Search Results for author:
Found 16 papers, 7 papers with code
MPIrigen: MPI Code Generation through Domain-Specific Language Models
This study first investigates the performance of state-of-the-art language models in generating MPI-based parallel programs.
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP
Large language models (LLMs), as epitomized by models like ChatGPT, have revolutionized the field of natural language processing (NLP).
Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks
Specifically, we start off with HPC as a domain and build an HPC-specific LM, named MonoCoder, that is orders of magnitude smaller than existing LMs but delivers similar, if not better performance, on non-HPC and HPC tasks.
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset
The evaluation of CompCodeVet on two open-source code datasets shows that CompCodeVet has the ability to improve the training dataset quality for LLMs.
Scope is all you need: Transforming LLMs for HPC Code
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 address a variety of programming tasks.
MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers
Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes.
Advising OpenMP Parallelization via a Graph-Based Approach with Transformers
Specifically, we propose a novel approach, called OMPify, to detect and predict the OpenMP pragmas and shared-memory attributes in parallel code, given its serial version.
CWD: A Machine Learning based Approach to Detect Unknown Cloud Workloads
To address this problem of realizing the full potential of the underlying platform, we develop a machine learning based technique to characterize, profile and predict workloads running in the cloud environment.
Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?
We believe that our findings also generate interesting insights towards code quality measures that affect performance of MP systems.
GitRank: A Framework to Rank GitHub Repositories
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
In this paper, we treat the problem of tuning parameters of DL frameworks to improve training and inference performance as a black-box optimization problem.
ControlFlag: A Self-Supervised Idiosyncratic Pattern Detection System for Software Control Structures
Software debugging has been shown to utilize upwards of half of developers' time.
(Updated submission 11/20/2020) MISIM: A Novel Code Similarity System
First, MISIM uses a novel context-aware semantic structure (CASS), which is designed to aid in lifting semantic meaning from code syntax.
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection.
Auto-tuning TensorFlow Threading Model for CPU Backend
In this paper, we develop an automatic approach, called TensorTuner, to search for optimal parameter settings of TensorFlow's threading model for CPU backends.
