23 citations · 102 across the 34 of their papers we have counts for
37 papers
A Lock-Free Work-Stealing Algorithm for Bulk Operations
Raja Sai Nandhan Yadav Kataru, Danial Davarnia, Ali Jannesari
Work-stealing is a widely used technique for balancing irregular parallel workloads, and most modern runtime systems adopt lock-free work-stealing deques to reduce contention and i…
Data Race Satisfiability on Array Elements
Junhyung Shim, Quazi Ishtiaque Mahmud, Ali Jannesari
Detection of data races is one of the most important tasks for verifying the correctness of OpenMP parallel codes. Two main models of analysis tools have been proposed for detectin…
MPI Errors Detection using GNN Embedding and Vector Embedding over LLVM IR
Jad El Karchi, Hanze Chen, Ali TehraniJamsaz +3
Identifying errors in parallel MPI programs is a challenging task. Despite the growing number of verification tools, debugging parallel programs remains a significant challenge. Th…
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…
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Federated learning (FL) offers privacy-preserving decentralized machine learning, optimizing models at edge clients without sharing private data. Simultaneously, foundation models…
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark
Hung Phan, Ali Jannesari
Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of to…
PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
Ali TehraniJamsaz, Quazi Ishtiaque Mahmud, Le Chen +2
The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis. However, a key challenge in adopti…
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
Le Chen, Quazi Ishtiaque Mahmud, Hung Phan +2
Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis an…
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amo…
Redundancy and Concept Analysis for Code-trained Language Models
Arushi Sharma, Zefu Hu, Christopher Quinn +1
Code-trained language models have proven to be highly effective for various code intelligence tasks. However, they can be challenging to train and deploy for many software engineer…
Performance Optimization using Multimodal Modeling and Heterogeneous GNN
Akash Dutta, Jordi Alcaraz, Ali TehraniJamsaz +3
Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a mu…
GraphBinMatch: Graph-based Similarity Learning for Cross-Language Binary and Source Code Matching
Ali TehraniJamsaz, Hanze Chen, Ali Jannesari
Matching binary to source code and vice versa has various applications in different fields, such as computer security, software engineering, and reverse engineering. Even though th…
ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels
Ali TehraniJamsaz, Alok Mishra, Akash Dutta +3
GPU-based HPC clusters are attracting more scientific application developers due to their extensive parallelism and energy efficiency. In order to achieve portability among a varie…
Power Constrained Autotuning using Graph Neural Networks
Akash Dutta, Jee Choi, Ali Jannesari
Recent advances in multi and many-core processors have led to significant improvements in the performance of scientific computing applications. However, the addition of a large num…
Accelerating Domain-aware Deep Learning Models with Distributed Training
Aishwarya Sarkar, Chaoqun Lu, Ali Jannesari
Recent advances in data-generating techniques led to an explosive growth of geo-spatiotemporal data. In domains such as hydrology, ecology, and transportation, interpreting the com…
Addressing Data Heterogeneity in Decentralized Learning via Topological Pre-processing
Waqwoya Abebe, Ali Jannesari
Recently, local peer topology has been shown to influence the overall convergence of decentralized learning (DL) graphs in the presence of data heterogeneity. In this paper, we dem…
Towards Seamless Management of AI Models in High-Performance Computing
Sixing Yu, Murali Emani, Chunhua Liao +4
With the increasing prevalence of artificial intelligence (AI) in diverse science/engineering communities, AI models emerge on an unprecedented scale among various domains. However…
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion
Duy Phuong Nguyen, Sixing Yu, J. Pablo Muñoz +1
Concerned with user data privacy, this paper presents a new federated learning (FL) method that trains machine learning models on edge devices without accessing sensitive data. Tra…
Heterogeneous Graph Neural Networks for Software Effort Estimation
Hung Phan, Ali Jannesari
Software effort can be measured by story point [35]. Current approaches for automatically estimating story points focus on applying pre-trained embedding models and deep learning f…
Story Point Effort Estimation by Text Level Graph Neural Network
Hung Phan, Ali Jannesari
Estimating the software projects' efforts developed by agile methods is important for project managers or technical leads. It provides a summary as a first view of how many hours a…
Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization
Ali TehraniJamsaz, Mihail Popov, Akash Dutta +2
There is a large space of NUMA and hardware prefetcher configurations that can significantly impact the performance of an application. Previous studies have demonstrated how a mode…
CLAWS: Contrastive Learning with hard Attention and Weak Supervision
Jansel Herrera-Gerena, Ramakrishnan Sundareswaran, John Just +2
Learning effective visual representations without human supervision is a long-standing problem in computer vision. Recent advances in self-supervised learning algorithms have utili…
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data
Aishwarya Sarkar, Jien Zhang, Chaoqun Lu +1
Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods. Limited evidence about hidd…
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Sixing Yu, Phuong Nguyen, Waqwoya Abebe +3
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive comm…