24 citations · 49 across the 25 of their papers we have counts for
27 papers
ParBench: A Benchmark for Reliable Evaluation of LLM Parallel Code Translation
Samyak Jhaveri, Erel Kaplan, Tom Yotam +4
Modern compute-intensive software must migrate across a changing ecosystem of accelerators, programming APIs, compiler stacks, and portability layers, including CUDA, OpenMP, OpenC…
Latent Reasoning Guidance for Parallel Code Translation
Tomer Bitan, Erel Kaplan, Roee Bar-Yadin +5
Tackling complex coding tasks often requires autonomous agents and iterative repair pipelines. These increasingly rely on large amounts of test-time computation, often spending man…
ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation
Erel Kaplan, Tomer Bitan, Lian Ghrayeb +4
Parallel programming is central to HPC and AI, but producing code that is correct and fast remains challenging, especially for OpenMP GPU offload, where data movement and tuning do…
Counting Without Running: Evaluating LLMs' Reasoning About Code Complexity
Gregory Bolet, Giorgis Georgakoudis, Konstantinos Parasyris +4
Modern GPU software stacks demand developers who can anticipate performance bottlenecks before ever launching a kernel; misjudging floating-point workloads upstream can derail tuni…
OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms
Arijit Bhattacharjee, Ali TehraniJamsaz, Le Chen +4
Recent advances in large language models (LLMs) have significantly accelerated progress in code translation, enabling more accurate and efficient transformation across programming…
UniPar: A Unified LLM-Based Framework for Parallel and Accelerated Code Translation in HPC
Tomer Bitan, Tal Kadosh, Erel Kaplan +5
Translating programs between various parallel programming languages is an important problem in the high-performance computing (HPC) community. Existing tools for this problem are e…
Can Large Language Models Predict Parallel Code Performance?
Gregory Bolet, Giorgis Georgakoudis, Harshitha Menon +5
Accurate determination of the performance of parallel GPU code typically requires execution-time profiling on target hardware -- an increasingly prohibitive step due to limited acc…
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…
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…
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset
Le Chen, Arijit Bhattacharjee, Nesreen K. Ahmed +4
Large language models (LLMs) have become increasingly prominent in academia and industry due to their remarkable performance in diverse applications. As these models evolve with in…
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…
CWD: A Machine Learning based Approach to Detect Unknown Cloud Workloads
Mohammad Hossain, Derssie Mebratu, Niranjan Hasabnis +3
Workloads in modern cloud data centers are becoming increasingly complex. The number of workloads running in cloud data centers has been growing exponentially for the last few year…
Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?
Niranjan Hasabnis
Machine programming (MP) is an emerging field at the intersection of deterministic and probabilistic computing, and it aims to assist software and hardware engineers, among other a…
GitRank: A Framework to Rank GitHub Repositories
Niranjan Hasabnis
Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering…
Automatic Tuning of Tensorflow's CPU Backend using Gradient-Free Optimization Algorithms
Derssie Mebratu, Niranjan Hasabnis, Pietro Mercati +2
Modern deep learning (DL) applications are built using DL libraries and frameworks such as TensorFlow and PyTorch. These frameworks have complex parameters and tuning them to obtai…