The bane of achieving a scalable distributed file sharing system is the centralized data system and single server oriented file [sharing] system. In this paper, the solution to the problems in a distributed environmen...The bane of achieving a scalable distributed file sharing system is the centralized data system and single server oriented file [sharing] system. In this paper, the solution to the problems in a distributed environment is presented. Thus, inability to upload sizeable files, slow transportation of files, weak security and lack of fault tolerance are the major problems in the existing system. Hence, the utmost need is to build a client-server network that runs on two or more server systems in order to implement scalability, such that when one server is down, the other(s) would still hold up the activities within the network. And to achieve this reliable network environment, LINUX network operating system is recommended among others as a preferred platform for the synchronization of the server systems, such that every user activity like sending of internal memos/mails, publication of academic articles, is replicated;thereby, achieving the proposed result. Hence, Scalable Distributed File Sharing System provides the robustness required to having a reliable intranet.展开更多
The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors,significantly improving the scalability of many applications.But the scalability is limited within a...The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors,significantly improving the scalability of many applications.But the scalability is limited within a single node;therefore programmers still have to redesign applications to scale out over multiple nodes.This paper revisits the design and implementation of distributed shared memory (DSM)as a way to scale out applications optimized for non-uniform memory access (NUMA)architecture over a well-connected cluster.This paper presents MAGI,an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network interfaces.MAGI is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory management.MAGI also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA)to reduce the number of page faults and the cost of the coherence protocol.MAGI has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized modifications.We deployed MAGI over an 8-node RDMA-enabled cluster.Experimental evaluation shows that MAGI achieves up to 9.25:4 speedup compared with an unoptimized implementation,leading to a sealable performance for large-scale data-intensive applications.展开更多
视点合成失真算法(Synthesized View Distortion Change,SVDC)作为三维高效视频编码(3D High Efficiency Video Coding,3D-HEVC)中改善深度图编码效率的有效途径,已成为当下三维视频领域的研究前沿之一。基于阵列处理器,利用分布式共享...视点合成失真算法(Synthesized View Distortion Change,SVDC)作为三维高效视频编码(3D High Efficiency Video Coding,3D-HEVC)中改善深度图编码效率的有效途径,已成为当下三维视频领域的研究前沿之一。基于阵列处理器,利用分布式共享存储结构设计并实现一种SVDC算法的并行映射方式,并根据访存特性提出失真值计算优化方案,以像素级误差平方和(Sum of Squared Differences,SSD)计算替代单元级SSD计算。实验表明,相比于HTM平台,算法的平均性能可以提升19.03%,所设计的失真值计算并行方案串/并加速比为2.36,使用像素级SSD计算后相比于优化前平均性能可以提升39.3%。展开更多
文摘The bane of achieving a scalable distributed file sharing system is the centralized data system and single server oriented file [sharing] system. In this paper, the solution to the problems in a distributed environment is presented. Thus, inability to upload sizeable files, slow transportation of files, weak security and lack of fault tolerance are the major problems in the existing system. Hence, the utmost need is to build a client-server network that runs on two or more server systems in order to implement scalability, such that when one server is down, the other(s) would still hold up the activities within the network. And to achieve this reliable network environment, LINUX network operating system is recommended among others as a preferred platform for the synchronization of the server systems, such that every user activity like sending of internal memos/mails, publication of academic articles, is replicated;thereby, achieving the proposed result. Hence, Scalable Distributed File Sharing System provides the robustness required to having a reliable intranet.
基金the National Key Research and Development Program of China under Grant No.2016YFBI000500the National Natural Science Foundation of China under Grant No.61572314the National Youth Top-Notch Talent Support Program of China.
文摘The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors,significantly improving the scalability of many applications.But the scalability is limited within a single node;therefore programmers still have to redesign applications to scale out over multiple nodes.This paper revisits the design and implementation of distributed shared memory (DSM)as a way to scale out applications optimized for non-uniform memory access (NUMA)architecture over a well-connected cluster.This paper presents MAGI,an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network interfaces.MAGI is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory management.MAGI also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA)to reduce the number of page faults and the cost of the coherence protocol.MAGI has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized modifications.We deployed MAGI over an 8-node RDMA-enabled cluster.Experimental evaluation shows that MAGI achieves up to 9.25:4 speedup compared with an unoptimized implementation,leading to a sealable performance for large-scale data-intensive applications.
文摘视点合成失真算法(Synthesized View Distortion Change,SVDC)作为三维高效视频编码(3D High Efficiency Video Coding,3D-HEVC)中改善深度图编码效率的有效途径,已成为当下三维视频领域的研究前沿之一。基于阵列处理器,利用分布式共享存储结构设计并实现一种SVDC算法的并行映射方式,并根据访存特性提出失真值计算优化方案,以像素级误差平方和(Sum of Squared Differences,SSD)计算替代单元级SSD计算。实验表明,相比于HTM平台,算法的平均性能可以提升19.03%,所设计的失真值计算并行方案串/并加速比为2.36,使用像素级SSD计算后相比于优化前平均性能可以提升39.3%。
