High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

GPU Computing to Improve Game Engine Performance

Although the graphics processing unit (GPU) was originally designed to accelerate the image creation for output to display, today's general purpose GPU (GPGPU) computing offers unprecedented performance by offloading computing-intensive portions of the application to the GPGPU, while running the remainder of the code on the central processing unit (CPU). The highly parallel structure of a many core GPGPU can process large blocks of data faster using multithreaded concurrent processing. A game engine has many "components" and multithreading can be used to implement their parallelism. However, effective implementation of multithreading in a multicore processor has challenges, such as data and task parallelism. In this paper, we investigate the impact of using a GPGPU with a CPU to design high-performance game engines. First, we implement a separable convolution filter (heavily used in image processing) with the GPGPU. Then, we implement a multiobject interactive game console in an eight-core workstation using a multithreaded asynchronous model (MAM), a multithreaded synchronous model (MSM), and an MSM with data parallelism (MSMDP). According to the experimental results, speedup of about 61x and 5x is achieved due to GPGPU and MSMDP implementation, respectively. Therefore, GPGPU-assisted parallel computing has the potential to improve multithreaded game engine performance

Improving the User Experience of the rCUDA Remote GPU Virtualization Framework

Graphics processing units (GPUs) are being increasingly embraced by the high-performance computing community as an effective way to reduce execution time by accelerating parts of their applications. remote CUDA (rCUDA) was recently introduced as a software solution to address the high acquisition costs and energy consumption of GPUs that constrain further adoption of this technology. Specifically, rCUDA is a middleware that allows a reduced number of GPUs to be transparently shared among the nodes in a cluster. Although the initial prototype versions of rCUDA demonstrated its functionality, they also revealed concerns with respect to usability, performance, and support for new CUDA features. In response, in this paper, we present a new rCUDA version that (1) improves usability by including a new component that allows an automatic transformation of any CUDA source code so that it conforms to the needs of the rCUDA framework, (2) consistently features low overhead when using remote GPUs thanks to an improved new communication architecture, and (3) supports multithreaded applications and CUDA libraries. As a result, for any CUDA-compatible program, rCUDA now allows the use of remote GPUs within a cluster with low overhead, so that a single application running in one node can use all GPUs available across the cluster, thereby extending the single-node capability of CUDA. Copyright © 2014 John Wiley & Sons, Ltd.This work was funded by the Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. The author from Argonne National Laboratory was supported by the US Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357. The authors are also grateful for the generous support provided by Mellanox Technologies.Reaño González, C.; Silla Jiménez, F.; Castello Gimeno, A.; Peña Monferrer, AJ.; Mayo Gual, R.; Quintana Ortí, ES.; Duato Marín, JF. (2015). Improving the User Experience of the rCUDA Remote GPU Virtualization Framework. Concurrency and Computation: Practice and Experience. 27(14):3746-3770. https://doi.org/10.1002/cpe.3409S374637702714NVIDIA NVIDIA industry cases http://www.nvidia.es/object/tesla-case-studiesFigueiredo, R., Dinda, P. A., & Fortes, J. (2005). Guest Editors’ Introduction: Resource Virtualization Renaissance. Computer, 38(5), 28-31. doi:10.1109/mc.2005.159Duato J Igual FD Mayo R Peña AJ Quintana-Ortí ES Silla F An efficient implementation of GPU virtualization in high performance clusters Euro-Par 2009 Workshops, ser. LNCS, 6043 Delft, Netherlands, 385 394Duato J Peña AJ Silla F Mayo R Quintana-Ortí ES Performance of CUDA virtualized remote GPUs in high performance clusters International Conference on Parallel Processing, Taipei, Taiwan 2011 365 374Duato J Peña AJ Silla F Fernández JC Mayo R Quintana-Ortí ES Enabling CUDA acceleration within virtual machines using rCUDA International Conference on High Performance Computing, Bangalore, India 2011 1 10Shi, L., Chen, H., Sun, J., & Li, K. (2012). vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines. IEEE Transactions on Computers, 61(6), 804-816. doi:10.1109/tc.2011.112Gupta V Gavrilovska A Schwan K Kharche H Tolia N Talwar V Ranganathan P GViM: GPU-accelerated virtual machines 3rd Workshop on System-Level Virtualization for High Performance Computing, Nuremberg, Germany 2009 17 24Giunta G Montella R Agrillo G Coviello G A GPGPU transparent virtualization component for high performance computing clouds Euro-Par 2010 - Parallel Processing, 6271 Ischia, Italy, 379 391Zillians VGPU http://www.zillians.com/vgpuLiang TY Chang YW GridCuda: a grid-enabled CUDA programming toolkit Proceedings of the 25th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA), Biopolis, Singapore 2011 141 146Barak A Ben-Nun T Levy E Shiloh A Apackage for OpenCL based heterogeneous computing on clusters with many GPU devices Workshop on Parallel Programming and Applications on Accelerator Clusters, Heraklion, Crete, Greece 2010 1 7Xiao S Balaji P Zhu Q Thakur R Coghlan S Lin H Wen G Hong J Feng W-C VOCL: an optimized environment for transparent virtualization of graphics processing units Proceedings of InPar, San Jose, California, USA 2012 1 12Kim J Seo S Lee J Nah J Jo G Lee J SnuCL: an OpenCL framework for heterogeneous CPU/GPU clusters Proceedings of the 26th International Conference on Supercomputing, Venice, Italy 2012 341 352NVIDIA The NVIDIA CUDA Compiler Driver NVCC Version 5, NVIDIA 2012Quinlan D Panas T Liao C ROSE http://rosecompiler.org/Free Software Foundation, Inc. GCC, the GNU Compiler Collection http://gcc.gnu.org/LLVM Clang: a C language family frontend for LLVM http://clang.llvm.org/Martinez G Feng W Gardner M CU2CL: a CUDA-to-OpenCL Translator for Multi- and Many-core Architectures http://eprints.cs.vt.edu/archive/00001161/01/CU2CL.pdfLLVM The LLVM compiler infrastructure http://llvm.org/Reaño C Peña AJ Silla F Duato J Mayo R Quintana-Orti ES CU2rCU: towards the complete rCUDA remote GPU virtualization and sharing solution Proceedings of the 19th International Conference on High Performance Computing (HiPC), Pune, India 2012 1 10NVIDIA The NVIDIA GPU Computing SDK Version 4, NVIDIA 2011Sandia National Labs LAMMPS molecular dynamics simulator http://lammps.sandia.gov/Citrix Systems, Inc. Xen http://xen.org/Peña AJ Virtualization of accelerators in high performance clusters Ph.D. Thesis, 2013NVIDIA CUDA profiler user's guide version 5, NVIDIA 2012Igual, F. D., Chan, E., Quintana-Ortí, E. S., Quintana-Ortí, G., van de Geijn, R. A., & Van Zee, F. G. (2012). The FLAME approach: From dense linear algebra algorithms to high-performance multi-accelerator implementations. Journal of Parallel and Distributed Computing, 72(9), 1134-1143. doi:10.1016/j.jpdc.2011.10.014Slurm workload manager http://slurm.schedmd.co

Grid enabled virtual screening against malaria

34 pages, 5 figures, 3 tables, to appear in Journal of Grid Computing - PCSV, à paraître dans Journal of Grid ComputingWISDOM is an international initiative to enable a virtual screening pipeline on a grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public grid infrastructure. Protein-ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to pervasive grids. The first large scale docking experiment ran on the EGEE grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting Avian Flu neuraminidase N1

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing

The distributed computing attempts to improve performance in large-scale computing problems by resource sharing. Moreover, rising low-cost computing power coupled with advances in communications/networking and the advent of big data, now enables new distributed computing paradigms such as Cloud, Jungle and Fog computing.Cloud computing brings a number of advantages to consumers in terms of accessibility and elasticity. It is based on centralization of resources that possess huge processing power and storage capacities. Fog computing, in contrast, is pushing the frontier of computing away from centralized nodes to the edge of a network, to enable computing at the source of the data. On the other hand, Jungle computing includes a simultaneous combination of clusters, grids, clouds, and so on, in order to gain maximum potential computing power.To understand these new buzzwords, reviewing these paradigms together can be useful. Therefore, this paper describes the advent of new forms of distributed computing. It provides a definition for Cloud, Jungle and Fog computing, and the key characteristics of them are determined. In addition, their architectures are illustrated and, finally, several main use cases are introduced

Overlapping of Communication and Computation and Early Binding: Fundamental Mechanisms for Improving Parallel Performance on Clusters of Workstations

This study considers software techniques for improving performance on clusters of workstations and approaches for designing message-passing middleware that facilitate scalable, parallel processing. Early binding and overlapping of communication and computation are identified as fundamental approaches for improving parallel performance and scalability on clusters. Currently, cluster computers using the Message-Passing Interface for interprocess communication are the predominant choice for building high-performance computing facilities, which makes the findings of this work relevant to a wide audience from the areas of high-performance computing and parallel processing. The performance-enhancing techniques studied in this work are presently underutilized in practice because of the lack of adequate support by existing message-passing libraries and are also rarely considered by parallel algorithm designers. Furthermore, commonly accepted methods for performance analysis and evaluation of parallel systems omit these techniques and focus primarily on more obvious communication characteristics such as latency and bandwidth. This study provides a theoretical framework for describing early binding and overlapping of communication and computation in models for parallel programming. This framework defines four new performance metrics that facilitate new approaches for performance analysis of parallel systems and algorithms. This dissertation provides experimental data that validate the correctness and accuracy of the performance analysis based on the new framework. The theoretical results of this performance analysis can be used by designers of parallel system and application software for assessing the quality of their implementations and for predicting the effective performance benefits of early binding and overlapping. This work presents MPI/Pro, a new MPI implementation that is specifically optimized for clusters of workstations interconnected with high-speed networks. This MPI implementation emphasizes features such as persistent communication, asynchronous processing, low processor overhead, and independent message progress. These features are identified as critical for delivering maximum performance to applications. The experimental section of this dissertation demonstrates the capability of MPI/Pro to facilitate software techniques that result in significant application performance improvements. Specific demonstrations with Virtual Interface Architecture and TCP/IP over Ethernet are offered

Virtual Organization Clusters: Self-Provisioned Clouds on the Grid

Virtual Organization Clusters (VOCs) provide a novel architecture for overlaying dedicated cluster systems on existing grid infrastructures. VOCs provide customized, homogeneous execution environments on a per-Virtual Organization basis, without the cost of physical cluster construction or the overhead of per-job containers. Administrative access and overlay network capabilities are granted to Virtual Organizations (VOs) that choose to implement VOC technology, while the system remains completely transparent to end users and non-participating VOs. Unlike alternative systems that require explicit leases, VOCs are autonomically self-provisioned according to configurable usage policies. As a grid computing architecture, VOCs are designed to be technology agnostic and are implementable by any combination of software and services that follows the Virtual Organization Cluster Model. As demonstrated through simulation testing and evaluation of an implemented prototype, VOCs are a viable mechanism for increasing end-user job compatibility on grid sites. On existing production grids, where jobs are frequently submitted to a small subset of sites and thus experience high queuing delays relative to average job length, the grid-wide addition of VOCs does not adversely affect mean job sojourn time. By load-balancing jobs among grid sites, VOCs can reduce the total amount of queuing on a grid to a level sufficient to counteract the performance overhead introduced by virtualization