24 citations · 49 across the 25 of their papers we have counts for
7 papers · 1 filter
OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation
Tal Kadosh, Niranjan Hasabnis, Prema Soundararajan +5
Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core architectures. This pape…
Verified Code Transpilation with LLMs
Sahil Bhatia, Jie Qiu, Niranjan Hasabnis +2
Domain-specific languages (DSLs) are integral to various software workflows. Such languages offer domain-specific optimizations and abstractions that improve code readability and m…
Towards a high-performance AI compiler with upstream MLIR
Renato Golin, Lorenzo Chelini, Adam Siemieniuk +5
This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We d…
Tenspiler: A Verified Lifting-Based Compiler for Tensor Operations (Extended Version)
Jie Qiu, Colin Cai, Sahil Bhatia +3
Tensor processing infrastructures such as deep learning frameworks and specialized hardware accelerators have revolutionized how computationally intensive code from domains such as…
The Landscape and Challenges of HPC Research and LLMs
Le Chen, Nesreen K. Ahmed, Akash Dutta +14
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques ha…
MPIrigen: MPI Code Generation through Domain-Specific Language Models
Nadav Schneider, Niranjan Hasabnis, Vy A. Vo +9
The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of Message Passing Interface (…
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP
Le Chen, Arijit Bhattacharjee, Nesreen Ahmed +4
Large language models (LLMs)such as ChatGPT have significantly advanced the field of Natural Language Processing (NLP). This trend led to the development of code-based large langua…