3 papers
cs.LG2025
ReLATE: Learning Efficient Sparse Encoding for High-Performance Tensor Decomposition
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…
cs.DC2025
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…
cs.DC2025
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…