Abhinav Jangda | DeepAI

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∙ 08/19/2023

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

Over the past few years, Large Language Models of Code (Code LLMs) have ...

0 Federico Cassano, et al. ∙

∙ 05/22/2023

A Framework for Fine-Grained Synchronization of Dependent GPU Kernels

Machine Learning (ML) models contain highly-parallel computations, such ...

0 Abhinav Jangda, et al. ∙

∙ 08/17/2022

MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation

Large language models have demonstrated the ability to generate both nat...

6 Federico Cassano, et al. ∙

∙ 05/12/2021

Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads

Recent trend towards increasing large machine learning models require bo...

0 Abhinav Jangda, et al. ∙

∙ 09/14/2020

NextDoor: GPU-Based Graph Sampling for Graph Machine Learning

Representation learning is a fundamental task in machine learning. It co...

0 Abhinav Jangda, et al. ∙

∙ 09/16/2019

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

The efficient execution of image processing pipelines on GPUs is an area...

0 Abhinav Jangda, et al. ∙

∙ 02/15/2019

Formal Foundations of Serverless Computing

A robust, large-scale web service can be difficult to engineer. When dem...

0 Abhinav Jangda, et al. ∙

∙ 01/25/2019

Not So Fast: Analyzing the Performance of WebAssembly vs. Native Code

All major web browsers now support WebAssembly, a low-level bytecode int...

0 Abhinav Jangda, et al. ∙

∙ 01/25/2019

Mind the Gap: Analyzing the Performance of WebAssembly vs. Native Code

All major web browsers now support WebAssembly, a low-level bytecode int...

0 Abhinav Jangda, et al. ∙

∙ 01/16/2019

Predicting Variable Types in Dynamically Typed Programming Languages

Dynamic Programming Languages are quite popular because they increase th...

0 Abhinav Jangda, et al. ∙

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