLAS), Vol 43, Issue 3, November 2020. (Extended version of PPoPP 2018 paper) [code]
Abhinav Jangda, Donald Pinckney, Yuriy Brun, and Arjun Guha. Formal Foundations of Serverless Computing. ACM SIGPLAN Conference on Object Oriented Programming, Systems, Languages and Applications (OOPSLA), 2019 ACM SIGPLAN Distinguished Paper Award [code][talk video]
Abhinav Jangda, Bobby Powers, Emery D. Berger, and Arjun Guha. Not so fast: Analyzing the Performance of WebAssembly vs. Native Code. 2019 USENIX Annual Technical Conference (USENIX ATC' 2019) Invited for USENIX ;login: article [code][lightning talk video][talk video]
Phitchaya Mangpo Phothilimthana, Archibald Samuel Elliott, An Wang, Abhinav Jangda, Bastian Hagedorn, Henrik Barthels, Samuel J. Kaufman, Vinod Grover, Emina Torlak, and Rastislav Bodik. Swizzle Inventor: Data Movement Synthesis for GPU Kernels. 24th International Conference on Architectural Support for Programming Languages and Operating Systems [code][lightning talk video]
Abhinav Jangda and Uday Bondhugula. An Effective Fusion and Tile Size Model for Optimizing Image Processing Pipelines. ACM SIGPLAN symposium on Principles and Practice of Parallel Programming (PPoPP), Feb 2018 Artifact Functional and Results Reproduced [code]
Abhinav Jangda and Greta Yorsh. Unbounded Superoptimization. ACM Symposium on New Ideas in Programming and Reflections on Software 2017 (Onward 2017)
Abhinav Jangda and Rupesh Nasre. FastCollect: Offloading Generational Garbage Collection on Integrated GPUs. International Conference on Compilers, Architectures and Synthesis For Embedded Systems (CASES), ESWeek 2016
Abhinav Jangda, Mohit Mishra, and Bjorn De Sutter. Adaptive Just-In-Time Code Diversification. Proceedings of the Second ACM Workshop on Moving Target Defense, pages 49-53, Oct 2015