Filter Cache:一种提高Lustre I/O性能的方法

高性能计算系统需要一个可靠高效的并行文件系统.Lustre集群文件系统是典型的基于对象存储的集群文件系统,它适合大数据量聚合I/O操作.大文件I/O操作能够达到很高的带宽,但是小文件I/O性能低下.针对导致Lustre的设计中不利于小文件I/O操作的两个方面,提出了Filter Cache方法.在Lustre的OST组件中设计一个存放小文件I/O数据的Cache,让OST端的小文件I/O操作异步进行,以此来减少用户感知的小文件I/O操作完成的时间,提高小文件I/O操作的性能.     

集群下Cholesky分解的核外预取算法

核外计算中,由于I/O操作速度比较慢,所以对文件的访阿时间占的比例较大.如果使文件操作和计算重叠则可以大幅度地提高运行效率.软件数据预取是一种有效的隐藏存储延迟的技术,通过预取使数据在实际使用之前从硬盘读到缓存中,提高了缓存(cache)的命中率,降低了读取数据的时间.通过设置两个缓冲区来轮流存放本次和下一次读入的数据块,实现访存完全命中cache的效果,使Cholesky分解并行程序执行核外计算的效率得到了大幅度的提高.同时,I/O操作的时间与CPU的执行时间的比例也是影响效率的主要因素.     

基于内存与文件共享机制的Spark I/O性能优化

通过对Spark采用的弹性分布式数据集及任务调度等关键技术进行分析,发现数据处理I/O时间是影响Spark计算性能的主要瓶颈。为此,研究Spark合并文件运行模式,该模式能够减少缓存文件数量,提高Spark的I/O效率,但存在内存开销较高的缺点。在此基础上,给出改进的Spark Shuffle过程,即通过设计一种使每个Mapper只生成一个缓存文件的运行模式,并且每个Mapper共享同一个内存缓冲区,从而提高I/O效率和减少内存开销。仿真结果表明,与Spark默认模式相比,该运行模式宽依赖计算过程的I/O时间缩短42.9%,可有效提高内存利用率和Spark平台运算效率。     

一种改进的水平分布式环境下基于同态加密的隐私保护算法

提出了一种改进的水平分布式环境下关联规则挖掘的隐私保护算法,该算法应用部分隐藏的随机化回答方法和同态加密技术,引入半可信第三方,将各站点的数据集进行扰乱和隐藏,并将数据的水平格式表示转换成垂直格式表示,通过位运算计算局部支持数,利用Paillier算法计算全局支持数。所提算法具有站点之间无须通信、支持数计算效率高、I/O操作次数少以及传输安全等优点。实验结果表明,所提算法提高了局部支持数的计算效率并减少了I/O操作次数。     

面向新型非易失存储器的文件级磨损均衡机制

自旋转移力矩磁存储器（spin transfer torque random access memory ,ST TRAM ）和磁阻式随机存储器（magnetic random access memory ,MRAM ）等新型存储器具有接近于 DRAM 的访问速度,是构建高性能外存系统和提高计算机系统性能的重要手段,但有限的写次数是其重要局限之一．设计了文件系统级磨损均衡机制,使用 Hash 函数分散文件在外存中的存储,避免在创建和删除文件时反复分配某些存储块,通过分配文件空间时选择写次数较低的存储块,避免写操作的集中;使用主动迁移策略,在外存系统 I/O 负载较低时主动迁移写次数较高的数据块,减少磨损均衡机制对 I/O 性能的影响．最后在开源的基于对象存储设备 Open‐osd 上实现了面向新型存储器文件系统级磨损均衡机制的原型,使用存储系统通用测试工具 filebench 和 postmark 的多个通用数据集进行了测试与分析,验证了基于新型存储器的文件系统级磨损均衡机制能稳定地将存储块写次数差减少到原来的1/20左右,同时最高仅损失了6％的 I/O 性能和增加了0．5％的额外写操作,具有高效和稳定的特性．     

增量式隐私保护数据挖掘研究*

描述隐私保护数据挖掘技术研究进展,对主流算法进行了分类。由于隐私保护关联规则挖掘算法EMASK对Apriori算法具有较高的依赖性,计算效率较低,同时也不适用于动态变化数据库。本文用粒度计算的思想对EMASK算法进行改进,利用位操作的方法来保证准确性不降低的情况下,减少了I/O操作的次数,降低空间开销,同时在生成频繁项集的时候,也记录了其在扭曲后数据中的支持度,减少了文件的访问次数,由此提高计算效率。针对现实世界事务数据库变化情况,利用增量式更新算法FUP技术解决增量式事务数据库频繁项集计算问题。结果证明,无论是固定增量集数据库还是可变增量集数据库处理中,BIEMASK相对于EMASK而言,效率时间都有较大幅度的提高。     

一种XML数据流小枝模式查询算法

针对传统XML文档小枝模式查询算法系统开销大的问题,提出一种XML数据流小枝模式查询算法。该算法结合SAX数据流解析技术,将层次关系队列结构应用于XML文档查询中,采用动态生成区间编码的方式,免除建立编码索引文件的步骤。实验结果表明,在对相关数据集进行查询时,该算法可减少I/O操作,缩短查询响应时间,提高查询效率。     

基于透明计算模式的I/O Server的设计

设计并实现了一个基于透明计算模式的I/O Server系统，I/O Server和I/O Client是一个在透明计算环境下，支持多操作系统远程启动和运行的网络存储访问服务I/O Manager的2个软件模块，I/O Server工作在服务器端，I/O Client工作在客户端。在透明计算模式中，各客户机硬件与操作系统分离，用户需要的操作系统的应用程序存储在服务器端。在客户机启动时，I/O Server和启动协议将I/O Client下载到端系统上运行，然后I/O Client向I/O Server发出I/O请求，I/O Server对收到的I/O请求加以分析，进行优先级分类，在优先级分时轮转调度I/O请求、操作服务器上的虚拟硬盘文件，并通过预取和缓存策略减少磁盘I/O操作，将处理结果返回给客户端，支持操作系统的远程启动，并为系统运行时的各种请求提供服务。     

基于MapReduce的PageRank算法优化研究

为了提高PageRank算法的计算效率, 提出了基于块结构划分的方法, 将网页之间的链接关系转换成网络块间的关系, 减少了map和reduce操作的调用次数, 降低了I/O传输造成的开销, 提高计算的效率。实验证明, 该方法具有一定的优越性。     

并行科学计算应用中采样数据的聚集I/O

采样数据的并行I/O制约一些并行应用的运行效率。设计、实现了采样数据的聚集并行I/O方法。该方法在客户端部署采样数据缓存,然后合并数据到输出进程,再存储到文件。为了保障并行程序长时间运行过程中采样数据的存储一致性,该方法在JASMIN框架中监测应用程序的运行状态,当并行程序发生负载平衡或者重启动时刷新或者恢复数据。I/O过程中,进一步使用HDF5的分块I/O提高列存储数据的读写效率。测试表明,新方法不仅具有较好的可扩展性,还能在具有负载平衡与重启动等复杂功能的并行应用中提高采样数据的并行 I/O 效率7.5倍以上。     

Block I/O Scheduling on Storage Servers of Distributed File Systems

This paper presents a new scheme of I/O scheduling on storage servers of distributed/parallel file systems, for yielding better I/O performance. To this end, we first analyze read/write requests in the I/O queue of storage server (we name them block I/Os), by using our proposed technique of horizontal partition. Then, all block requests are supposed to be divided into multiple groups, on the basis of their offsets. This is to say, all requests related to the same chunk file will be grouped together, and then be satisfied within the same time slot between opening and closing the target chunk file on the storage server. As a result, the time resulted by completing block I/O requests can be significantly decreased, because of less file operations on the corresponding chunk files at the low-level file systems of server machines. Furthermore, we introduce an algorithm to rate a priority for each group of block I/O requests, and then the storage server dispatches groups of I/Os by following the priority order. Consequently, the applications having higher I/O priorities, e.g. they have less I/O operations and small size of involved data, can finish at a earlier time. We implement a prototype of this server-side scheduling in the PARTE file system, to demonstrate the feasibility and applicability of the proposed scheme. Experimental results show that the newly proposed scheme can achieve better I/O bandwidth and less I/O time, compared with the strategy of First Come First Served, as well as other server-side I/O scheduling approaches.     

一种改进的基于缓存池机制的小文件I/O策略

Lustre文件系统对大文件的I/O性能较好,但对小文件不佳。针对这个问题,提出建立一个基于MDS节点的小文件缓存池机制,在缓存池里缓存经常被访问的小文件。在该机制中,小文件缓存池与OST使用全相联映射方式对应,并且使用贯穿读出式和直写式策略保持文件的一致性;缓存池更新策略综合考虑了文件的访问时间和次数等因素,使用改进的近期最少使用算法（LRU）更新替换。实验结果表明,改进后的Lustre文件系统减少了小文件的网络传输开销和访问时间,对小文件的I/O性能有较明显的提高。虽然它对大文件的I/O性能有所降低,但在可接受范围之内,仍具有一定的实用价值。     

一种服务端驱动的无锁的文件元数据操作方法

当前的大规模存储系统提供大量的聚合I/O带宽,但并没有实现高度的元数据扩展性以管理分布于成千上万存储节点之上的文件。本文提出服务端驱动的无锁元数据操作来改进文件元数据操作的扩展性。服务端驱动技术简化了一致性维护,无锁技术既避免了资源冲突,又增进了元数据操作的并行性。实现了文件创建、删除操作。实验结果表明,该方法能显著提高系统的性能和扩展性。     

Inverted file partitioning schemes in multiple disk systems

Multiple-disk I/O systems (disk arrays) have been an attractive approach to meet high performance I/O demands in data intensive applications such as information retrieval systems. When we partition and distribute files across multiple disks to exploit the potential for I/O parallelism, a balanced I/O workload distribution becomes important for good performance. Naturally, the performance of a parallel information retrieval system using an inverted file structure is affected by the partitioning scheme of the inverted file. In this paper, we propose two different partitioning schemes for an inverted file system for a shared-everything multiprocessor machine with multiple disks. We study the performance of these schemes by simulation under a number of workloads where the term frequencies in the documents are varied, the term frequencies in the queries are varied, the number of disks are varied and the multiprogramming level is varied     

非定常Monte Carlo输运问题的并行算法

文中给出了非定常MonteCarlo(下文简写为MC)输运问题的并行算法 ,对并行程序的加载运行模式进行了讨论和优化设计 .针对MC并行计算设计了一种理想情况下无通信的并行随机数发生器算法 .动态MC输运问题有大量的I/O操作 ,特别是读取剩余粒子数据文件需要大量的I/O时间 ,文中针对I/O问题 ,提出了三种并行I/O算法 .最后给出了并行算法的性能测试结果 ,对比串行计算时间 ,使用 6 4台处理机时的并行计算时间缩短了 30倍     

Scalable Design and Implementations for MPI Parallel Overlapping I/O

We investigate the message passing interface input/output (MPI I/O) implementation issues for two overlapping access patterns: the overlaps among processes within a single I/O operation and the overlaps across a sequence of I/O operations. The former case considers whether I/O atomicity can be obtained in the overlapping regions. The latter focuses on the file consistency problem on parallel machines with client-side file caching enabled. Traditional solutions for both overlapping I/O problems use whole file or byte-range file locking to ensure exclusive access to the overlapping regions and bypass the file system cache. Unfortunately, not only can file locking serialize I/O, but it can also increase the aggregate communication overhead between clients and I/O servers. For atomicity, we first differentiate MPI's requirements from the portable operating system interface (POSIX) standard and propose two scalable approaches, graph coloring and process-rank ordering, which can resolve access conflicts and maintain I/O parallelism. For solving the file consistency problem across multiple I/O operations, we propose a method called persistent file domains, which tackles cache coherency with additional information and coordination to guarantee safe cache access without using file locks     

一种基于VirtFS的共享文件空间技术

由于虚拟环境多次I/O操作模拟的存在,运行在宿主服务器中的各虚拟客户机之间使用传统代理进行文件访问效率不高。为此,提出了基于VirtFS的共享文件空间技术。构建了虚拟客户机共享文件空间的系统架构,设计了虚拟磁盘操作映射机制,使用VirtFS框架提供的系统层接口,在宿主服务器中的VirtFS服务端截取和映射文件操作,直接将虚拟机文件系统的操作映射为对宿主服务器文件系统的操作。为了保障安全,彻底隔离用户域,实现分组共享。实验结果表明,采用该方法比现有代理方法文件读写效率分别提高9.7和5.7倍。     

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.     

Enabling dynamic file I/O path selection at runtime for parallel file system

Parallel file systems are experiencing more and more applications from various fields. Various applications have different I/O workload characteristics, which have diverse requirements on accessing storage resources. However, parallel file systems often adopt the “one-size-fits-all” solution, which fails to meet specific application needs and hinders the full exploitation of potential performance. This paper presents a framework to enable dynamic file I/O path selection with fine granularity at runtime. The framework adopts a file handle-rich scheme to allow file systems choose corresponding optimizations to serve I/O requests. Consistency control algorithms are proposed to ensure data consistency while changing optimizations at runtime. One case study on our prototype shows that choosing proper optimizations can improve the I/O performance for small files and large files by up to 40 and 64.4 %, respectively. Another case study shows that the data prefetch performance for real-world application traces can be improved by up to 193 % by selecting correct prefetch patterns. Simulations in large-scale environment also show that our method is scalable and both the memory consumption and the consistency control overhead can be negligible.