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As we enter the era of CMP platforms with multiple threads/cores on the die, the diversity of the simultaneous workloads running on them is expected to increase. The rapid deployment of virtualization as a means to consolidate workloads on to a single platform is a prime example of this trend. In such scenarios, the quality of service (QoS) that each individual workload gets from the platform can widely vary depending on the behavior of the simultaneously running workloads. While the number of cores assigned to each workload can be controlled, there is no hardware or software support in today's platforms to control allocation of platform resources such as cache space and memory bandwidth to individual workloads. In this paper, we propose a QoS-enabled memory architecture for CMP platforms that addresses this problem. The QoS-enabled memory architecture enables more cache resources (i.e. space) and memory resources (i.e. bandwidth) for high priority applications based on guidance from the operating environment. The architecture also allows dynamic resource reassignment during run-time to further optimize the performance of the high priority application with minimal degradation to low priority. To achieve these goals, we will describe the hardware/software support required in the platform as well as the operating environment (O/S and virtual machine monitor). Our evaluation framework consists of detailed platform simulation models and a QoS-enabled version of Linux. Based on evaluation experiments, we show the effectiveness of a QoSenabled architecture and summarize key findings/trade-offs.

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The Pt catalysts are the best potential hydrogen evolution reaction (HER) electrocatalysts for industrial applications, but development of efficient Pt catalysts with low Pt loading and high utilization efficiency remains...

Adolescents' romantic relationships have been associated with higher levels of depression, although their links with externalizing behavioral problems remain unclear. The present study examined the impact of adolescent romantic relationships on depression and externalizing behaviors in a large sample of 10,509 Chinese secondary school students (ages 12-19, 54.5% female). The results showed that romantic involvement in adolescence, especially in early adolescence, was associated with more depressive symptoms and behavior problems. Breakups in romantic relationships were an important factor in producing the negative emotional and behavioral consequences. Romantically involved girls experienced higher levels of depressive symptoms, while romantically involved boys had higher levels of externalizing behaviors, compared to their non-dating peers. The results also indicated that the adverse impact was stronger for those involved in romantic relationships at younger ages.

Rates of emotional and behavioral problems among children and adolescents in China are increasing and represent a major public health concern. To investigate the etiology of such problems, including the effects and interplay of genes and environment, the Beijing Twin Study (BeTwiSt) was established. A representative sample of adolescent twins in Beijing (N = 1,387 pairs of adolescent twins, mostly between the ages of 10 and 18 years) was recruited and assessed longitudinally. Data collection included the following: emotional and behavioral problems (e.g., depressive symptoms, anxiety, delinquency, drinking, and smoking); family, peer, and school environments; stress; social and academic competence; cognitive traits (e.g., emotion suppression, rumination, and effortful control); and saliva samples for DNA genotyping and sequencing. The combination of quantitative and molecular genetic approaches and the timeliness of the project, with the sample residing in a region with a rapidly changing economic and cultural climate, are particular strengths of this study. Findings from this study are expected to help understanding of the etiological mechanisms underlying child and adolescent normal and abnormal development in regions undergoing substantial social, cultural, and economic changes.

Cloud computing represents a paradigm shift driven by the increasing demand of Web based applications for elastic, scalable and efficient system architectures that can efficiently support their ever-growing data volume and large-scale data analysis. A typical data management system has to deal with real-time updates by individual users, and as well as periodical large scale analytical processing, indexing, and data extraction. While such operations may take place in the same domain, the design and development of the systems have somehow evolved independently for transactional and periodical analytical processing. Such a system-level separation has resulted in problems such as data freshness as well as serious data storage redundancy. Ideally, it would be more efficient to apply ad-hoc analytical processing on the same data directly. However, to the best of our knowledge, such an approach has not been adopted in real implementation.Intrigued by such an observation, we have designed and implemented epiC, an elastic power-aware data-itensive Cloud platform for supporting both data intensive analytical operations (ref. as OLAP) and online transactions (ref. as OLTP). In this paper, we present ES 2 -the elastic data storage system of epiC, which is designed to support both functionalities within the same storage. We present the system architecture and the functions of each system component, and experimental results which demonstrate the efficiency of the system.

One of the essential features in modern computer systems is context switching, which allows multiple threads of execution to time-share a limited number of processors. While very useful, context switching can introduce high performance overheads, with one of the primary reasons being the cache perturbation effect. Between the time a thread is switched out and when it resumes execution, parts of its working set in the cache may be perturbed by other interfering threads, leading to (context switch) cache misses to recover from the perturbation.The goal of this paper is to understand how cache parameters and application behavior influence the number of context switch misses the application suffers from. We characterize a previously-unreported type of context switch misses that occur as the artifact of the interaction of cache replacement policy and an application's temporal reuse behavior. We characterize the behavior of these "reordered misses" for various applications, cache sizes, and the amount of cache perturbation. As a second contribution, we develop an analytical model that reveals the mathematical relationship between cache design parameters, an application's temporal reuse pattern, and the number of context switch misses the application suffers from. We validate the model against simulation studies and find that it is accurate in predicting the trends of context switch misses. The mathematical relationship provided by the model allows us to derive insights into precisely why some applications are more vulnerable to context switch misses than others. Through a case study, we also find that prefetching tends to aggravate the number of context switch misses.