Stream Processing on Hybrid CPU/Intel® Xeon Phi™ Systems

2018 ◽  
pp. 796-810 ◽  
Author(s):  
Paulo Ferrão ◽  
Hélder Marques ◽  
Hervé Paulino
Keyword(s):  
Stream Processing ◽  
Xeon Phi

Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

2020 ◽  
Vol 140 (9) ◽  
pp. 1030-1039
Author(s):  
W.A. Shanaka P. Abeysiriwardhana ◽  
Janaka L. Wijekoon ◽  
Hiroaki Nishi
Keyword(s):  
Stream Processing ◽  
Community Services ◽  
Edge Computing ◽  
Computing Node ◽  
Smart Community

RFID data stream processing technology based on CEP

2009 ◽  
Vol 29 (10) ◽  
pp. 2786-2790 ◽  
Author(s):  
Xiao-jia YIN ◽  
Shi-guang JU ◽  
Ying-jie WANG
Keyword(s):  
Data Stream ◽  
Stream Processing ◽  
Processing Technology ◽  
Data Stream Processing ◽  
Rfid Data

Video-rate, spatially variant filtering technique using stream processing architecture

1988 ◽  
Vol 24 (25) ◽  
pp. 1580
Author(s):  
D.J. Allerton ◽  
J.D. Evemy ◽  
E.J. Zaluska
Keyword(s):  
Stream Processing ◽  
Variant Filtering ◽  
Filtering Technique ◽  
Spatially Variant ◽  
Processing Architecture

Stream Processing Languages and Abstractions

2019 ◽  
pp. 1600-1607
Author(s):  
Martin Hirzel ◽  
Guillaume Baudart
Keyword(s):  
Stream Processing

scholarly journals MILC Code Performance on High End CPU and GPU Supercomputer Clusters

2018 ◽  
Vol 175 ◽  
pp. 02009
Author(s):  
Carleton DeTar ◽  
Steven Gottlieb ◽  
Ruizi Li ◽  
Doug Toussaint
Keyword(s):  
Conjugate Gradient ◽  
Memory Hierarchy ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Code Performance ◽  
Recent Developments ◽  
Knights Landing ◽  
Many Core ◽  
Intel Xeon

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

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