Автор: Felipe Rocha da Rosa, Luciano Ost, Ricardo Reis
Издательство: Springer
Год: 2020
Страниц: 142
Язык: английский
Формат: pdf (true), epub
Размер: 38.8 MB
This book describes the benefits and drawbacks inherent in the use of virtual platforms (VPs) to perform fast and early soft error assessment of multicore systems. The authors show that VPs provide engineers with appropriate means to investigate new and more efficient fault injection and mitigation techniques. Coverage also includes the use of machine learning techniques (e.g., linear regression) to speed-up the soft error evaluation process by pinpointing parameters (e.g., architectural) with the most substantial impact on the software stack dependability. This book provides valuable information and insight through more than 3 million individual scenarios and 2 million simulation-hours. Further, this book explores machine learning techniques usage to navigate large fault injection datasets.
The increasing computing capacity of multicore components such as processors and GPUs offers new opportunities for embedded and high-performance computing (HPC) domains. The progressively growing computing capacity of multicore-based systems enables to efficiently perform complex application workloads at a lower power consumption compared to traditional single-core solutions. Such efficiency and the ever-increasing complexity of application workloads encourage the industry to integrate more and more computing components into the same system. The number of computing components employed in large-scale HPC systems already exceeds a million cores, while 1000-cores on-chip platforms are available in the embedded community.
Beyond the massive number of cores, the increasing computing capacity, as well as the number of internal memory cells (e.g., registers and internal memory) inherent to emerging processor architectures, is making large-scale systems more vulnerable to both hard and soft errors. Moreover, to meet emerging performance and power requirements, the underlying processors usually run in aggressive clock frequencies and multiple voltage domains, increasing their susceptibility to soft errors, such as the ones caused by radiation effects. The occurrence of soft errors or single event effects (SEEs) may cause critical failures in system behavior, which may lead to financial or human life losses.
Contents:
1. Introduction
2. Background on Soft Errors
3. Fault Injection Framework Using Virtual Platforms
4. Performance and Accuracy Assessment of Fault Injection Frameworks Based on VPs
5. Extensive Soft Error Evaluation
6. Machine Learning Applied to Soft Error Assessment in Multicore Systems
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