Incremental Sparse Tensor Format for Maximizing Efficiency in Tensor-Vector Multiplications
Additional information
Authors
Niu X.,
Meyer G.,
Pasadakis D.,
Yzelman A. J.,
Schenk O.
Type
Article in conference proceedings
Year
2025
Language
English
Abstract
Current state-of-the-art sparse tensor formats achieve memory-efficient representations but often require extensive indexing or precomputation, thus limiting their flexibility and efficiency. We propose Bit-IF (Incremental Sparse Fibers with Bit Encoding), a format that only records index increments encoded by a compact bit array. This mode-independent approach allows for an arbitrary index traversal during tensor-vector multiplications (TVM), enabling the usage of space-filling curves. Bit-IF’s design characteristics significantly reduce memory overhead, improve data locality, and eliminate the need for multiple tensor copies or mode-specific preprocessing before performing a TVM. Our analysis and initial comparative studies show that Bit-IF reduces memory consumption and TVM computation time compared to COO-based approaches. The applicability of this method could be extended to other tensor operations, such as tensor-matrix and
Khatri-Rao products.
Keywords
Tensors, Limiting, Algebra, Conferences, High performance computing, Memory management, Cluster computing, Encoding, Arrays, Indexing, Sparse tensor format, Incremental indexes, Tensor algebra
Conference proceedings
2025 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops)
Numero ( Mese )
September
Publisher
IEEE
Meeting name
IEEE Cluster 2025
Meeting place
Edinburgh, United Kingdom
Meeting date
September 2nd-5th, 2025
Pages (or article number)
1-2
