Smaller transistor sizes and reduction in voltage levels in modern microprocessors induce higher soft error rates. This trend makes reliability a primary design constraint for computer systems. Redundant multithreading (RMT) makes use of parallelism in modern systems by employing thread-level time redundancy for fault detection and recovery. RMT can detect faults by running identical copies of the program as separate threads in parallel execution units with identical inputs and comparing their outputs. In this article, we present a survey of RMT implementations at different architectural levels with several design considerations. We explain the implementations in seminal papers and their extensions and discuss the design choices employed by the techniques. We review both hardware and software approaches by presenting the main characteristics and analyze the studies with different design choices regarding their strengths and weaknesses. We also present a classification to help potential users find a suitable method for their requirement and to guide researchers planning to work on this area by providing insights into the future trend.
In systems with complex many-core cache hierarchy, exploiting data locality can significantly reduce execution time and energy consumption of parallel applications. Locality can be exploited at various hardware and software layers. For instance, by implementing private and shared caches in a multi-level fashion, recent hardware designs are already optimised for locality. However, this would all be useless if the software scheduling does not cast the execution in a manner that promotes locality available in the programs themselves. Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures. This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS). The LeTS heuristic is a work-conserving algorithm that takes into account both locality and load balancing in order to reduce the execution time of target applications. The working principle of LeTS is based on two distinctive phases, namely; working task group formation phase (WTG-FP) and working task group ordering phase (WTG-OP). The WTG-FP forms groups of tasks in order to capture data reuse across tasks while the WTG-OP determines an optimal order of execution for task groups that minimizes the reuse distance of shared data between tasks. We have performed experiments using randomly generated task graphs by varying three major performance
Unmanned Aerial Vehicles (UAVs) are used for many missions, including weather reconnaissance, search and rescue assisting operations over seas and mountains, aerial photographing and mapping, fire detection, and traffic control. Autonomous operation of UAVs requires the development of control systems that can work without human support for long time periods. The path planners, which generate collision-free and optimized paths, are needed to provide autonomous operation capabilities to the UAVs. The optimization of the flight trajectory is a multi-objective problem dealing with variable terrain features as well as dynamic environment conditions. This paper presents a simulation environment for offline path planning of unmanned aerial vehicles on three-dimensional terrains. Our path planner aims to identify the shortest path and/or flight envelope in a given line of sight by avoiding terrain collisions, traveling on a path that stays within the restricted minimum and maximum distances above the terrain, traveling far from the specified threat zones, and maneuvering with an angle greater than the minimum curvature radius. We present two meta-heuristics (genetic algorithms and hyper-heuristics) in order to construct the paths for UAV navigation and compare our results with a reference work given in the literature. A comparative study over a set of terrains with various characteristics validates the effectiveness of the proposed meta-heuristics, where the quality of a solution is measured with the total cost of a constructed path, including the penalties of all constraints.
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