scholarly journals Self-Restoration Mechanism for Run-Time Reconfigurable Data-Stream Processors

2021 ◽  
Author(s):  
Irina Terterian
Keyword(s):  
Reconfigurable Computing ◽  
High Performance ◽  
Processing System ◽  
Maximum Level ◽  
Computing Systems ◽  
Computing Platform ◽  
Restoration Mechanism ◽  
Run Time ◽  
Computing Platforms ◽  
The Cost

The cost of a hardward failure in high-performance computing systems is usually extremely high because of the system stall where billions of operations can be lost within one second. Thus, implementation of self-restoration mechanisms is one of the most effective approaches to keep system performance on a required level. The project presents a new approach, which allows retaining the performance of the Run-Time Reconfigurable stream processing system on its maximum level. This becomes possible by development of multi-level self-restoration mechanism that consists of: restoration by FPGA-scrubbing, restoration by FPGA-slot replacement and restoration with optimum performance degradation. All above levels of restoration procedure were developed and tested on reconfigurable computing platform based on XILINX Virtex FPGA. Analysis of achieved results of the developed mechanism shows a very fast restoration of functionality and dramatic increase of lifetime of FPGA based computing platforms.

scholarly journals Self-Restoration Mechanism for Run-Time Reconfigurable Data-Stream Processors

2021 ◽  
Author(s):  
Irina Terterian
Keyword(s):  
Reconfigurable Computing ◽  
High Performance ◽  
Processing System ◽  
Maximum Level ◽  
Computing Systems ◽  
Computing Platform ◽  
Restoration Mechanism ◽  
Run Time ◽  
Computing Platforms ◽  
The Cost

The cost of a hardward failure in high-performance computing systems is usually extremely high because of the system stall where billions of operations can be lost within one second. Thus, implementation of self-restoration mechanisms is one of the most effective approaches to keep system performance on a required level. The project presents a new approach, which allows retaining the performance of the Run-Time Reconfigurable stream processing system on its maximum level. This becomes possible by development of multi-level self-restoration mechanism that consists of: restoration by FPGA-scrubbing, restoration by FPGA-slot replacement and restoration with optimum performance degradation. All above levels of restoration procedure were developed and tested on reconfigurable computing platform based on XILINX Virtex FPGA. Analysis of achieved results of the developed mechanism shows a very fast restoration of functionality and dramatic increase of lifetime of FPGA based computing platforms.

scholarly journals A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 154 ◽  
Author(s):  
Mário P. Véstias
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Reconfigurable Computing ◽  
Data Privacy ◽  
High Performance ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Computing Platform ◽  
The Cost

The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher computing and memory requirements. Inference of convolutional neural networks is therefore usually done in centralized high-performance platforms. However, many applications based on CNNs are migrating to edge devices near the source of data due to the unreliability of a transmission channel in exchanging data with a central server, the uncertainty about channel latency not tolerated by many applications, security and data privacy, etc. While advantageous, deep learning on edge is quite challenging because edge devices are usually limited in terms of performance, cost, and energy. Reconfigurable computing is being considered for inference on edge due to its high performance and energy efficiency while keeping a high hardware flexibility that allows for the easy adaption of the target computing platform to the CNN model. In this paper, we described the features of the most common CNNs, the capabilities of reconfigurable computing for running CNNs, the state-of-the-art of reconfigurable computing implementations proposed to run CNN models, as well as the trends and challenges for future edge reconfigurable platforms.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

scholarly journals Run-Time and Compiler Support for Programming in Adaptive Parallel Environments

1997 ◽  
Vol 6 (2) ◽  
pp. 215-227 ◽  
Author(s):  
Guy Edjlali ◽  
Gagan Guyagrawal ◽  
Alan Sussman ◽  
Jim Humphries ◽  
Joel Saltz
Keyword(s):  
Parallel Programming ◽  
High Performance ◽  
Navier Stokes ◽  
Programming Environments ◽  
Data Parallel ◽  
Adaptive Environment ◽  
Run Time ◽  
Time Required ◽  
The Cost ◽  
Performance Results

For better utilization of computing resources, it is important to consider parallel programming environments in which the number of available processors varies at run-time. In this article, we discuss run-time support for data-parallel programming in such an adaptive environment. Executing programs in an adaptive environment requires redistributing data when the number of processors changes, and also requires determining new loop bounds and communication patterns for the new set of processors. We have developed a run-time library to provide this support. We discuss how the run-time library can be used by compilers of high-performance Fortran (HPF)-like languages to generate code for an adaptive environment. We present performance results for a Navier-Stokes solver and a multigrid template run on a network of workstations and an IBM SP-2. Our experiments show that if the number of processors is not varied frequently, the cost of data redistribution is not significant compared to the time required for the actual computation. Overall, our work establishes the feasibility of compiling HPF for a network of nondedicated workstations, which are likely to be an important resource for parallel programming in the future.

scholarly journals Contextual Contracts for Component-Based Resource Abstraction in a Cloud of HPC Services

2019 ◽  
Author(s):  
Wagner Al Alam ◽  
Francisco Carvalho Junior
Keyword(s):  
Cloud Computing ◽  
Parallel Computing ◽  
Large Scale ◽  
Matrix Multiplication ◽  
Small Scale ◽  
Computing Systems ◽  
Computing Platform ◽  
Computing Platforms

The efforts to make cloud computing suitable for the requirements of HPC applications have motivated us to design HPC Shelf, a cloud computing platform of services for building and deploying parallel computing systems for large-scale parallel processing. We introduce Alite, the system of contextual contracts of HPC Shelf, aimed at selecting component implementations according to requirements of applications, features of targeting parallel computing platforms (e.g. clusters), QoS (Quality-of-Service) properties and cost restrictions. It is evaluated through a small-scale case study employing a componentbased framework for matrix-multiplication based on the BLAS library.

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