The garnet-type Li7La3Zr2O12 (LLZO) ceramic solid electrolyte combines high Li-ion conductivity at room temperature with high chemical stability. Several all-solid-state Li batteries featuring the LLZO electrolyte and the LiCoO2 (LCO) or LiCoO2–LLZO composite cathode were demonstrated. However, all batteries exhibit rapid capacity fading during cycling, which is often attributed to the formation of cracks due to volume expansion and the contraction of LCO. Excluding the possibility of mechanical failure due to crack formation between the LiCoO2/LLZO interface, a detailed investigation of the LiCoO2/LLZO interface before and after cycling clearly demonstrated cation diffusion between LiCoO2 and the LLZO. This electrochemically driven cation diffusion during cycling causes the formation of an amorphous secondary phase interlayer with high impedance, leading to the observed capacity fading. Furthermore, thermodynamic analysis using density functional theory confirms the possibility of low- or non-conducting secondary phases forming during cycling and offers an additional explanation for the observed capacity fading. Understanding the presented degradation paves the way to increase the cycling stability of garnet-based all-solid-state Li batteries.
Performance tuning for data centers is essential and complicated. It is important since a data center comprises thousands of machines and thus a single-digit performance improvement can significantly reduce cost and power consumption. Unfortunately, it is extremely difficult as data centers are dynamic environments where applications are frequently released and servers are continually upgraded.In this paper, we study the effectiveness of different processor prefetch configurations, which can greatly influence the performance of memory system and the overall data center. We observe a wide performance gap when comparing the worst and best configurations, from 1.4% to 75.1%, for 11 important data center applications. We then develop a tuning framework which attempts to predict the optimal configuration based on hardware performance counters. The framework achieves performance within 1% of the best performance of any single configuration for the same set of applications.
Simulation is a common approach for assisting system design and optimization. For system-wide optimization, energy and computational resources are often the two most critical issues. Monitoring the energy state of each hardware component and measuring the time spent in each state is needed for accurate energy and performance prediction. For software optimization, it is important to profile the energy and the time consumed by each software construct in a realistic operating environment with a proper workload. However, the conventional approaches of simulation often fail to produce satisfying data. First, building a cycle-accurate simulation environment for a complex system, such as an Android smartphone, is difficult and can take a long time. Second, a slow simulation can significantly alter the behavior of multithreaded, I/O-intensive applications and can affect the accuracy of profiles. Third, existing software-based profilers generally do not work on simulators, which makes it difficult for performance analysis of complicated software, for example, Java applications executed by the Dalvik VM in an Android system. To address these aforementioned problems, we proposed and prototyped a framework, called virtual performance analyzer (VPA). VPA takes advantage of an existing emulator or virtual machine monitor to reduce the complexity of building a simulator. VPA allows the user to selectively and incrementally integrate timing models and power models into the emulator with our carefully designed performance/power monitors, tracing facility, and profiling tools to evaluate and analyze the emulated system. The emulated system can perform at different levels of speed to help verify if the profile data are impacted by the emulation speed. Finally, VPA supports existing software-based profiles and enables non-intrusive tracing/profiling by minimizing the probe effect. Our experimental results show that the VPA framework allows users to quickly establish a performance/power evaluation environment and gather useful information to support system design and software optimization for Android smartphones.
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