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 Experiment Video

Updated: Apr 20, 2026

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.8K

Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems.

George Teodoro, Tahsin M Kurc, Tony Pan

    Proceedings. IPDPS (Conference)
    |November 25, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Parallel Processing01:20

    Parallel Processing

    928
    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...
    928

    You might also read

    Related Articles

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

    Sort by
    Same author

    Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities.

    American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting·2026
    Same author

    Machine learning identification of maternal inflammatory response and systemic inflammatory response from placental membrane whole slide images.

    Placenta·2026
    Same author

    Impact of Concurrent Graft-versus-Host Disease on Long-Term Survival in Critically Ill Patients Following Allogeneic Hematopoietic Stem Cell Transplantation for Hematological Malignancies.

    Transplantation and cellular therapy·2026
    Same author

    Enhancing minority class recovery in high resolution land cover mapping with dynamic imbalance aware oversampling.

    Scientific reports·2026
    Same author

    Haploidentical allogeneic haematopoietic stem cell transplantation for paroxysmal nocturnal haemoglobinuria: a retrospective analysis.

    Annals of hematology·2026
    Same author

    Association of <i>katG</i>, <i>inhA,</i> and <i>AhpC</i> Mutations with Isoniazid Resistance of <i>Mycobacterium tuberculosis</i> in Pulmonary Tuberculosis Patients from Nanjing, China.

    Microbial drug resistance (Larchmont, N.Y.)·2026
    Same journal

    Predicting and Comparing the Performance of Array Management Libraries.

    Proceedings. IPDPS (Conference)·2021
    Same journal

    Architectural Implications for Spatial Object Association Algorithms.

    Proceedings. IPDPS (Conference)·2015
    Same journal

    Orientation Refinement of Virus Structures with Unknown Symmetry.

    Proceedings. IPDPS (Conference)·2015
    Same journal

    High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms.

    Proceedings. IPDPS (Conference)·2014
    Same journal

    Parallel Mapping Approaches for GNUMAP.

    Proceedings. IPDPS (Conference)·2013
    Same journal

    Translational Research Design Templates, Grid Computing, and HPC.

    Proceedings. IPDPS (Conference)·2011
    See all related articles

    This study introduces a performance-aware scheduling technique for efficient CPU-GPU collaboration in high performance computing. Co-scheduling these processors significantly boosts performance in large-scale image analysis tasks.

    Area of Science:

    • High Performance Computing
    • Parallel Processing
    • Computer Architecture

    Background:

    • The integration of Graphics Processing Units (GPUs) as general-purpose processors has created heterogeneous CPU-GPU systems in high performance computing.
    • Despite high theoretical performance, efficiently utilizing both CPU and GPU resources simultaneously in these hybrid systems presents a significant challenge.
    • Many applications currently underutilize either the CPU or GPU, failing to leverage the full parallel computing power available.

    Purpose of the Study:

    • To propose, implement, and evaluate a novel performance-aware scheduling technique.
    • To enable efficient collaborative utilization of both CPUs and GPUs on parallel systems.
    • To address the underutilization of computational resources in heterogeneous computing environments.
    Keywords:
    CPU-GPU systemsImage analysisIn SilicoMicroscopy

    Related Experiment Videos

    Last Updated: Apr 20, 2026

    Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
    07:19

    Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

    Published on: June 28, 2017

    10.8K

    Main Methods:

    • Development of a performance-aware scheduling algorithm.
    • Implementation of optimizations for collaborative CPU-GPU execution.
    • Evaluation of the technique in the context of feature computations for large-scale image analysis.

    Main Results:

    • The proposed technique facilitates intelligent co-scheduling of CPUs and GPUs.
    • Evaluations demonstrate significant performance improvements compared to CPU-only or GPU-only approaches.
    • Efficient collaborative use of heterogeneous resources was achieved.

    Conclusions:

    • Intelligent co-scheduling of CPUs and GPUs is crucial for maximizing performance in heterogeneous systems.
    • The developed scheduling technique effectively enhances performance in demanding applications like large-scale image analysis.
    • This approach offers a practical solution for leveraging the full potential of modern high performance computing platforms.