Giga-scale Kernel Matrix-Vector Multiplication on GPU
Authors: Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that F3M has empirical linear time and memory complexity with a relative error of order 10 3 and can compute a full KMVM for a billion points in under a minute on a high-end GPU, leading to a significant speed-up in comparison to existing CPU methods. |
| Researcher Affiliation | Collaboration | Robert Hu Amazon robyhu@amazon.co.uk Siu Lun Chau Department of Statistics University of Oxford siu.chau@stats.ox.ac.uk Dino Sejdinovic School of Computer and Mathematical Sciences University of Adelaide dino.sejdinovic@adelaide.edu.au Joan Alexis Glaunès MAP5 Université Paris Descartes alexis.glaunes@mi.parisdescartes.fr |
| Pseudocode | No | The paper does not contain a dedicated pseudocode block or algorithm listing. |
| Open Source Code | Yes | Codebase is released here [14]. |
| Open Datasets | Yes | We consider uniformly and normally sampled data, the Open Street Map (OSM) dataset [1] and a classification task on the NYC Taxi dataset [2]... We mimic the setup in [32] and consider the datasets 3DRoad, Song, Buzz and House Electric... |
| Dataset Splits | No | The paper describes error calculation, not a training/validation split for the model's overall performance. No explicit validation set is mentioned for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | All experiments were run on NVIDIA V100-32GB cards, where the data is fitted entirely on the GPU. |
| Software Dependencies | No | The paper mentions 'Lib Torch' (PyTorch) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The parameters used for F3M are η = 0.1, 0.2, 0.3, 0.5 and r = 2D, 3D, 4D with a cap at r = 2048. |