Scholar
Abhinav Jangda
Google Scholar ID: T1bu2_IAAAAJ
Microsoft Research
Citations & Impact
Citations
1,198
H-index
11
i10-index
12
Publications
20
Co-authors
0
Contact
Publications
Resume
Academic Achievements
- Published multiple papers, including:
- - Fast Kronecker Matrix Multiplication on GPUs
- - A Framework for Fine-Grained Synchronization of Dependent GPU Kernels
- - Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
- - Accelerating Graph Sampling for Graph Machine Learning using GPUs
- - Model based Warp Overlapped Tiling for Image Processing Programs on GPUs
- - An Effective Fusion and Tile Size Model for PolyMage
- - Formal Foundations of Serverless Computing
- - Not so fast: Analyzing the Performance of WebAssembly vs. Native Code
- - Swizzle Inventor: Data Movement Synthesis for GPU Kernels
- - An Effective Fusion and Tile Size Model for Optimizing Image Processing Pipelines
- - Unbounded Superoptimization
- - FastCollect: Offloading Generational Garbage Collection on Integrated GPUs
- - Adaptive Just-In-Time Code Diversification
Research Experience
- Working in the Research in Software Engineering (RiSE) group at Microsoft Research.
Education
- PhD in Computer Science from College of Information and Computer Sciences, University of Massachusetts Amherst, advised by Prof. Arjun Guha and collaborated with Prof. Emery Berger and Prof. Marco Serafini.
Background
- Senior Researcher, working at the intersection of Programming Languages, Systems, and High Performance Computing.
Miscellany
- No personal interests or other information provided.