P. Rech, L. Carro
A higher Degree of Parallelism decreases the code execution time. However, to manage the increased number of parallel processes a higher scheduling strain is required and caches, registers, and other resources utilization will be affected. All these parallelism management variations may have the countermeasure of increasing the GPU neutron sensitivity. The results of an extensive […]
View View   Download Download (PDF)   
W. Nedel, F. Kastensmidt, J.R. Azambuja
Graphic Processing Units have become popular in a broad range of applications due to their high computational power and low prices. Among the applications are the safety critical ones, where fault tolerance is mandatory. This paper presents the implementation of a CUDA core, the main processing core of a GPU and its evaluation under Single […]
View View   Download Download (PDF)   
Stefano Di Carlo, Giulio Gambardella, Marco Indaco, Ippazio Martella, Paolo Prinetto, Daniele Rolfo, Pascal Trotta
Nowadays, Graphical Processing Units (GPUs) have become increasingly popular due to their high computational power and low prices. This makes them particularly suitable for high-performance computing applications, like data elaboration and financial computation. In these fields, high efficient test methodologies are mandatory. One of the most effective ways to detect and localize hardware faults in […]
View View   Download Download (PDF)   
Min Li
With the advances of very large scale integration (VLSI) technology, the feature size has been shrinking steadily together with the increase in the design complexity of logic circuits. As a result, the efforts taken for designing, testing, and debugging digital systems have increased tremendously. Although the electronic design automation (EDA) algorithms have been studied extensively […]
View View   Download Download (PDF)   
Artem Durytskyy, Mohamed Zahran, Ramesh Karri
With hundreds of processing units in current state-of-the-art graphics processing units (GPUs), the probability that one or more processing units fail due to permanent faults, during fabrication or post deployment, increases drastically. In our experiments we found that the loss of a single streaming multiprocessor (SM) in an 8-SM GPU resulted in as much as […]
View View   Download Download (PDF)   
Atif Hashmi, Hugues Berry, Olivier Temam, Mikko Lipasti
Recent advances in the neuroscientific understanding of the brain are bringing about a tantalizing opportunity for building synthetic machines that perform computation in ways that differ radically from traditional Von Neumann machines. These brain-like architectures, which are premised on our understanding of how the human neocortex computes, are highly fault-tolerant, averaging results over large numbers […]
View View   Download Download (PDF)   
K. Gulati, S.P. Khatri
In this paper, we explore the implementation of fault table generation on a Graphics Processing Unit (GPU). A fault table is essential for fault diagnosis and fault detection in VLSI testing and debug. Generating a fault table requires extensive fault simulation, with no fault dropping, and is extremely expensive from a computational standpoint. Fault simulation […]
View View   Download Download (PDF)   
Huawei Li, Dawen Xu, Yinhe Han, Kwang-Ting Cheng, Xiaowei Li
We present nGFSIM, a GPU-based fault simulator for stuck-at faults which can report the fault coverage of one-to n-detection for any specified integer n using only a single run of fault simulation. nGFSIM, which explores the massive parallelism in the GPU architecture and optimizes the memory access and usage, enables accelerated fault simulation without the […]
View View   Download Download (PDF)   
Min Li, Michael S. Hsiao
In this paper, we present an efficient diagnostic fault simulator based on a state-of-the-art graphics processing unit (GPU). Diagnostic fault simulation plays an important role to identify and locate the causes of circuit failures. However, today’s complex VLSI circuits pose ever higher computational demand for such simulators. Our GPU based diagnostic fault simulator (GDSim) is […]
Huawei Li, Dawen Xu, Kwang-Ting Cheng
GPUs have recently been explored as a new general-purpose computing platform, which are suitable for the acceleration of compute-intensive EDA applications. In this paper we describe a GPU-based one- to n-detection fault simulator for both stuck-at and transition faults, which demonstrates a 20X speedup over a commercial CPU-based fault simulator. We further show new fault-simulation-based […]
L.D. Solano-Quinde, B.M. Bode, A.K. Somani
Graphics Processing Units (GPUs) are increasingly used to solve non-graphical scientific problems. However, it has been shown that the reliability of the GPUs is a concern because of the occurrence of the soft and hard errors. The checkpoint/restart is the most commonly used technique to achieve fault tolerance in the presence of failures. This work […]
J.L. Autran, S. Uznanski, S. Martinie, P. Roche, G. Gasiot, D. Munteanu
This work reports the CUDA implementation of the collection-diffusion model to compute the soft-error rate (SER) of large area and/or complex circuits on graphics processing units (GPU). We detail the time parallelization introduced in the algorithm to accelerate by one order of magnitude the SER calculation. Code performances are evaluated on a NVIDIA Tesla C1060 […]
View View   Download Download (PDF)   
Page 1 of 212

* * *

* * *

Follow us on Twitter

HGPU group

1660 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: