Reconfigurable Hardware and Software Architectural Constructs for the Enablement of Resilient Computing Systems

2006 ◽  
Author(s):  
Paul L. Master
Keyword(s):  
Reconfigurable Hardware ◽  
Computing Systems ◽  
Resilient Computing

Resilient Computing Systems

1986 ◽  
Vol 28 (2) ◽  
pp. 104 ◽  
Author(s):  
D Millington
Keyword(s):  
Computing Systems ◽  
Resilient Computing

scholarly journals Architecting Resilient Computing Systems: Overall Approach and Open Issues

2011 ◽  
pp. 48-62 ◽  
Author(s):  
Miruna Stoicescu ◽  
Jean-Charles Fabre ◽  
Matthieu Roy
Keyword(s):  
Computing Systems ◽  
Resilient Computing ◽  
Open Issues

scholarly journals Architecting resilient computing systems: A component-based approach for adaptive fault tolerance

2017 ◽  
Vol 73 ◽  
pp. 6-16 ◽  
Author(s):  
Miruna Stoicescu ◽  
Jean-Charles Fabre ◽  
Matthieu Roy
Keyword(s):  
Fault Tolerance ◽  
Computing Systems ◽  
Resilient Computing

Design and Development of Texture Filtering Architecture for GPU Application Using Reconfigurable Computing

2012 ◽  
Vol 1 (3) ◽  
pp. 108
Author(s):  
Sanket Suresh Naik Dessai
Keyword(s):  
Reconfigurable Computing ◽  
General Purpose ◽  
Reconfigurable Hardware ◽  
High Demand ◽  
Computing Systems ◽  
Graphical Processing Units ◽  
Quartus Ii ◽  
Filter Unit ◽  
Texture Filtering ◽  
Graphical Processing

Graphical Processing Units (GPUs) have become an integral part of today’s mainstream computing systems. They are also being used as reprogrammable General Purpose GPUs (GP-GPUs) to perform complex scientific computations. Reconfigurability is an attractive approach to embedded systems allowing hardware level modification.  Hence, there is a high demand for GPU designs based on reconfigurable hardware. The texture filter unit is designed to process geometric data like vertices and convert these into pixels on the screen. This process involves number of operations, like circle and cube generation, rotator, and scaling. The texture filter unit is designed with all necessary hardware to deal with all the different filtering operations. The designed texture filtering units are modelled in Verilog on Altera Quartus II and simulated using ModelSim tools. The functionality of the modelled blocks is verified using test inputs in the simulator.Circle and cube coordinates are generated for circle and cube generation. The work can form the basis for designing a complete reconfigurable GPU.

Artefact in 20Å-Resolution Electron Images of Negatively Stained Twodimensional Membrane Protein Crystals

1982 ◽  
Vol 40 ◽  
pp. 92-93
Author(s):  
Douglas L. Dorset ◽  
Barbara Moss
Keyword(s):  
Electron Scattering ◽  
Cross Correlation ◽  
Transmission Function ◽  
Group Symmetry ◽  
Asymmetric Structure ◽  
Object Model ◽  
Phase Object ◽  
Computing Systems ◽  
Resolution Electron ◽  
Plane Group

A number of computing systems devoted to the averaging of electron images of two-dimensional macromolecular crystalline arrays have facilitated the visualization of negatively-stained biological structures. Either by simulation of optical filtering techniques or, in more refined treatments, by cross-correlation averaging, an idealized representation of the repeating asymmetric structure unit is constructed, eliminating image distortions due to radiation damage, stain irregularities and, in the latter approach, imperfections and distortions in the unit cell repeat. In these analyses it is generally assumed that the electron scattering from the thin negativelystained object is well-approximated by a phase object model. Even when absorption effects are considered (i.e. “amplitude contrast“), the expansion of the transmission function, q(x,y)=exp (iσɸ (x,y)), does not exceed the first (kinematical) term. Furthermore, in reconstruction of electron images, kinematical phases are applied to diffraction amplitudes and obey the constraints of the plane group symmetry.

Cross-Layer Reliability of Computing Systems

2020 ◽  
Keyword(s):  
Cross Layer ◽  
Computing Systems

Recovery boiler sootblowers: History and technological advances

TAPPI Journal ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 51-60
Author(s):  
HONGHI TRAN ◽  
DANNY TANDRA
Keyword(s):  
Cfd Modeling ◽  
Computing Systems ◽  
The Past ◽  
Technological Advances ◽  
Recovery Boilers ◽  
Computational Fluid Dynamics Cfd ◽  
Recovery Boiler ◽  
Steam Parameters ◽  
Insight Into ◽  
Deposit Removal

Sootblowing technology used in recovery boilers originated from that used in coal-fired boilers. It started with manual cleaning with hand lancing and hand blowing, and evolved slowly into online sootblowing using retractable sootblowers. Since 1991, intensive research and development has focused on sootblowing jet fundamentals and deposit removal in recovery boilers. The results have provided much insight into sootblower jet hydrodynamics, how a sootblower jet interacts with tubes and deposits, and factors influencing its deposit removal efficiency, and have led to two important innovations: fully-expanded sootblower nozzles that are used in virtually all recovery boilers today, and the low pressure sootblowing technology that has been implemented in several new recovery boilers. The availability of powerful computing systems, superfast microprocessors and data acquisition systems, and versatile computational fluid dynamics (CFD) modeling capability in the past two decades has also contributed greatly to the advancement of sootblowing technology. High quality infrared inspection cameras have enabled mills to inspect the deposit buildup conditions in the boiler during operation, and helped identify problems with sootblower lance swinging and superheater platens and boiler bank tube vibrations. As the recovery boiler firing capacity and steam parameters have increased markedly in recent years, sootblowers have become larger and longer, and this can present a challenge in terms of both sootblower design and operation.

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