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| 1 | Hybrid CPU management for adapting to the diversity of virtual machines 显示文摘 | Weng Chuliang Guo Minyi Yuan Luo | 2013 | IEEE Transactions on Computers2013，62，7： | 1 |
| 2 | An In-VM Measuring Framework for Increasing Virtual Machine Security in Clouds显示文摘 | Liu Qian Weng Chuliang Li Minglu | 2010 | IEEE Transactions on Security&Privacy2010，8，6： | 1 |
| 3 | A Mandatory Access Control Framework in Virtual Machine System with Respect to Multi-level Security I: Theory显示文摘At present,there are few security models which control the communication between virtual machines (VMs).Moreover,these models are not applicable to multi-level security (MLS).In order to implement mandatory access control (MAC) and MLS in virtual machine system,this paper designs Virt-BLP model,which is based on BLP model.For the distinction between virtual machine system and non-virtualized system,we build elements and security axioms of Virt-BLP model by modifying those of BLP.Moreover,comparing with BLP,the number of state transition rules of Virt-BLP is reduced accordingly and some rules can only be enforced by trusted subject.As a result,Virt-BLP model supports MAC and partial discretionary access control (DAC),well satisfying the requirement of MLS in virtual machine system.As space is limited,the implementation of our MAC framework will be shown in a continuation. | LIU Qian WANG Guanhai WENG Chuliang LUO Yuan LI Minglu | 2010 | China Communications2010，7，4： | 1 |
| 4 | Hybrid CPU Management for Adapting to the Diversity of Virtual Machines显示文摘 | Weng Chuliang Guo Minyi Yuan Luo | 2013 | IEEE Transactions on Computers2013，62，7： | 1 |
| 5 | D-Cubicle:boosting data transfer dynamically for large-scale analytical queries in single-GPU systems显示文摘In analytical queries,a number of important operators like JOIN and GROUP BY are suitable for parallelization,and GPU is an ideal accelerator considering its power of parallel computing.However,when data size increases to hundreds of gigabytes,one GPU card becomes insufficient due to the small capacity of global memory and the slow data transfer between host and device.A straightforward solution is to equip more GPUs linked with high-bandwidth connectors,but the cost will be highly increased.We utilize unified memory(UM)produced by NVIDIA CUDA(Compute Unified Device Architecture)to make it possible to accelerate large-scale queries on just one GPU,but we notice that the transfer performance between host and UM,which happens before kernel execution,is often significantly slower than the theoretical bandwidth.An important reason is that,in singleGPU environment,data processing systems usually invoke only one or a static number of threads for data copy,leading to an inefficient transfer which slows down the overall performance heavily.In this paper,we present D-Cubicle,a runtime module to accelerate data transfer between host-managed memory and unified memory.D-Cubicle boosts the actual transfer speed dynamically through a self-adaptive approach.In our experiments,taking data transfer into account,D-Cubicle processes 200 GB of data on a single GPU with 32 GB of global memory,achieving 1.43x averagely and 2.09x maximally the performance of the baseline system. | Jialun WANG Wenhao PANG Chuliang WENG Aoying ZHOU | 2023 | Frontiers of Computer Science2023，17，4： | 0 |
| 6 | Dynamic depth-width optimization for capsule graph convolutional network显示文摘1 Introduction Encouraged by the success of Convolutional Neural Networks(CNNs),many studies[1],known as Graph Convolutional Networks(GCNs),borrowed the idea of convolution and redefined it for graph data.In graph-level classification tasks,Classic GCN methods[2,3]generate graph embeddings based on the learned node embeddings which consider each node’s representation as multiple independent scalar features.However,they neglect the detailed mutual relations among different node features such as position,direction,and connection.Inspired by CapsNet[4]which encodes each feature of an image as a vector(a capsule),CapsGNN[5]extracts multi-scale node features from different convolutional layers in the form of capsules.However,CapsGNN uses a static model structure to conduct training,which inherently restricts its representation ability on different datasets. | Shangwei WU Yingtong XIONG Chuliang WENG | 2023 | Frontiers of Computer Science2023，17，6： | 0 |
| 7 | A scheduling strategy for finite element analysis on the computational grid显示文摘The computational grid provides a promising platform for the deployment of various high-performance computing applications. A grid system consists of heterogeneous resource domains, while the computational tasks of finite element analysis may differ in demand of computing power. The cost-effective utilization of resources in the grid can be obtained through scheduling tasks to optimal resource domains. Firstly, a cost-effective scheduling strategy is presented for finite element applications. Secondly, aiming at the conjugate gradient solver stemming from finite element analysis, a performance evaluation formula is presented for determining optimal resource domains, which is derived from phase parallel model and takes the heterogeneous characteristic of resource domains into account. Finally, experimental results show that the presented formula delivers a good estimation of the actual execution time, and indicate that the presented formula can be used to determine optimal resource domains in the grid environment. | Weng Chuliang(翁楚良) Lu Xinda Yue Ying | 2005 | High Technology Letters2005，11，3： | 0 |
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