Robust Redundancy Scheme for the Repair Process: Hierarchical Codes in the Bandwidth-Limited Systems

High performance computing can be well supported by the Grid or cloud computing systems. However, these systems have to overcome the failure risks, where data is stored in the “unreliable” storage nodes that can leave the system at any moment and the nodes’ network bandwidth is limited. In this case, the basic way to assure data reliability is to add redundancy using either replication or erasure codes. As compared to replication, erasure codes are more space efficient. Erasure codes break data into blocks, encode these blocks and distribute them into different storage nodes. When storage nodes permanently or temporarily abandon the system, new redundant blocks must be created to guarantee the data reliability, which is referred to as repair. Later when the churn nodes rejoin the system, the blocks stored in these nodes can reintegrate the data group to enhance the data reliability. For “classical” erasure codes, generating a new block requires to transmit a number of k blocks over the network, which brings lots of repair traffic, high computation complexity and high failure probability for the repair process. Then a near-optimal erasure code named Hierarchical Codes, has been proposed that can significantly reduce the repair traffic by reducing the number of nodes participating in the repair process, which is referred to as the repair degree d. To overcome the complexity of reintegration and provide an adaptive reliability for Hierarchical Codes, we refine two concepts called location and relocation, and then propose an integrated maintenance scheme for the repair process. Our experiments show that Hierarchical Code is the most robust redundancy scheme for the repair process as compared to other famous coding schemes.     

并行程序错误调试技术研究综述

随着多核设备的普及,主流软件系统的构建模式已经由单线程串行转为多线程的并行方式．然而,由于并行程序的不确定性,使得调试并行程序错误要比串行程序的错误困难很多．因此,如何高效地调试并行程序错误成为了亟待解决的问题．对并行错误调试技术做了全面的研究与分析．在此基础上,进一步讨论了不同调试技术的优劣,也对并行调试技术可能的发展方向进行展望．     

关于多核系统并行程序效率的编程因素及其研究

着重分析了多核架构系统中内存对齐技术与cache利用率等因素对并行程序性能的影响。用共享存储环境OpenMP分析了并行计算量与处理器核心数目之间的关系,通过用MPI编程实现的矩阵相乘的行划分和CANNON算法等实例分析,指出了只有综合考虑了多核系统的结构特征、系统软件、多核编程语言环境以及正确运用算法等,才能设计出高效且能耗又小的并行应用程序。     

共享主存多SIMD结构及编译技术研究

目前多媒体应用已经成为各种运算平台的主要应用类型。随着多媒体应用的多样性和复杂性,共享主存多SIMD结构逐渐成为主从多核结构中多媒体加速部件的首要选择。总结了目前共享主存多SIMD结构的特征,同时深入分析了共享主存多SIMD编译优化的主要问题以及相关编译技术。     

利用虚拟机动态迁移技术整合虚拟和模拟环境

系统虚拟化和模拟技术对当今计算机科学研究和相关产业有着重要的影响.整合虚拟和模拟环境,让运行在虚拟机中的操作系统获得更多重要的服务是一项具有挑战性和有意义的工作.由系统虚拟化提供的虚拟机动态迁移技术作进一步扩展后,可整合这两个计算环境.提出Roam,一个支持在虚拟和模拟环境之间进行虚拟机动态迁移的框架.开发的Roam原型系统实现了Linux虚拟机在Xen和纯Qemu环境之间的动态迁移.相关性能测试表明Roam是一个可行的虚拟机动态迁移方案,并且虚拟机的停机时间和整体迁移时间都在一个可接受的范围内.     

面向云计算的虚拟机动态迁移框架

根据云计算平台的特点,提出一种新型虚拟机动态迁移框架,并在Xen和KVM这2种典型的开源虚拟机监控器基础上,实现原型系统。测试结果表明,在不同类型计算资源的环境下,该动态迁移框架具有良好的性能,能够对动态迁移进行实时控制,从而满足服务等级协议的要求。     

Hiding message delivery latency using Direct-to-Cache-Transfer techniques in message passing environments

Communication overhead is the key obstacle to reaching hardware performance limits. The majority is associated with software overhead, a significant portion of which is attributed to message copying. To reduce this copying overhead, we have devised techniques that do not require to copy a received message in order for it to be bound to its final destination. Rather, a late-binding mechanism, which involves address translation and a dedicated cache, facilitates fast access to received messages by the consuming process/thread.We have introduced two policies namely Direct to Cache Transfer (DTCT) and lazy DTCT that determine whether a message after it is bound needs to be transferred into the data cache. We have studied the proposed methods in simulation and have shown their effectiveness in reducing access times to message payloads by the consuming process.     

二维SIMD结构的低功耗调度

针对二维SIMD结构,提出一种可以动态关闭空转部件且结合编译器、指令集和体系结构支持的低功耗调度算法,其中包括编译器优化二维SIMD指令,功耗指令发出部件开关信号,系统接收信号并执行。采用对不同功能单元分别调度的方式和部件局部化的方法。在模拟器上的实验结果表明该方法可以节省整个系统约15％的能量消耗。     

可扩展的自动并行化编译系统

介绍一种可扩展的自动并行化编译系统Agassiz,研究其架构设计及关键特性。该系统可以把串行程序转换为并行程序,并为编译优化技术的研究提供良好的平台,通过面向对象的设计和实现,能有效集成各种并行优化技术。实验结果表明,该系统具有良好的可扩展性。