23 citations · 102 across the 34 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
DeepRace: Finding Data Race Bugs via Deep Learning
Ali Tehrani, Mohammed Khaleel, Reza Akbari +1
With the proliferation of multi-core hardware, parallel programs have become ubiquitous. These programs have their own type of bugs known as concurrency bugs and among them, data r…