GPU Semiring Primitives for Sparse Neighborhood Methods
This work enables many neighborhood-based information retrieval and machine learning algorithms to handle sparse input on GPUs, though it is incremental as it builds on existing GPU primitives.
The authors tackled the challenge of efficiently computing various distance measures on sparse vectors using GPUs, by introducing a flexible sparse semiring primitive that maintains performance and memory efficiency while supporting multiple critical distance measures.
High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations. We demonstrate that a sparse semiring primitive can be flexible enough to support a wide range of critical distance measures while maintaining performance and memory efficiency on the GPU. We further show that this primitive is a foundational component for enabling many neighborhood-based information retrieval and machine learning algorithms to accept sparse input. To our knowledge, this is the first work aiming to unify the computation of several critical distance measures on the GPU under a single flexible design paradigm and we hope that it provides a good baseline for future research in this area. Our implementation is fully open source and publicly available as part of the RAFT library of GPU-accelerated machine learning primitives (https://github.com/rapidsai/raft).