1 citations · 1 across the 5 of their papers we have counts for
8 papers
Co-design for Trustworthy AI: An Interpretable and Explainable Tool for Type 2 Diabetes Prediction Using Genomic Polygenic Risk Scores
Ralf Beuthan, Megan Coffee, Heejin Kim +13
The polygenic risk scores (PRS) have emerged as an important methodology for quantifying genetic predisposition to complex traits and clinical disease. Significant progress has bee…
ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings
Ahmed E. Helal, Fabio Checconi, Jan Laukemann +4
Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on mo…
Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
Jesmin Jahan Tithi, Hanjiang Wu, Avishaii Abuhatzera +1
The explosive growth of Large Language Models (LLMs), such as GPT-4 with 1.8 trillion parameters, demands a fundamental rethinking of data center architecture to ensure scalability…
Enhancing Scalability and Performance in Influence Maximization with Optimized Parallel Processing
Hanjiang Wu, Huan Xu, Joongun Park +5
Influence Maximization (IM) is vital in viral marketing and biological network analysis for identifying key influencers. Given its NP-hard nature, approximate solutions are employe…
Efficient Parallel Multi-Hop Reasoning: A Scalable Approach for Knowledge Graph Analysis
Jesmin Jahan Tithi, Fabio Checconi, Fabrizio Petrini
Multi-hop reasoning (MHR) is a process in artificial intelligence and natural language processing where a system needs to make multiple inferential steps to arrive at a conclusion…
Performance Optimization of SU3_Bench on Xeon and Programmable Integrated Unified Memory Architecture
Jesmin Jahan Tithi, Fabio Checconi, Douglas Doerfler +1
SU3\_Bench is a microbenchmark developed to explore performance portability across multiple programming models/methodologies using a simple, but nontrivial, mathematical kernel. Th…