Titlebook: Architecture of Computing Systems – ARCS 2020; 33rd International C André Brinkmann,Wolfgang Karl,Carsten Trinitis Conference proceedings 2

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书目名称Architecture of Computing Systems – ARCS 2020影响因子(影响力)




书目名称Architecture of Computing Systems – ARCS 2020影响因子(影响力)学科排名




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书目名称Architecture of Computing Systems – ARCS 2020网络公开度学科排名




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书目名称Architecture of Computing Systems – ARCS 2020被引频次学科排名




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书目名称Architecture of Computing Systems – ARCS 2020年度引用学科排名




书目名称Architecture of Computing Systems – ARCS 2020读者反馈




书目名称Architecture of Computing Systems – ARCS 2020读者反馈学科排名

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Evaluating Dynamic Task Scheduling with Priorities and Adaptive Aging in a Task-Based Runtime System capability of individual applications. In such scenarios, considering the differing importance of applications while scheduling tasks is done by assigning priorities to the tasks. However, priorities may lead to starvation in highly utilized systems. A solution is offered by aging mechanisms that r

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He..ro DB: A Concept for Parallel Data Processing on Heterogeneous Hardwarehardware platforms composed of specialized computing resources. These are highly efficient for certain workloads but difficult to handle from the software engineering perspective. Even state-of-the-art database management systems do not exploit . heterogeneous hardware components, as their character

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Investigating Transactional Memory for High Performance Embedded Systemsegies, which can be applied according to the workload. Additionally, the TMU enables unbounded transactions in terms of size. Our approach tackles two challenges of traditional HTMs: (1) potentially high abort rates, (2) missing support for unbounded transactions. By enhancing a simulator with a tra

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X-CEL: A Method to Estimate Near-Memory Acceleration Potential in Tile-Based MPSoCs. However, it is not easy to determine whether the advantages arise from the near-memory integration or the hardware acceleration (versus software execution). We propose ., a method to accurately estimate the potential of near-memory acceleration using an easy-to-integrate .. We showcase . benefits

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Engineering an Optimized Instruction Set Architecture for AMIDAR Processorsadvantages. In the AMIDAR project Java Bytecode was used as the instruction set. Instructions are realized as compositions of . that are distributed to specialized functional units. An explicit timing of these micro instructions is not necessary in AMIDAR processors. This simplifies the conversion o