24 citations · 49 across the 25 of their papers we have counts for
12 papers · 1 filter
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…
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 (…
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…
Auto-tuning TensorFlow Threading Model for CPU Backend
Niranjan Hasabnis
TensorFlow is a popular deep learning framework used by data scientists to solve a wide-range of machine learning and deep learning problems such as image classification and speech…