A Workload-Specific Memory Capacity Configuration Approach for In-Memory Data Analytic Platforms

We propose WSMC, a workload-specific memory capacity configuration approach for the Spark workloads, which guides users on the memory capacity configuration with the accurate prediction of the workload's memory requirement under various input data size and parameter settings.First, WSMC classifies the in-memory computing workloads into four categories according to the workloads' Data Expansion Ratio. Second, WSMC establishes a memory requirement prediction model with the consideration of the input data size, the shuffle data size, the parallelism of the workloads and the data block size. Finally, for each workload category, WSMC calculates the shuffle data size in the prediction model in a workload-specific way. For the ad-hoc workload, WSMC can profile its Data Expansion Ratio with small-sized input data and decide the category that the workload falls into. Users can then determine the accurate configuration in accordance with the corresponding memory requirement prediction.Through the comprehensive evaluations with SparkBench workloads, we found that, contrasting with the default configuration, configuration with the guide of WSMC can save over 40% memory capacity with the workload performance slight degradation (only 5%), and compared to the proper configuration found out manually, the configuration with the guide of WSMC leads to only 7% increase in the memory waste with the workload's performance slight improvement (about 1%

Dynamic calibration of current-steering DAC

The demand for high-speed communication systems has dramatically increased during the last decades. Working as an interface between the digital and analog world, Digital-to-Analog converters (DACs) are becoming more and more important because they are a key part which limits the accuracy and speed of an overall system. Consequently, the requirements for high-speed and high-accuracy DACs are increasingly demanding. It is well recognized that dynamic performance of the DACs degrades dramatically with increasing input signal frequencies and update rates. The dynamic performance is often characterized by the spurious free dynamic range (SFDR). The SFDR is determined by the spectral harmonics, which are attributable to system nonlinearities.;A new calibration approach is presented in this thesis that compensates for the dynamic errors in performance. In this approach, the nonlinear components of the input dependent and previous input code dependent errors are characterized, and correction codes that can be used to calibrate the DAC for these nonlinearities are stored in a two-dimensional error look-up table. A series of pulses is generated at run time by addressing the error look-up table with the most significant bits of the Boolean input and by using the corresponding output to drive a calibration DAC whose output is summed with the original DAC output. The approach is applied at both the behavioral level and the circuit level in current-steering DAC.;The validity of this approach is verified by simulation. These simulations show that the dynamic nonlinearities can be dramatically reduced with this calibration scheme. The simulation results also show that this calibration approach is robust to errors in both the width and height of calibration pulses.;Experimental measurement results are also provided for a special case of this dynamic calibration algorithm that show that the dynamic performance can be improved through dynamic calibration, provided the mean error values in the table are close to their real values

A Study of Cost Accounting Practices - Through Germany, Japan, and the United States

This thesis aims to describe two most prominent cost accounting systems originated in Germany, Japan, and the U.S. respectively from the background, theoretical and empirical aspects, thereafter through comparison amongst the examined costing systems of the three chosen countries, bring about discussions on cost accounting practices in general as well as the development of cost accounting practices in connection with the influence of national culture

A gang of thieves -- evolution of cooperative kleptoparasitism in the subfamily Argyrodinae (Araneae: Theridiidae)

This is the first comprehensive study of group-living behavior in kleptoparasitic Argyrodinae, and the first species level molecular phylogenetic analysis of the Argyrodinae (Araneae: Theridiidae). I included four research chapters in this dissertation. In Chapter 2, I showed the first empirical study of cooperative kleptoparasitism in Argyrodes miniaceus. The results showed that, at least at the level of foraging, group-living behavior has adaptive function of cooperation. Using a game theory model, the payoff of being cooperator in a group is greater than the payoff of being solitary. In Chapter 3, I concluded that kleptoparasites do not aggregate simply because the webs are large and can support multiple kleptoparasites. Social interactions among group members provide additional benefits that favor individuals remaining in groups. In Chapter 4, I concluded that group members could gain indirect benefit of fitness by cooperating with group members, who are potentially related individuals. This is because in group-living Argyrodes, group members are significantly more closely related than the individuals drawn randomly from the population in a small geographic scale. In Chapter 5, the phylogenetic analyses showed several independent origins of group-living behavior in different species groups. The evolutionary sequence of foraging strategies of Argyrodinae is from free-living to araneophagy, then to kleptoparasitism. The comparative analyses showed the specialization to large host is correlated with the evolution of group-living behavior. In addition, the processes of specialization thus becoming group-living may have caused diversification within species groups

Link Clustering with Extended Link Similarity and EQ Evaluation Division.

Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrogram to determine the cut level for best community detection. However, the original link clustering method does not consider the link similarity of non-neighbor links, and the partition density tends to divide the communities into many small communities. In this paper, an Extended Link Clustering method (ELC) for overlapping community detection is proposed. The improved method employs a new link similarity, Extended Link Similarity (ELS), to produce a denser transform matrix, and uses the maximum value of EQ (an extended measure of quality of modularity) as a means to optimally cut the dendrogram for better partitioning of the original network space. Since ELS uses more link information, the resulting transform matrix provides a superior basis for clustering and analysis. Further, using the EQ value to find the best level for the hierarchical clustering dendrogram division, we obtain communities that are more sensible and reasonable than the ones obtained by the partition density evaluation. Experimentation on five real-world networks and artificially-generated networks shows that the ELC method achieves higher EQ and In-group Proportion (IGP) values. Additionally, communities are more realistic than those generated by either of the original LC method or the classical CPM method