SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication

Thumbnail Image

View/Download File

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication

Published Date






Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal processing and recommender systems. Real-world tensors can be enormous in size and often very sparse. There is a need for efficient, high-performance tools capable of processing the massive sparse tensors of today and the future. This paper introduces SPLATT, a C library with shared-memory parallelism for three-mode tensors. SPLATT contains algorithmic improvements over competing state of the art tools for sparse tensor factorization. SPLATT has a fast, parallel method of multiplying a matricized tensor by a Khatri-Rao product, which is a key kernel in tensor factorization methods. SPLATT uses a novel data structure that exploits the sparsity patterns of tensors. This data structure has a small memory footprint similar to competing methods and allows for the computational improvements featured in our work. We also present a method of finding cache-friendly reorderings and utilizing them with a novel form of cache tiling. To our knowledge, this is the first work to investigate reordering and cache tiling in this context. SPLATT averages almost 30x speedup compared to our baseline when using 16 threads and reaches over 80x speedup on NELL-2.



Related to



Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Smith, Shaden; Ravindran, Niranjay; Sidiropoulos, Nicholas D.; Karypis, George. (2015). SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215973.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.