High Performance Parallel Computing with Clouds and Cloud Technologies

Infrastructure services (Infrastructure-as-a-service), provided by cloud vendors, allow any user to provision a large number of compute instances fairly easily. Whether leased from public clouds or allocated from private clouds, utilizing these virtual resources to perform data/compute intensive analyses requires employing different parallel runtimes to implement such applications. Among many parallelizable problems, most “pleasingly parallel” applications can be performed using MapReduce technologies such as Hadoop, CGL-MapReduce, and Dryad, in a fairly easy manner. However, many scientific applications, which have complex communication patterns, still require low latency communication mechanisms and rich set of communication constructs offered by runtimes such as MPI. In this paper, we first discuss large scale data analysis using different MapReduce implementations and then, we present a performance analysis of high performance parallel applications on virtualized resources.

[1]  Edward Walker,et al.  Benchmarking Amazon EC2 for High-Performance Scientific Computing , 2008, login Usenix Mag..

[2]  GhemawatSanjay,et al.  The Google file system , 2003 .

[3]  Marlon E. Pierce,et al.  SWARM: Scheduling Large-Scale Jobs over the Loosely-Coupled HPC Clusters , 2008, 2008 IEEE Fourth International Conference on eScience.

[4]  Yuan Yu,et al.  Dryad: distributed data-parallel programs from sequential building blocks , 2007, EuroSys '07.

[5]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[6]  Geoffrey C. Fox,et al.  Matrix algorithms on a hypercube I: Matrix multiplication , 1987, Parallel Comput..

[7]  Chandra Krintz,et al.  Evaluating the Performance Impact of Xen on MPI and Process Execution For HPC Systems , 2006, First International Workshop on Virtualization Technology in Distributed Computing (VTDC 2006).

[8]  George Bosilca,et al.  Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation , 2004, PVM/MPI.

[9]  Constantinos Evangelinos,et al.  Cloud Computing for parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere- , 2008 .

[10]  Geoffrey C. Fox,et al.  NaradaBrokering: A Distributed Middleware Framework and Architecture for Enabling Durable Peer-to-Peer Grids , 2003, Middleware.

[11]  J. Jurka.  Repbase update: a database and an electronic journal of repetitive elements. , 2000, Trends in genetics : TIG.

[12]  Bertram Ludäscher,et al.  Scientific workflow management and the Kepler system: Research Articles , 2006 .

[13]  Geoffrey C. Fox,et al.  MapReduce for Data Intensive Scientific Analyses , 2008, 2008 IEEE Fourth International Conference on eScience.

[14]  Lamia YouseffRich.  Evaluating the Performance Impact of Xen on MPI and Process Execution For HPC Systems , 2006 .

[15]  Vishakha Gupta,et al.  High-Performance Hypervisor Architectures: Virtualization in HPC Systems , 2007 .

[16]  Geoffrey C. Fox,et al.  Parallel Data Mining from Multicore to Cloudy Grids , 2008, High Performance Computing Workshop.

[17]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[18]  Carole A. Goble,et al.  Taverna: a tool for building and running workflows of services , 2006, Nucleic Acids Res..

[19]  Ian T. Foster,et al.  Virtual workspaces: Achieving quality of service and quality of life in the Grid , 2005, Sci. Program..

[20]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[21]  M. Batzer,et al.  Alu repeats and human genomic diversity , 2002, Nature Reviews Genetics.

[22]  S. Lennart Johnsson,et al.  Matrix multiplication on the connection machine , 1989, Proceedings of the 1989 ACM/IEEE Conference on Supercomputing (Supercomputing '89).

[23]  Corporate The MPI Forum.  MPI: a message passing interface , 1993, Supercomputing '93.

[24]  Jack Dongarra,et al.  Integrated Pvm Framework Supports Heterogeneous Network Computing , 1993 .

[25]  Yong Zhao,et al.  Falkon: a Fast and Light-weight tasK executiON framework , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).

[26]  Michael Isard,et al.  DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language , 2008, OSDI.

[27]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[28]  Rob Pike,et al.  Interpreting the data: Parallel analysis with Sawzall , 2005, Sci. Program..

[29]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[30]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..

[31]  O. Gotoh.  An improved algorithm for matching biological sequences. , 1982, Journal of molecular biology.

[32]  X. Huang,et al.  CAP3: A DNA sequence assembly program. , 1999, Genome research.

[33]  Douglas Thain,et al.  All-Pairs: An Abstraction for Data-Intensive Computing on Campus Grids , 2010, IEEE Transactions on Parallel and Distributed Systems.

[34]  Becky Verastegui,et al.  Proceedings of the 2007 ACM/IEEE conference on Supercomputing , 2007, HiPC 2007.

[35]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[36]  Robert L. Grossman,et al.  Sector and Sphere: the design and implementation of a high-performance data cloud , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.