Advancing understanding of turbulence through extreme-scale computation: Intermittency and simulations at large problem sizes

2020 ◽  
Vol 5 (11) ◽  
Author(s):  
P. K. Yeung ◽  
K. Ravikumar
Keyword(s):  
Large Problem ◽  
Extreme Scale

Decomposition of tasks in a group of robots using cloud computing technologies

2017 ◽  
Vol 12 (1) ◽  
pp. 83-88
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Cloud Computing ◽  
Experimental Verification ◽  
Decomposition Algorithms ◽  
Complex Task ◽  
Large Problem ◽  
Working Capacity ◽  
Linear Distribution ◽  
Effective Teams ◽  
Computing Technologies

In this paper, various variants of decomposition of tasks in a group of robots using cloud computing technologies are considered. The specifics of the field of application (teams of robots) and solved problems are taken into account. In the process of decomposition, the solution of one large problem is divided into a solution of a series of smaller, simpler problems. Three ways of decomposition based on linear distribution, swarm interaction and synthesis of solutions are proposed. The results of experimental verification of the developed decomposition algorithms are presented, the working capacity of methods for planning trajectories in the cloud is shown. The resulting solution is a component of the complex task of building effective teams of robots.

Cardiovascular flow simulation at extreme scale

2009 ◽  
Vol 46 (1) ◽  
pp. 71-82 ◽  
Author(s):  
Min Zhou ◽  
Onkar Sahni ◽  
H. Jin Kim ◽  
C. Alberto Figueroa ◽  
Charles A. Taylor ◽  
...  
Keyword(s):  
Flow Simulation ◽  
Cardiovascular Flow ◽  
Extreme Scale

scholarly journals PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures

2016 ◽  
Author(s):  
Md. Mostofa Ali Patwary ◽  
Nadathur Rajagopalan Satish ◽  
Narayanan Sundaram ◽  
Jialin Liu ◽  
Peter Sadowski ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Distributed Architectures ◽  
Extreme Scale

NgCAM and VAMP2 Reveal that Direct Delivery and Dendritic Degradation Maintain Axonal Polarity

2021 ◽  
Author(s):  
Alec T. Nabb ◽  
Marvin Bentley
Keyword(s):  
Membrane Proteins ◽  
Molecular Mechanisms ◽  
Minor Role ◽  
Axonal Membrane ◽  
Quantitative Analyses ◽  
Direct Delivery ◽  
Novel Strategy ◽  
Extreme Scale ◽  
A Minor

Neurons are polarized cells of extreme scale and compartmentalization. To fulfill their role in electrochemical signaling, axons must maintain a specific complement of membrane proteins. Despite being subject of considerable attention, the trafficking pathway of axonal membrane proteins is not well understood. Two pathways, direct delivery and transcytosis, have been proposed. Previous studies reached contradictory conclusions about which of these mediates delivery of axonal membrane proteins to their destination, in part because they evaluated long-term distribution changes and not vesicle transport. We developed a novel strategy to selectively label vesicles in different trafficking pathways and determined the trafficking of two canonical axonal membrane proteins, NgCAM and VAMP2. Results from detailed quantitative analyses of transporting vesicles differed substantially from previous studies and found that axonal membrane proteins overwhelmingly undergo direct delivery. Transcytosis plays only a minor role in axonal delivery of these proteins. In addition, we identified a novel pathway by which wayward axonal proteins that reach the dendritic plasma membrane are targeted to lysosomes. These results redefine how axonal proteins achieve their polarized distribution, a crucial requirement for elucidating the underlying molecular mechanisms. [Media: see text] [Media: see text] [Media: see text] [Media: see text]

Design and analysis of a segmented blade for a 50 MW wind turbine rotor

2022 ◽  
pp. 0309524X2110693
Author(s):  
Alejandra S Escalera Mendoza ◽  
Shulong Yao ◽  
Mayank Chetan ◽  
Daniel Todd Griffith
Keyword(s):  
Wind Turbine ◽  
Large Mass ◽  
Turbine Rotor ◽  
Bolted Joints ◽  
Bonded Joints ◽  
Sheer Size ◽  
Extreme Scale ◽  
The Impact ◽  
Adhesive Materials ◽  
Wind Turbine Rotor

Extreme-size wind turbines face logistical challenges due to their sheer size. A solution, segmentation, is examined for an extreme-scale 50 MW wind turbine with 250 m blades using a systematic approach. Segmentation poses challenges regarding minimizing joint mass, transferring loads between segments and logistics. We investigate the feasibility of segmenting a 250 m blade by developing design methods and analyzing the impact of segmentation on the blade mass and blade frequencies. This investigation considers various variables such as joint types (bolted and bonded), adhesive materials, joint locations, number of joints and taper ratios (ply dropping). Segmentation increases blade mass by 4.1%–62% with bolted joints and by 0.4%–3.6% with bonded joints for taper ratios up to 1:10. Cases with large mass growth significantly reduce blade frequencies potentially challenging the control design. We show that segmentation of an extreme-scale blade is possible but mass reduction is necessary to improve its feasibility.

scholarly journals Application-based fault tolerance techniques for sparse matrix solvers

2017 ◽  
Vol 32 (5) ◽  
pp. 627-640
Author(s):  
Simon McIntosh–Smith ◽  
Rob Hunt ◽  
James Price ◽  
Alex Warwick Vesztrocy
Keyword(s):  
Fault Tolerance ◽  
High Performance Computing ◽  
High Performance ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Error Correcting Codes ◽  
Computing Systems ◽  
Hardware Costs ◽  
Extreme Scale ◽  
Performance Computing

High-performance computing systems continue to increase in size in the quest for ever higher performance. The resulting increased electronic component count, coupled with the decrease in feature sizes of the silicon manufacturing processes used to build these components, may result in future exascale systems being more susceptible to soft errors caused by cosmic radiation than in current high-performance computing systems. Through the use of techniques such as hardware-based error-correcting codes and checkpoint-restart, many of these faults can be mitigated at the cost of increased hardware overhead, run-time, and energy consumption that can be as much as 10–20%. Some predictions expect these overheads to continue to grow over time. For extreme scale systems, these overheads will represent megawatts of power consumption and millions of dollars of additional hardware costs, which could potentially be avoided with more sophisticated fault-tolerance techniques. In this paper we present new software-based fault tolerance techniques that can be applied to one of the most important classes of software in high-performance computing: iterative sparse matrix solvers. Our new techniques enables us to exploit knowledge of the structure of sparse matrices in such a way as to improve the performance, energy efficiency, and fault tolerance of the overall solution.

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