Workload modeling for parallel computers

The availability of good workload models is essential for the design and analysis of parallel computer systems. A workload model can be applied directly in an experimental or simulation environment to verify new scheduling policies or strategies. Moreover, it can be used for extrapolating and predicting future workload conditions. In this work, we focus on the workload modeling for parallel computers. To this end, we start with an examination of the overall features of the available workloads. Here, we find a strong sequential dependency in the submission series of computational jobs. Next, a new approach using Markov chains is proposed that is capable of describing the temporal dependency. Second, we analyze the missing attributes in some workloads. Our results show that the missing information can be still recovered when the relevant model is trained from other complete data set. Based on the results of overall workload analysis, we begin to inspect the workload characteristics based on particular user-level features. That is, we analyze in detail how the individual users use parallel computers. In particular, we cluster the users into several manageable groups, while each of these groups has distinct features. These different groups provide a clear explanation for the global characteristics of workloads. Afterwards, we examine the user feedbacks and present a novel method to identify them. These evidences indicate that some users have an adaptive tendency and a complete workload model should not ignore the users' feedbacks. The work ends with a brief conclusion on the discussed modeling aspects and gives an outlook on future work

Characteristic gene expression in the liver monocyte-macrophage-DC system is associated with the progression of fibrosis in NASH

BackgroundThe monocyte-macrophage-dendritic cell (DC) (MMD) system exerts crucial functions that may modulate fibrogenesis in nonalcoholic steatohepatitis (NASH). In this study, we explored the cell characteristics, distribution and developmental trajectory of the liver MMD system in NASH mice with fibrosis and clarified characteristic genes of the MMD system involved in liver fibrosis progression in NASH mice and patients.MethodsSingle cells in liver tissue samples from NASH and normal mice were quantified using single-cell RNA sequencing (scRNA-seq) analysis. Differentially expressed genes (DEGs) in the MMD system by pseudotime analysis were validated by tyramide signal amplification (TSA)-immunohistochemical staining (IHC) and analyzed by second harmonic generation (SHG)/two-photon excitation fluorescence (TPEF).ResultsCompared with control mice, there were increased numbers of monocytes, Kupffer cells, and DCs in two NASH mouse models. From the transcriptional profiles of these single cells, we identified 8 monocyte subsets (Mono1-Mono8) with different molecular and functional properties. Furthermore, the pseudotime analysis showed that Mono5 and Mono6 were at the beginning of the trajectory path, whereas Mono2, Mono4, Kupffer cells and DCs were at a terminal state. Genes related to liver collagen production were at the late stage of this trajectory path. DEGs analysis revealed that the genes Fmnl1 and Myh9 in the MMD system were gradually upregulated during the trajectory. By TSA-IHC, the Fmnl1 and Myh9 expression levels were increased and associated with collagen production and fibrosis stage in NASH mice and patients.ConclusionsOur transcriptome data provide a novel landscape of the MMD system that is involved in advanced NASH disease status. Fmnl1 and Myh9 expression in the MMD system was associated with the progression of NASH fibrosis

Evaluate Operational Modal Analysis and Compare the Result to Visualized Mode Shapes

The prototypes vibration test carried out for obtaining reliable information concerning machine’s dynamic properties in development process. Analysis results should be able to correlate with FE model to determine if some underlying assumptions (material properties & boundary conditions) were correct. EMA used for extracting structure modal parameter under laboratory condition. However, EMA can generally not provide all required information concerning machine dynamic property. To simulate vibration in operating, it commonly requires the model based on dynamic properties of the machine under operating. Thus, vibration tests need carried out under operational condition. OMA is a useful tool for extracting information concerning dynamic properties of operating machine. This report concerns vibration test of part of mining machine under operating condition. Modal parameters extracted by two kinds of OMA methods. Results from OMA was compared with corresponding EMA results, illustrates reader the advantages of OMA

Workload Modeling for Parallel Computers

I want to thank all those who made this work possible by their supports. In particular, I want to express my appreciation to my doctorial advisor, Prof. Dr.-Ing. Uwe Schwiegelshohn, for the source of instructive guidance and supports over the past years. I am grateful to all people at Robotic Research Institute at the University of Dortmund for the excellent working atmosphere. Especially, I would also like to thank Dr.-Ing. Ramin Yahyapour, whose efforts and expertise help me to finish my study. I would like to express my gratitude to Dipl.-Inform. Carsten Ernemann, who give me many helpful suggestions and supports. I would also like to thank the Graduate School of Production Engineering and Logistics at the University of Dortmund, which provides me not only the financial supports but also the broad contacts with other departments. I would like to thank many of my chinese friends with them I had a pleasure time in Dortmund. At last, I would like to thank my parents, my wife and my son, who alway

Parallel Computer Workload Modeling with Markov Chains

In order to evaluate di#erent scheduling strategies for parallel computers, simulations are often executed. As the scheduling quality highly depends on the workload that is served on the parallel machine, a representative workload model is required. Common approaches such as using a probability distribution model can capture the static feature of real workloads, but they do not consider the temporal relation in the traces. In this paper, a workload model is presented which uses Markov chains for modeling job parameters. In order to consider the interdependence of individual parameters without requiring large scale Markov chains, a novel method for transforming the states in di#erent Markov chains is presented. The results show that the model yields closer results to the real workloads than other common approaches

User Group-based Workload Analysis and Modeling

Knowledge about the workload is an important aspect for scheduling of resources as parallel computers or Grid components. As the scheduling quality highly depends on the characteristics of the workload running on such resources, a representative workload model is significant for performance evaluation. Previous approaches on workload modelling mainly focused on methods that use statistical distributions to fit the overall workload characteristics. Therefore, the individual association and correlation to users or groups are usually lost. However, job scheduling for single parallel installations as well as for Grid systems started to focus more on the quality of service for specific user groups. Here, detailed knowledge of the individual user characteristic and preference is necessary for developing appropriate scheduling strategies. In the absence of a large information base of actual workloads, the adequate modelling of submission behaviors is sought. In this paper, we propose a new workload model, called MUGM (Mixed User Group Model), which maintains the characteristics of individual user groups. The MUGM method has been further evaluated by simulations and shown to yield good results.

A Novel Reliability Evaluation Method Based on RBD and AHP for Industrial Network Systems

Since each component has different impacts on reliability of the industrial network system, a multi-layer reliability evaluation method was proposed in this paper. Firstly, in order to construct multi-layer reliability evaluation system, a framework of industrial network system was introduced based on analytic hierarchy process (AHP). Secondly, a multi-layer reliability evaluation model with weight coefficient of components was proposed based on reliability block diagram (RBD). Thirdly, the simple rule-based fuzzy judgment and risk priority number (RPN) were applied to determining weight coefficient. Last, a reliability evaluation case of the industrial network system for an electronic automatic assembly line was studied. It shows that the proposed method is more reasonable than the conventional reliability analysis method, and the reliability prediction result is consistent with the engineering practice

A Novel Reliability Evaluation Method Based on RBD and AHP for Industrial Network Systems

Since each component has different impacts on reliability of the industrial network system, a multi-layer reliability evaluation method was proposed in this paper. Firstly, in order to construct multi-layer reliability evaluation system, a framework of industrial network system was introduced based on analytic hierarchy process (AHP). Secondly, a multi-layer reliability evaluation model with weight coefficient of components was proposed based on reliability block diagram (RBD). Thirdly, the simple rule-based fuzzy judgment and risk priority number (RPN) were applied to determining weight coefficient. Last, a reliability evaluation case of the industrial network system for an electronic automatic assembly line was studied. It shows that the proposed method is more reasonable than the conventional reliability analysis method, and the reliability prediction result is consistent with the engineering practice