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Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution （cDE） algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light （cDElight）, to address not only the memory saving necessities but also real-time requirements, cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.

The performance loss resulting from different cache misses is variable in modern systems for two reasons： 1） memory access latency is not uniform, and 2） the latency toleration ability of processor cores varies across different misses. Compared with parallel misses and store misses, isolated fetch and load misses are more costly. The variation of cache miss penalty suggests that the cache replacement policy should take it into account. To that end, first, we propose the notion of retention benefit. Retention benefits can evaluate not only the increment of processor stall cycles on cache misses, but also the reduction of processor stall cycles due to cache hits. Then, we propose Retention Benefit Based Replacement （RBR） which aims to maximize the aggregate retention benefits of blocks reserved in the cache. RBR keeps track of the total retention benefit for each block in the cache, and it preferentially evicts the block with the minimum total retention benefit on replacement. The evaluation shows that RBR can improve cache performance significantly in both single-core and multi-core environment while requiring a low storage overhead. It also outperforms other state-of-the-art techniques.

Virtualization has recently gained popularity largely due to its promise in increasing utilization, improving availability and enhancing security. Very often, the role of computer systems needs to change as the business environment changes. Initially, the system may only need to host one operating system and seek full execution speed. Later, it may be required to add other functionalities such as allowing easy software/hardware maintenance, surviving system failures and hosting multiple operating systems. Virtualization allows these functionalities to be supported easily and effectively. However, virtualization techniques generally incur non-negligible performance penalty. Fortunately, many virtualization- enabled features such as online software/hardware maintenance and fault tolerance do not require virtualization standby all the time. Based on this observation, this paper proposes a technique, called Self-virtualization, which provides the operating system with the capability to turn on and off virtualization on demand, without disturbing running applications. This technique enables computer systems to reap most benefits from virtualization without sacrificing performance. This paper presents the design and implementation of Mercury, a working prototype based on Linux and Xen virtual machine monitor. The performance measurement shows that Mercury incurs very little overhead： about 0.2 ms on 3 GHz Xeon CPU to complete a mode switch, and negligible performance degradation compared to Linux.