6 citations · 6 across the 9 of their papers we have counts for
11 papers
Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking
Md Arafat Hossain, Thomas Randall, Akash Dutta +3
As the complexity of High Performance Computing (HPC) ecosys- tems continually increases, achieving optimal performance becomes a challenge. Traditional performance autotuning tech…
SuperSAM: Crafting a SAM Supernetwork via Structured Pruning and Unstructured Parameter Prioritization
Waqwoya Abebe, Sadegh Jafari, Sixing Yu +6
Neural Architecture Search (NAS) is a powerful approach of automating the design of efficient neural architectures. In contrast to traditional NAS methods, recently proposed one-sh…
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
Akash Dutta, Ali Jannesari
One of the primary areas of interest in High Performance Computing is the improvement of performance of parallel workloads. Nowadays, compilable source code-based optimization task…
Static Generation of Efficient OpenMP Offload Data Mappings
Luke Marzen, Akash Dutta, Ali Jannesari
Increasing heterogeneity in HPC architectures and compiler advancements have led to OpenMP being frequently used to enable computations on heterogeneous devices. However, the effic…
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…
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…