33 citations · 34 across the 5 of their papers we have counts for
5 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…
MAGNUS: Generating Data Locality to Accelerate Sparse Matrix-Matrix Multiplication on CPUs
Jordi Wolfson-Pou, Jan Laukemann, Fabrizio Petrini
Sparse general matrix-matrix multiplication (SpGEMM) is a critical operation in many applications. Current multithreaded implementations are based on Gustavson's algorithm and ofte…
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…
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…
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…