Optimizing I/O server placement for parallel I/O on switch-based irregular networks

In this paper, we study I/O server placement for optimizing parallel I/O performance on switch-based clusters, which typically adopt irregular network topologies to allow construction of scalable systems with incremental expansion capability. Finding optimal solution to this problem is computationally intractable. We quantified the number of messages travelling through each network link by a workload function, and developed three heuristic algorithms to find good solutions based on the values of the workload function. The maximum-workload-based heuristic chooses the locations for I/O nodes in order to minimize the maximum value of the workload function. The distance-based heuristic aims to minimize the average distance between the compute nodes and I/O nodes, which is equivalent to minimizing average workload on the network links. The load-balance-based heuristic balances the workload on the links based on a recursive traversal of the routing tree for the network. Our simulation results demonstrate performance advantage of our algorithms over a number of algorithms commonly used in existing parallel systems. In particular, the load-balance-based algorithm is superior to the other algorithms in most cases, with improvement ratio of 10 to 95% in terms of parallel I/O throughput.     

利用缓冲区提高Java应用程序的I／O性能

Java语言的跨平台特性使得其在各个领域得到了广泛应用。与此同时，Java应用程序的性能，特别是I／O性能，也是人们普遍关注的话题。本文采用一个生物信息学应用程序的实例来探讨如何利用I／O缓冲区提高Java应用程序的I／O性能。我们采用三种不同的I／O缓冲区的实现方式来实现该应用程序，对其进行性能分析比较并将其与相应的C＋＋应用程序进行比较。大量实验表明，I／O缓冲区对于Java应用程序的I／O性能起着至关重要的作用。恰当地使用I／O缓冲区，某些场合下能使Java应用程序的I／O性能略高高于相应的C＋＋应用程序的I／O性能。     

一种高性能I／O方法

并存文伴系统是解决I／O瓶颈问题的重要途径。研究表明，科学应用中跨越式的文件访问模式与现存并行文件系统访问这些数据的方法的结合，对于大型数据集的访问其I／O性能是难以接受的。为了提高并行文件系统中对不连续数据的I／O性能，创建了一种新型高性能I／O方法：用户自定义文件视图结合合并I／O请求。并且在WPFS并行文件系统中实现了该方法。研究和实验结果表明，该方法具有增强科学应用性能的潜力。     

Reducing I/O variability using dynamic I/O path characterization in petascale storage systems

In petascale systems with a million CPU cores, scalable and consistent I/O performance is becoming increasingly difficult to sustain mainly because of I/O variability. The I/O variability is caused by concurrently running processes/jobs competing for I/O or a RAID rebuild when a disk drive fails. We present a mechanism that stripes across a selected subset of I/O nodes with the lightest workload at runtime to achieve the highest I/O bandwidth available in the system. In this paper, we propose a probing mechanism to enable application-level dynamic file striping to mitigate I/O variability. We implement the proposed mechanism in the high-level I/O library that enables memory-to-file data layout transformation and allows transparent file partitioning using subfiling. Subfiling is a technique that partitions data into a set of files of smaller size and manages file access to them, making data to be treated as a single, normal file to users. We demonstrate that our bandwidth probing mechanism can successfully identify temporally slower I/O nodes without noticeable runtime overhead. Experimental results on NERSC’s systems also show that our approach isolates I/O variability effectively on shared systems and improves overall collective I/O performance with less variation.     

高性能计算中的并行I／O技术

1 引言高性能计算能力已经日益成为一个国家经济、科技与国防实力的重要组成部分。由于科学工程计算和大规模商业事务处理需求的牵引,高性能计算中对I/O处理能力的要求简直是无止境的。大规模多媒体应用要求大容量快速存储系统支持,多用户事务处理环境要求快速I/O支持实时访问,而一些重大挑战性科学计算课题更是追求计算机系统具有3T性能(即要求能提供 1 Teraflops计算能力、1 Terabyte主存容量和1 Terabyte/s I/O带宽)。 I/O一直是高性能计算中的瓶颈。典型地,磁盘比主存的速度要慢一万倍到十万倍。近年来,随着大规模集成电路技术和网络技术的飞速发展,CPU的性能大约每三年就有一个较大的飞跃,网络带宽增长更快,但I/O设备的性能受制于机     

影响VoD服务器I/O性能的关键因素

在讨论了基于网络视频点系统模型和通用服务器体系结构的基础上，主要分析了影响视频点播服务器性能I／O能力的几个关键因素，对存储设备的吞吐量，PCI总线速率，SCSI通道的速率和网卡的传输速度等对服务器I／O性能的影响进行了较全面，深入的分析，最后提高一种利用服务器集群的方式提高VoD系统I／O能力的方法。     

基于高性能I/O技术的Memcached优化研究

内存对象缓存系统在通信方面受制于传统以太网的高延迟,在存储方面受限于服务器内可部署的内存规模,亟需融合新一代高性能I/O技术来提升性能、扩展容量.以广泛应用的Memcached为例,聚焦内存对象缓存系统的数据通路并基于高性能I/O对其进行通信加速与存储扩展.首先,基于日益流行的高性能远程直接内存访问(remote direct memory access, RDMA)语义重新设计通信协议,并针对不同的Memcached操作及消息大小设计不同的策略,降低了通信延迟.其次,利用高性能NVMe SSD来扩展Memcached存储,采用日志结构管理内存与外存2级存储,并通过用户级驱动实现对SSD的直接访问,降低了软件开销.最终,实现了支持JVM环境的高性能缓存系统U2cache.U2cache通过旁路操作系统内核和JVM运行时与内存拷贝、RDMA通信、SSD访问交叠流水的方法,显著降低了数据访问开销.实验结果表明,U2cache通信延迟接近RDMA底层硬件性能;对大消息而言,相较无优化版本,性能提高超过20%;访问SSD中的数据时,相比通过内核I/O软件栈的方式,访问延迟最高降低了31%.     

Analysis of I/O Performance on an Amazon EC2 Cluster Compute and High I/O Platform

Cloud computing is currently being explored by the scientific community to assess its suitability for High Performance Computing (HPC) environments. In this novel paradigm, compute and storage resources, as well as applications, can be dynamically provisioned on a pay-per-use basis. This paper presents a thorough evaluation of the I/O storage subsystem using the Amazon EC2 Cluster Compute platform and the recent High I/O instance type, to determine its suitability for I/O-intensive applications. The evaluation has been carried out at different layers using representative benchmarks in order to evaluate the low-level cloud storage devices available in Amazon EC2, ephemeral disks and Elastic Block Store (EBS) volumes, both on local and distributed file systems. In addition, several I/O interfaces (POSIX, MPI-IO and HDF5) commonly used by scientific workloads have also been assessed. Furthermore, the scalability of a representative parallel I/O code has also been analyzed at the application level, taking into account both performance and cost metrics. The analysis of the experimental results has shown that available cloud storage devices can have different performance characteristics and usage constraints. Our comprehensive evaluation can help scientists to increase significantly (up to several times) the performance of I/O-intensive applications in Amazon EC2 cloud. An example of optimal configuration that can maximize I/O performance in this cloud is the use of a RAID 0 of 2 ephemeral disks, TCP with 9,000 bytes MTU, NFS async and MPI-IO on the High I/O instance type, which provides ephemeral disks backed by Solid State Drive (SSD) technology.     

UBIFS损耗均衡对系统I／O性能的影响

分析flash文件系统的损耗均衡问题,指出损耗均衡的触发条件对系统I／O性能的影响。在最新UBIFS文件系统上运行测试程序,结果证明在不同损耗均衡触发条件下,系统I／O性能的最大差距超过20％。针对现有损耗均衡算法的不足提出一种改进的自适应算法。     

附网存储体系中的I/O性能分析

附网存储是目前网络存储设备发展的新趋势。文章以附网存储体系结构的研究为背景，针对系统中设备的I／O性能进行了分析，根据建立排除论理论模型，定量在分析出了有关影响性能的主要参数，并导出了相关性能公式，通过对分析结果的计算检验，在数学理论的角度揭示了造成性能差异的原因。     

Xen虚拟机间的磁盘I/O性能隔离

针对当前的虚拟化技术无法使各个虚拟机平等地或按特定比例地共享磁盘带宽、无法保证虚拟机间的I/O性能隔离的问题,基于Xen半虚拟化技术中的块IO请求处理过程,提出一种适用于Xen虚拟机间的磁盘I/O性能隔离算法-XIOS(XenI/O Scheduler)算法,在通用块层调度各虚拟机的块IO操作(bio结构),在I/O调度层保障延迟需求.实验结果表明该算法有效地在虚拟机间按比例地分配磁盘带宽.     

并行PC系统结构及其I／O性能评价

传统的微／小型计算机采用共享总线结构，其带宽，扩展性，可用性，可靠性等受到限制，因此，总线形成了系统内部通信瓶颈，并行PC结构是一种新型的，以交换网络为中心的、高效的系统结构，它解决了传统PC机存在的不足和缺陷，本文对并行PC系统结构了介绍，然后采用M／M／1排队模型，针对传统PC和并行PC的I／O性能进行了评价与比较，最后给出结论及建议。     

一种自适应的基于预测的I／O性能优化方法

目前计算机的I／O性能已经成为严重制约计算机整体性能提升的瓶颈。在现有技术条件下，I／O性能的优化方法对于提升I／O性能显得尤为重要。本文提出了一种自适应的基于预测的I／O性能的优化方法，该方法采用了基于预测的数据预读策略，改进I／O读操作的响应速度；采用基于预测的缓存分配策略，改进了I／O写操作的响应速度，从而在整体上提高了计算机系统的I／O性能。从实验测试的结果可以看出，这种方法适用于多种不同的负载类型，能够大幅度提升计算机的I／O性能．是一种通用的、效果显著的I／O性能优化方法。     

高性能计算机I/O技术PCI Express分析

分析了PCI/PCI-X架构的I/O瓶颈,然后深入阐述第三代I/O技术PCI Express的特性及其体系结构,最后指出PCI Express作为InfiniBand^TM的重要补充必将在未来计算机I／O体系结构中扮演举足轻重的角色。     

Linux系统中网络I/O性能改进方法的研究

选择并设计高效的网络I/O模型是改善服务器性能的关键。该文通过对Linux系统中几种网络I/O模型的分析和研究,提出3种改善网络I/O性能的方法,并讨论这3种方法在Linux系统中的实现技术。实验结果验证了该方案的有效性。     

Xen的I/O虚拟化性能分析与优化

在Credit算法应用中,由I/O事务唤醒的VCPU处于最高优先级BOOST状态,优先抢占PCPU资源,使I/O操作的响应速度提高,但多个虚拟机同时进行I/O操作时,会引起较长延时和公平性原则被破坏问题.针对这个问题,研究分析SEDF算法、Credit算法、Credit2算法,提出L-Credit调度算法解决多个虚拟机同时进行I/O操作引起响应延迟的问题.通过监测I/O设备环共享页面中响应和请求的个数的方法,对处于BOOST状态下I/O操作进一步细化排序,使稀疏型I/O操作较密集型I/O操作先调用执行.通过对L-Credit算法与Credit算法在同一应用场景下反复对比实验,得出L-Credit算法可以提高I/O响应性能,并且继承了Credit算法负载均衡和按比例公平共享的特点.     

使用聚集I/O传输的方法提高网络存储系统的性能

网络存储技术为存储系统提供了更高的扩展性、可用性以及更好的灵活性,并且已经广泛应用到各种领域．但小I／O严重影响网络存储系统的性能,现有的一些方法缺少理论模型．通过排队理论建立聚集I／O传输的数学模型,然后在典型的IP网络存储(NAS)系统中,分别在NAS存储设备和客户两端设计了聚集模块,称之为CFS(chunk file system),实现双向聚集I／O．最后对CFS进行了实验,结果和理论分析基本吻合,说明聚集I／O传输能够改善网络存储整体性能．     

Quantitative characterization and analysis of the I/O behavior of acommercial distributed-shared-memory machine

This paper presents a unified evaluation of the I/O behavior of a commercial clustered DSM machine, the HP Exemplar. Our study has the following objectives: 1) To evaluate the impact of different interacting system components, namely, architecture, operating system, and programming model, on the overall I/O behavior and identify possible performance bottlenecks, and 2) To provide hints to the users for achieving high out-of-box I/O throughput. We find that for the DSM machines that are built as a cluster of SMP nodes, integrated clustering of computing and I/O resources, both hardware and software, is not advantageous for two reasons. First, within an SMP node, the I/O bandwidth is often restricted by the performance of the peripheral components and cannot match the memory bandwidth. Second, since the I/O resources are shared as a global resource, the file-access costs become nonuniform and the I/O behavior of the entire system, in terms of both scalability and balance, degrades. We observe that the buffered I/O performance is determined not only by the I/O subsystem, but also by the programming model, global-shared memory subsystem, and data-communication mechanism. Moreover, programming-model support can be used effectively to overcome the performance constraints created by the architecture and operating system. For example, on the HP Exemplar, users can achieve high I/O throughput by using features of the programming model that balance the sharing and locality of the user buffers and file systems. Finally, we believe that at present, the I/O subsystems are being designed in isolation, and there is a need for mending the traditional memory-oriented design approach to address this problem     

基于外存性能模型的多通道并行I／O

I／O部分一直是制约计算机系统整体性能提升的瓶颈。本文提出外存性能模型，用于定量分析外存的I／O性能并帮助克服I／O瓶颈，并在此基础上，提出用多通道I／O克服PCI总线瓶颈。采用多钱程控制和异步I／O技术，使所有通道的磁盘并行工作。对比实．验表明，最大顺序读性能提升了46％，顺序写提升48％，随机读提升4％，随机写提升57％。     

FAIRIO: A Throughput-oriented Algorithm for Differentiated I/O Performance

Providing differentiated service in a consolidated storage environment is a challenging task. To address this problem, we introduce FAIRIO, a cycle-based I/O scheduling algorithm that provides differentiated service to workloads concurrently accessing a consolidated RAID storage system. FAIRIO enforces proportional sharing of I/O service through fair scheduling of disk time. During each cycle of the algorithm, I/O requests are scheduled according to workload weights and disk-time utilization history. Experiments, which were driven by the I/O request streams of real and synthetic I/O benchmarks and run on a modified version of DiskSim, provide evidence of FAIRIO’s effectiveness and demonstrate that fair scheduling of disk time is key to achieving differentiated service in a RAID storage system. In particular, the experimental results show that, for a broad range of workload request types, sizes, and access characteristics, the algorithm provides differentiated storage throughput that is within 10% of being perfectly proportional to workload weights; and, it achieves this with little or no degradation of aggregate throughput. The core design concepts of FAIRIO, including service-time allocation and history-driven compensation, potentially can be used to design I/O scheduling algorithms that provide workloads with differentiated service in storage systems comprised of RAIDs, multiple RAIDs, SANs, and hypervisors for Clouds.