Parallel I/O system for a clustered computing environment

Master'sMASTER OF SCIENC

Parallel I/O scheduling in the presence of data duplication on multiprogrammed cluster computing systems

The widespread adoption of cluster computing as a high performance computing platform has seen the growth of data intensive scientific, engineering and commercial applications such as digital libraries, climate modeling, computational chemistry, computational fluid dynamics and image repositories. However, I/O subsystem performance has not been keeping pace with processor and memory performance, and is fast becoming the dominant factor in overall system performance.&nbsp; Thus, parallel I/O has become a necessity in the face of performance improvements in other areas of computing systems. This paper addresses the problem of parallel I/O scheduling on cluster computing systems in the presence of data replication.&nbsp; We propose two new I/O scheduling algorithms and evaluate the relative performance of the proposed policies against two existing approaches.&nbsp; Simulation results show that the proposed policies perform substantially better than the baseline policies.<br /

Load balancing techniques for I/O intensive tasks on heterogeneous clusters

Load balancing schemes in a cluster system play a critically important role in developing highperformance cluster computing platform. Existing load balancing approaches are concerned with the effective usage of CPU and memory resources. I/O-intensive tasks running on a heterogeneous cluster need a highly effective usage of global I/O resources, previous CPU-or memory-centric load balancing schemes suffer significant performance drop under I/O- intensive workload due to the imbalance of I/O load. To solve this problem, Zhang et al. developed two I/O-aware load-balancing schemes, which consider system heterogeneity and migrate more I/O-intensive tasks from a node with high I/O utilization to those with low I/O utilization. If the workload is memory-intensive in nature, the new method applies a memory-based load balancing policy to assign the tasks. Likewise, when the workload becomes CPU-intensive, their scheme leverages a CPU-based policy as an efficient means to balance the system load. In doing so, the proposed approach maintains the same level of performance as the existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation study show that, when a workload is I/O-intensive, the proposed schemes improve the performance with respect to mean slowdown over the existing schemes by up to a factor of 8. In addition, the slowdowns of almost all the policies increase consistently with the system heterogeneity

A shared-disk parallel cluster file system

Dissertação apresentada para obtenção do Grau de Doutor em Informática Pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaToday, clusters are the de facto cost effective platform both for high performance computing (HPC) as well as IT environments. HPC and IT are quite different environments and differences include, among others, their choices on file systems and storage: HPC favours parallel file systems geared towards maximum I/O bandwidth, but which are not fully POSIX-compliant and were devised to run on top of (fault prone) partitioned storage; conversely, IT data centres favour both external disk arrays (to provide highly available storage) and POSIX compliant file systems, (either general purpose or shared-disk cluster file systems, CFSs). These specialised file systems do perform very well in their target environments provided that applications do not require some lateral features, e.g., no file locking on parallel file systems, and no high performance writes over cluster-wide shared files on CFSs. In brief, we can say that none of the above approaches solves the problem of providing high levels of reliability and performance to both worlds. Our pCFS proposal makes a contribution to change this situation: the rationale is to take advantage on the best of both – the reliability of cluster file systems and the high performance of parallel file systems. We don’t claim to provide the absolute best of each, but we aim at full POSIX compliance, a rich feature set, and levels of reliability and performance good enough for broad usage – e.g., traditional as well as HPC applications, support of clustered DBMS engines that may run over regular files, and video streaming. pCFS’ main ideas include: · Cooperative caching, a technique that has been used in file systems for distributed disks but, as far as we know, was never used either in SAN based cluster file systems or in parallel file systems. As a result, pCFS may use all infrastructures (LAN and SAN) to move data. · Fine-grain locking, whereby processes running across distinct nodes may define nonoverlapping byte-range regions in a file (instead of the whole file) and access them in parallel, reading and writing over those regions at the infrastructure’s full speed (provided that no major metadata changes are required). A prototype was built on top of GFS (a Red Hat shared disk CFS): GFS’ kernel code was slightly modified, and two kernel modules and a user-level daemon were added. In the prototype, fine grain locking is fully implemented and a cluster-wide coherent cache is maintained through data (page fragments) movement over the LAN. Our benchmarks for non-overlapping writers over a single file shared among processes running on different nodes show that pCFS’ bandwidth is 2 times greater than NFS’ while being comparable to that of the Parallel Virtual File System (PVFS), both requiring about 10 times more CPU. And pCFS’ bandwidth also surpasses GFS’ (600 times for small record sizes, e.g., 4 KB, decreasing down to 2 times for large record sizes, e.g., 4 MB), at about the same CPU usage.Lusitania, Companhia de Seguros S.A, Programa IBM Shared University Research (SUR

ClusterRAID: Architecture and Prototype of a Distributed Fault-Tolerant Mass Storage System for Clusters

During the past few years clusters built from commodity off-the-shelf (COTS) components have emerged as the predominant supercomputer architecture. Typically comprising a collection of standard PCs or workstations and an interconnection network, they have replaced the traditionally used integrated systems due to their better price/performance ratio. As paradigms shift from mere computing intensive to I/O intensive applications, mass storage solutions for cluster installations become a more and more crucial aspect of these systems. The inherent unreliability of the underlying components is one of the reasons why no system has been established as a standard storage solution for clusters yet. This thesis sets out the architecture and prototype implementation of a novel distributed mass storage system for commodity off-the-shelf clusters and addresses the issue of the unreliable constituent components. The key concept of the presented system is the conversion of the local hard disk drive of a cluster node into a reliable device while preserving the block device interface. By the deployment of sophisticated erasure-correcting codes, the system allows the adjustment of the number of tolerable failures and thus the overall reliability. In addition, the applied data layout considers the access behaviour of a broad range of applications and minimizes the number of required network transactions. Extensive measurements and functionality tests of the prototype, both stand-alone and in conjunction with local or distributed file systems, show the validity of the concept

Uma arquitetura paralela para o armazenamento de imagens médicas em sistemas de arquivos distribuídos

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.Com a implantação da Rede Catarinense de Telemedicina tem-se verificado um aumento significativo no volume de imagens médicas, do padrão DICOM, geradas pelos dispositivos médicos interconectados nesta rede. Visando a manipulação dessas imagens médicas, foi desenvolvido em um projeto prévio, um servidor conhecido como CyclopsDCMServer, para a manipulação das imagens DICOM considerando a abordagem usando o Hierarchical Data Format (HDF5). Todavia, é esperado que a abordagem venha a encontrar gargalos devido ao crescimento no volume de dados e operações simultâneas que são submetidas ao servidor. Com o objetivo de dar continuidade ao esforço para prover uma melhor escalabilidade ao servidor CyclopsDCMServer, nesta dissertação apresenta-se uma pesquisa no sentido de potencializar a implementação de um paradigma paralelo no servidor para o armazenamento e recuperação das imagens DICOM. Desta forma, desenvolveu-se um módulo considerando bibliotecas E/S paralelas de alto desempenho. Este módulo efetua uma comunicação com o servidor que é responsável pela realização do acesso paralelo no formato de dados hierárquico. Visando a avaliação de desempenho da abordagem paralela, foram executados experimentos em diferentes sistemas de arquivos distribuídos. Os experimentos foram focados principalmente nas operações de armazenamento e recuperação das imagens médicas. Comparou-se o tempo médio de execução de cada operação em serial e paralelo. Foi coletado também o tempo de E/S em cada operação, para averiguar somente o desempenho do processo de escrita e leitura dos dados, descartando qualquer atraso que pudesse interferir nos resultados. Os resultados empíricos demonstraram que, independente do sistema de arquivos, a abordagem paralela ainda não apresenta uma eficiência considerável, quando comparada com a arquitetura serial. A média do declínio de desempenho pode ser considerada em torno de 45% na operação de recuperação e 71% na operação de armazenamento. Verificou-se também que o aumento do número de processos paralelos pode causar uma perda maior de desempenho nesta abordagem.With the deployment of Catarinense Network of Telemedicine has verified a meaningful increase in volume of medical images, DICOM standard, generated by medical devices interconnected on this network. In order to manipulate this medical images was develop in one previous project, a server known as CyclopsDCMServer, to manipulate DICOM images considering the approach Hierarchical Data Format (HDF5). However, it is expected that this approach will find bottlenecks due the spread of data size and simultaneously operations submitted to the server. With focus to continue the effort to supply better scalability to the server CyclopsDCMServer, this dissertation presents a research in the sense to empowerment the implementation of a parallel paradigm in the server to storage and retrieve DICOM images. Thus, it was developed a module considering high performance parallel I/O libraries. This module performs a communication with the server that is responsible for the creation of parallel access in hierarchical data format Aiming at the performance evaluation of the parallel approach, experiments were performed in different distributed file systems. The experiments were mainly focused on the operations of storage and retrieval of medical images. It was compared the average execution time of each operation in serial and parallel. It was also collected the I/O time in each operation, only to ascertain the performance of the process of writing and reading data, discarding any delay that could meddle the results. The empirical results show that, regardless of file system, the parallel approach does not present a considerable eficiency when compared to the serial architecture. The average decline in performance can be seen at around 45 % in the recovery operation and 71 % in the storage operation. It was also observed that increasing the number of parallel processes can cause a larger loss of performance in this approach

An Apache Hadoop Framework for Large-Scale Peptide Identification

Peptide identification is an essential step in protein identification, and Peptide Spectrum Match (PSM) data set is huge, which is a time consuming process to work on a single machine. In a typical run of the peptide identification method, PSMs are positioned by a cross correlation, a statistical score, or a likelihood that the match between the trial and hypothetical is correct and unique. This process takes a long time to execute, and there is a demand for an increase in performance to handle large peptide data sets. Development of distributed frameworks are needed to reduce the processing time, but this comes at the price of complexity in developing and executing them. In distributed computing, the program may divide into multiple parts to be executed. The work in this thesis describes the implementation of Apache Hadoop framework for large-scale peptide identification using C-Ranker. The Apache Hadoop data processing software is immersed in a complex environment composed of massive machine clusters, large data sets, and several processing jobs. The framework uses Apache Hadoop Distributed File System (HDFS) and Apache Mapreduce to store and process the peptide data respectively.The proposed framework uses a peptide processing algorithm named CRanker which takes peptide data as an input and identifies the correct PSMs. The framework has two steps: Execute the C-Ranker algorithm on Hadoop cluster and compare the correct PSMs data generated via Hadoop approach with the normal execution approach of C-Ranker. The goal of this framework is to process large peptide datasets using Apache Hadoop distributed approach

Accelerating Network Communication and I/O in Scientific High Performance Computing Environments

High performance computing has become one of the major drivers behind technology inventions and science discoveries. Originally driven through the increase of operating frequencies and technology scaling, a recent slowdown in this evolution has led to the development of multi-core architectures, which are supported by accelerator devices such as graphics processing units (GPUs). With the upcoming exascale era, the overall power consumption and the gap between compute capabilities and I/O bandwidth have become major challenges. Nowadays, the system performance is dominated by the time spent in communication and I/O, which highly depends on the capabilities of the network interface. In order to cope with the extreme concurrency and heterogeneity of future systems, the software ecosystem of the interconnect needs to be carefully tuned to excel in reliability, programmability, and usability. This work identifies and addresses three major gaps in today's interconnect software systems. The I/O gap describes the disparity in operating speeds between the computing capabilities and second storage tiers. The communication gap is introduced through the communication overhead needed to synchronize distributed large-scale applications and the mixed workload. The last gap is the so called concurrency gap, which is introduced through the extreme concurrency and the inflicted learning curve posed to scientific application developers to exploit the hardware capabilities. The first contribution is the introduction of the network-attached accelerator approach, which moves accelerators into a "stand-alone" cluster connected through the Extoll interconnect. The novel communication architecture enables the direct accelerators communication without any host interactions and an optimal application-to-compute-resources mapping. The effectiveness of this approach is evaluated for two classes of accelerators: Intel Xeon Phi coprocessors and NVIDIA GPUs. The next contribution comprises the design, implementation, and evaluation of the support of legacy codes and protocols over the Extoll interconnect technology. By providing TCP/IP protocol support over Extoll, it is shown that the performance benefits of the interconnect can be fully leveraged by a broader range of applications, including the seamless support of legacy codes. The third contribution is twofold. First, a comprehensive analysis of the Lustre networking protocol semantics and interfaces is presented. Afterwards, these insights are utilized to map the LNET protocol semantics onto the Extoll networking technology. The result is a fully functional Lustre network driver for Extoll. An initial performance evaluation demonstrates promising bandwidth and message rate results. The last contribution comprises the design, implementation, and evaluation of two easy-to-use load balancing frameworks, which transparently distribute the I/O workload across all available storage system components. The solutions maximize the parallelization and throughput of file I/O. The frameworks are evaluated on the Titan supercomputing systems for three I/O interfaces. For example for large-scale application runs, POSIX I/O and MPI-IO can be improved by up to 50% on a per job basis, while HDF5 shows performance improvements of up to 32%