2 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks
Tal Kadosh, Niranjan Hasabnis, Vy A. Vo +10
With easier access to powerful compute resources, there is a growing trend in AI for software development to develop large language models (LLMs) to address a variety of programmin…
Scope is all you need: Transforming LLMs for HPC Code
Tal Kadosh, Niranjan Hasabnis, Vy A. Vo +9
With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to addres…
Quantifying OpenMP: Statistical Insights into Usage and Adoption
Tal Kadosh, Niranjan Hasabnis, Timothy Mattson +2
In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-c…
Advising OpenMP Parallelization via a Graph-Based Approach with Transformers
Tal Kadosh, Nadav Schneider, Niranjan Hasabnis +3
There is an ever-present need for shared memory parallelization schemes to exploit the full potential of multi-core architectures. The most common parallelization API addressing th…
MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
Nadav Schneider, Tal Kadosh, Niranjan Hasabnis +3
Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, perfor…