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RIS citation export for FRBL05: RemoteVis: An Efficient Library for Remote Visualization of Large Volumes Using NVIDIA Index

AU  - Spina, T.V.
AU  - Alnajjar, D.
AU  - Bernardi, M.L.
AU  - Furusato, F.S.
AU  - Kuhn, A.
AU  - Miqueles, E.X.
AU  - Nienhaus, M.
AU  - Peixinho, A.Z.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - RemoteVis: An Efficient Library for Remote Visualization of Large Volumes Using NVIDIA Index
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - Advancements in X-ray detector technology are increasing the amount of volumetric data available for material analysis in synchrotron light sources. Such developments are driving the creation of novel solutions to visualize large datasets both during and after image acquisition. Towards this end, we have devised a library called RemoteVis to visualize large volumes remotely in HPC nodes, using NVIDIA IndeX as the rendering backend. RemoteVis relies on RDMA-based data transfer to move large volumes from local HPC servers, possibly connected to X-ray detectors, to remote dedicated nodes containing multiple GPUs for distributed volume rendering. RemoteVis then injects the transferred data into IndeX for rendering. IndeX is a scalable software capable of using multiple nodes and GPUs to render large volumes in full resolution. As such, we have coupled RemoteVis with slurm to dynamically schedule one or multiple HPC nodes to render any given dataset. RemoteVis was written in C/C++ and Python, providing an efficient API that requires only two functions to 1) start remote IndeX instances and 2) render regular volumes and point-cloud (diffraction) data on the web browser/Jupyter client.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 1047
EP  - 1053
KW  - software
KW  - synchrotron
KW  - detector
KW  - network
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-FRBL05
UR  - https://jacow.org/icalepcs2021/papers/frbl05.pdf
ER  -