Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parallel Processing01:20

Parallel Processing

632
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
632
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

288
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
288

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

LeafLiteX mobile application for leaf disease detection using U-Net segmentation and lightweight deep learning.

Scientific reports·2026
Same author

Comprehensive review of heart disease prediction: A comparative study from 2019 onwards.

Artificial intelligence in medicine·2026
Same author

A Unified Hybrid Model for Cardiovascular Risk Prediction: Merging Statistical, Kernel-Based and Neural Approaches.

Journal of cellular and molecular medicine·2025
Same author

Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging.

Frontiers in human neuroscience·2025
Same author

Integrative approach for efficient detection of kidney stones based on improved deep neural network architecture.

SLAS technology·2024

Related Experiment Video

Updated: Jan 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

High performance GPU implementation of KNN algorithm: A review.

Pooja Bidye1, Pradnya Borkar1, Nitin Rakesh1

  • 1Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, 412115, India.

Methodsx
|October 6, 2025
PubMed
Summary
This summary is machine-generated.

Optimizing the K-Nearest Neighbor (KNN) algorithm using High-Performance Computing (HPC) on Graphics Processing Units (GPUs) significantly accelerates processing for complex, high-dimensional datasets. Techniques like memory access optimization and data segmentation achieve substantial speedups, enhancing machine learning performance.

Keywords:
Graphics processing unitHigh performance computingK-nearest neighborMachine learning

Related Experiment Videos

Last Updated: Jan 16, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

Area of Science:

  • Computer Science
  • Machine Learning
  • High-Performance Computing

Background:

  • Machine Learning (ML) algorithms struggle with large, complex datasets on standard CPUs.
  • The K-Nearest Neighbor (KNN) algorithm, while widely used, faces performance challenges with high-dimensional data.

Purpose of the Study:

  • To review optimization techniques for accelerating the KNN algorithm on GPU platforms.
  • To assess the impact of these optimizations on high-dimensional datasets within an HPC environment.

Main Methods:

  • Review of research on KNN algorithm parallelization and optimization for GPUs.
  • Analysis of techniques including coalesced-memory access, tiling, chunking, data segmentation, and pivot-based partitioning.

Main Results:

  • Optimized KNN algorithms leveraging GPU capabilities achieved significant speedups.
  • Speedups reached up to 750x on dual-GPU and 1840x on multi-GPU platforms for high-dimensional data.

Conclusions:

  • GPU-based optimization techniques are crucial for accelerating KNN on large, high-dimensional datasets.
  • This study provides valuable insights for researchers in HPC and ML applications.