Empirical Study on Online Political Participation of Young Migrant Workers

Popularization of network technology and development of online political participation expand approaches of young migrant workers participating in political affairs and raise interest and ability of young migrant workers participating in political affairs. Through questionnaire of young migrant workers participating in political affairs in Xi'an,Xianyang and Yangling,the survey team found that political participation of young migrant workers takes on following characteristics: active and positive online political concern,passive and profit seeking online political expression,and claim of right. Besides,online political participation of young migrant workers is related to region,cultural level,and occupation,but not related with their political status. Based on this survey,it came up with recommendations: governments at all levels should strengthen network information construction,carry out theoretical and practical training for online political participation of young migrant workers,establish online political participation government feedback mechanism and enhance party organization construction,and bring into play the lead model role of party members of young migrant workers in online political participation.

FlowDNN:a physics-informed deep neural network for fast and accurate flow prediction

For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze performance.While important,CFD simulations are a resource-demanding and time-consuming iterative process.The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design.In this paper,we propose Flow DNN,a novel deep neural network(DNN)to efficiently learn flow representations from CFD results.Flow DNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes.Flow DNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction.This approach not only improves the prediction accuracy,but also preserves the physical consistency of the predicted flow fields,which is essential for CFD.Various metrics are derived to evaluate Flow DNN with respect to the whole flow fields or regions of interest(RoIs)(e.g.,boundary layers where flow quantities change rapidly).Experiments show that Flow DNN significantly outperforms alternative methods with faster inference and more accurate results.It speeds up a graphics processing unit(GPU)accelerated CFD solver by more than 14000×,while keeping the prediction error under 5%.

More Bang for Your Buck:Boosting Performance with Capped Power Consumption

Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing(HPC)clusters,aiming to improve application performance without breaching peak power constraints and total energy consumption.Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance.It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so.We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge(HPCC)benchmark suites and evaluate it on a representative HPC cluster.Experimental results show that our approach can effectively mitigate memory contention to improve application performance,and it achieves this without significantly increasing the peak power and overall energy consumption.Our approach obtains on average 12.69%performance improvement over the default resource allocation strategy,but uses 7.06%less total power,which translates into 17.77%energy savings.

Programming bare-metal accelerators with heterogeneous threading models:a case study of Matrix-3000

As the hardware industry moves toward using specialized heterogeneous many-core processors to avoid the effects of the power wall,software developers are finding it hard to deal with the complexity of these systems.In this paper,we share our experience of developing a programming model and its supporting compiler and libraries for Matrix-3000,which is designed for next-generation exascale supercomputers but has a complex memory hierarchy and processor organization.To assist its software development,we have developed a software stack from scratch that includes a low-level programming interface and a high-level OpenCL compiler.Our low-level programming model offers native programming support for using the bare-metal accelerators of Matrix-3000,while the high-level model allows programmers to use the OpenCL programming standard.We detail our design choices and highlight the lessons learned from developing system software to enable the programming of bare-metal accelerators.Our programming models have been deployed in the production environment of an exascale prototype system.