33 citations · 52 across the 9 of their papers we have counts for
9 papers
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…
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…
Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation
Jan Laukemann, Ahmed E. Helal, S. Isaac Geronimo Anderson +7
High-dimensional sparse data emerge in many critical application domains such as healthcare and cybersecurity. To extract meaningful insights from massive volumes of these multi-di…
Ridgeline: A 2D Roofline Model for Distributed Systems
Fabio Checconi, Jesmin Jahan Tithi, Fabrizio Petrini
In this short paper, we introduce the Ridgeline model, an extension of the Roofline model [4] for distributed systems. The Roofline model targets shared memory systems, bounding th…
Efficient, Out-of-Memory Sparse MTTKRP on Massively Parallel Architectures
Andy Nguyen, Ahmed E. Helal, Fabio Checconi +6
Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accele…
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…
ALTO: Adaptive Linearized Storage of Sparse Tensors
Ahmed E. Helal, Jan Laukemann, Fabio Checconi +4
The analysis of high-dimensional sparse data is becoming increasingly popular in many important domains. However, real-world sparse tensors are challenging to process due to their…
PIUMA: Programmable Integrated Unified Memory Architecture
Sriram Aananthakrishnan, Shamsul Abedin, Vincent Cave +16
High performance large scale graph analytics are essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource u…