ArticlePDPTA '19 : proceedings of the 2019 International Conference on Parallel & Distributed Processing Techniquess & Applications. International Conference on Parallel and Distributed Processing Techniques and Applications (2019 : Las Vegas,...
High-Performance Host-Device Scheduling and Data-Transfer Minimization Techniques for Visualization of 3D Agent-Based Wound Healing Applications.
Article in PDPTA '19 : proceedings of the 2019 International Conference on Parallel & Distributed Processing Techniquess & Applications. International Conference on Parallel and Distributed Processing Techniques and Applications (2019 : Las Vegas,.... The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma.Communications medicine · 2024Article
- Progress in Vocal Fold Regenerative Biomaterials: An Immunological Perspective.Advanced nanobiomed research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
High-fidelity numerical simulations produce massive amounts of data. Analyzing these numerical data sets as they are being generated provides useful insights into the processes underlying the modeled phenomenon. However, developing real-time in-situ visualization techniques to process large amounts of data can be challenging since the data does not fit on the GPU, thus requiring expensive CPU-GPU data copies. In this work, we present a scheduling scheme that achieve real-time simulation and interactivity through GPU hyper-tasking. Furthermore, the CPU-GPU communications were minimized using an activity-aware technique to reduce redundant copies. Our simulation platform is capable of visualizing 1.7 billion protein data points in situ, with an average frame rate of 42.8 fps. This performance allows users to explore large data sets on remote server with real-time interactivity as they are performing their simulations.
Indexed as
Identifiers
33123701PMC7592707What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.