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Globus Toolkit. The Globus Toolkit, is an open source toolkit for grid computing developed and provided by the Globus Alliance. Standards implementation[edit] The Globus Toolkit is an implementation of the following standards: The following Globus Toolkit components are supported by the OGF-defined SAGA C++/Python API: Compatible third-party software[edit] A number of tools can function with Globus Toolkit, including: XML-based web services offer a way to access the diverse services and applications in a distributed environment. In 2004, Univa Corporation began providing commercial support for the Globus Toolkit using a business model similar to that of Red Hat. Job schedulers[edit] GRAM (Grid Resource Allocation Manager), a component of the Globus Toolkit, officially supports the following job schedulers or batch-queuing systems: Unofficial job schedulers that can be used with the Globus Toolkit: Sun Grid Engine, an open source batch-queuing system, supported by Sun Microsystems.

Development plans[edit] Simple Linux Utility for Resource Management. Simple Linux Utility for Resource Management (SLURM) is a free and open-source job scheduler for the Linux kernel used by many of the world's supercomputers and computer clusters. It provides three key functions. First, it allocates exclusive and/or non-exclusive access to resources (computer nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (typically a parallel job such as MPI) on a set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending jobs. SLURM is the batch system on many of the TOP500 supercomputers, including the most powerful computer of all Tianhe-2 with 3.1 million cores and 33.9 Petaflop performance at the NUDT. SLURM uses a best fit algorithm based on Hilbert curve scheduling or fat tree network topology in order to optimize locality of task assignments on parallel computers.[1] History[edit] Structure[edit] Notable features[edit] General.

IBM Tivoli Workload Scheduler LoadLeveler. IBM Tivoli Workload Scheduler is a family of IBM Tivoli workload automation products that plan, execute and track jobs on several platforms and environments. It comprises two products: Workload Scheduler for z/OS, previous known as OPCWorkload Scheduler, previously known as Maestro when IBM acquired Unison Software in 1997 Plus some ancillary applications Workload Scheduler for Applications - for managing business applications like SAP, Oracle and PeopleSoft ()Dynamic Workload Broker - for automating grid application environments Products can be integrated to schedule and monitor from a single point of control with the use of a java console called JSC (Job Scheduling Console) or in the latest versions with a web based user interface called TDWC (Tivoli Dynamic Workload Console). Workload Scheduler for z/OS (TWSz) was originally produced in the 1970s by IBM's Nordic Laboratory in Lidingo, Sweden where it was known as OPC, which stands for "Operations Planning and Control".

Platform LSF. Platform Load Sharing Facility (or simply LSF) is a workload management platform, job scheduler, for distributed HPC environments. It can be used to execute batch jobs on networked Unix and Windows systems on many different architectures.[1][2] In 2007, Platform released Platform Lava, which is a simplified version of LSF based on an old version of LSF release, licensed under GNU General Public License v2.[3] LSF was based on the Utopia research project at the University of Toronto.[4] In Jan 2012, Platform Computing was acquired by IBM.[5] LSF Scheduling Policies[edit] Fair share, preemptive, backfill and SLA schedulingHigh throughput schedulingMulticluster schedulingTopology-, resource-, and energy-aware scheduling LSF Addon Products[edit] IBM Platform Application Center Web interfaces for job submission, management and remote visualization.IBM Platform RTM A real-time dashboard for monitoring global workloads and resource.

IBM Platform License Scheduler IBM Platform Analytic HPC Profile Basic. Enabling Grids for E-sciencE. The European Grid Infrastructure (EGI) is a series of efforts to provide access to high-throughput computing resources across Europe using grid computing techniques.[1] The EGI links centres in different European countries to support international research in many scientific disciplines.

Following a series of research projects such as DataGrid and Enabling Grids for E-sciencE, the EGI.eu organization was formed in 2010 to sustain the services of the EGI.[2] Purpose[edit] Science has become increasingly based on open collaboration between researchers across the world. It uses high-capacity computing to model complex systems and to process experimental results.

In the early 21st century, Grid computing became popular for scientific disciplines such as high-energy physics, bioinformatics to share and combine the power of computers and sophisticated, often unique, scientific instruments in a process known as e-Science.[2] EGI is partially supported by the EGI-InSPIRE EC project. History[edit] Xgrid. Xgrid is a proprietary program and distributed computing protocol developed by the Advanced Computation Group subdivision of Apple Inc that allows networked computers to contribute to a single task. It provides network administrators a method of creating a computing cluster, which allows them to exploit previously unused computational power for calculations that can be divided easily into smaller operations, such as Mandelbrot maps. The setup of an Xgrid cluster can be achieved at next to no cost, as Xgrid client is pre-installed on all computers running Mac OS X 10.4 to Mac OS X 10.7.

The Xgrid client was not included in Mac OS X 10.8. The Xgrid controller, the job scheduler of the Xgrid operation, is also included within Mac OS X Server and as a free download from Apple. Apple has kept the command-line job control mechanism minimalist while providing an API to develop more sophisticated tools built around it. History[edit] Zilla Protocol[edit] Xgrid Protocol Architecture[edit] Notes[edit] GridWay. GridWay[1] is an open source meta-scheduling technology that enables large-scale, secure, reliable and efficient sharing of computing resources (clusters, computing farms, servers, supercomputers...), managed by different Distributed Resource Management Systems (DRMS), such as SGE, Condor, PBS or LSF, within a single organization (enterprise grid) or scattered across several administrative domains (partner or supply-chain grid).

To this end, GridWay supports several Grid middlewares. Functionality[edit] GridWay provides end users and application developers with a scheduling framework similar to that found on local DRMS, allowing to submit, monitor, synchronize and control jobs by means of a DRMS-like command line interface (gwsubmit, gwwait, gwkill...) and DRMAA (an OGF standard). GridWay performs job execution management and resource brokering, allowing unattended, reliable, and efficient execution of jobs, array jobs, or complex jobs on heterogeneous, dynamic and loosely-coupled Grids. Portable Batch System. Portable Batch System (or simply PBS) is the name of computer software that performs job scheduling. Its primary task is to allocate computational tasks, i.e., batch jobs, among the available computing resources. It is often used in conjunction with UNIX cluster environments. PBS is supported as a job scheduler mechanism by several meta schedulers including Moab by Cluster Resources (which became Adaptive Computing Enterprises Inc.)[1] and GRAM (Grid Resource Allocation Manager), a component of the Globus Toolkit.

History and versions[edit] PBS was originally developed for NASA under a contract project that began on June 17, 1991. The main contractor who developed the original code was MRJ Technology Solutions. The following versions of PBS are currently available: OpenPBS — original open source version released by MRJ in 1998 (not actively developed)TORQUE — a fork of OpenPBS that is maintained by Adaptive Computing Enterprises, Inc.

License[edit] References[edit] Jump up ^ [1] Condor High-Throughput Computing System. HTCondor is an open-source high-throughput computing software framework for coarse-grained distributed parallelization of computationally intensive tasks.[1] It can be used to manage workload on a dedicated cluster of computers, and/or to farm out work to idle desktop computers – so-called cycle scavenging. HTCondor runs on Linux, Unix, Mac OS X, FreeBSD, and contemporary Windows operating systems. HTCondor can seamlessly integrate both dedicated resources (rack-mounted clusters) and non-dedicated desktop machines (cycle scavenging) into one computing environment. HTCondor was formerly known as Condor; the name was changed in October 2012 to resolve a trademark lawsuit.[2] HTCondor can run both sequential and parallel jobs.

In the world of parallel jobs, HTCondor supports the standard MPI and PVM (Goux, et al. 2000) in addition to its own Master Worker "MW" library for extremely parallel tasks. HTCondor-G allows HTCondor jobs to use resources not under its direct control. See also[edit] DRMAA. In 2007, DRMAA was one of the first two (the other one was GridRPC) specifications that reached the full recommendation status in the Open Grid Forum.[1] Development Model[edit] The development of this API was done through the Global Grid Forum, in the model of IETF standard development, and it was originally co-authored by: Roger Brobst from Cadence Design SystemsWaiman Chan from IBMFritz Ferstl from Sun Microsystems, now UnivaJeff Gardiner from John P. Robarts Research InstituteAndreas Haas from Sun Microsystems (Co-Chair)Bill Nitzberg from Altair EngineeringHrabri Rajic from Intel (Maintainer & Co-Chair)John Tollefsrud from Sun Microsystems Founding (Chair) This specification was first proposed at Global Grid Forum 3 (GGF3)[2] in Frascati, Italy, but gained most of its momentum at Global Grid Forum 4 in Toronto, Ontario.

Significance[edit] Without DRMAA, no standard model existed to submit jobs to component regions of a Grid, assuming each region was running local DRMSs. References[edit] Twp-gridengine-overview-167117.pdf (application/pdf Object) Batch Farm User's Guide - Computer Center Documentation. From Computer Center Documentation Introduction Hardware The batch farm is historically and currently a loosely coupled heterogeneous computing environment where the compute nodes of varying specifications are under a locally developed batch and accounting system (Auger) that is integrated with a data migration subsytem (JASMine).

Operating Systems The Linux operating system is currently the only operating system used on the batch farm. The distribution and version used is CentOS a RedHat enterprise variant. Portable Batch System The batch farm is managed by an open source software package PBS (Portable Batch System) and Maui scheduler. Scheduling Scheduling is done to maximize throughput, and to balance the load between differnet accounts and users. Dispatching Jobs are dispatched to farm nodes that match their resource requirements. Auger Auger is a Jefferson Lab front end to farm batch system. In addition to providing a front end for submitting jobs and pre-staging files from magnetic tap.

. #! Octave - General - running octave in a distributed/compute farm environment. Technical Compute Farm Whitepaper - Improving the Business of Technical Computing. Oracle acquired Sun Microsystems in 2010, and since that time Oracle's hardware and software engineers have worked side-by-side to build fully integrated systems and optimized solutions designed to achieve performance levels that are unmatched in the industry. Early examples include the Oracle Exadata Database Machine X2-8, and the first Oracle Exalogic Elastic Cloud, both introduced in late 2010.

During 2011, Oracle introduced the SPARC SuperCluster T4-4, a general-purpose, engineered system with Oracle Solaris that delivered record-breaking performance on a series of enterprise benchmarks. Oracle's SPARC-based systems are some of the most scalable, reliable, and secure products available today. Sun's prized software portfolio has continued to develop as well, with new releases of Oracle Solaris, MySQL, and the recent introduction of Java 7. Oracle invests in innovation by designing hardware and software systems that are engineered to work together. 806-7330-10. Building a Compute Farm. Posted by daniel on April 21, 2005 at 10:50 AM PDT Scaling up to solve parallelizable problems The idea of a distributed "job jar" is very attractive. Task objects sit in some central ComputeSpace.

If you are a worker, you wait for a task to be assigned to you, you work on the task, and then you return the result to the ComputeSpace and wait for the next task. This is an outgrowth of the Replicated-Worker described in the book "JavaSpaces Principles, Patterns, and Practice". I still say that, as Tom demonstrates, Jini already addresses questions that we are still working hard to solve today with other technologies. Earlier in the week Tom posted about the lack of one liners in Java. Chet Haase points to an article he co-authored in Desktop Java Features in Mustang in today's Doug Twilleager blogs about Mobile Multiplayer Games and 3G. In Get the Latest, Greg Sporar blogs about installing the M6 release of the NetBeans profiler. In part two of their series JSF for nonbelievers. Compute Farm Management | SPK and Associates. Compute Farm Management Need an Efficient Compute Farm to Maximize Your Investment? Most companies take advantage of shared compute power, and Engineering organizations are key users of this resource.

Typically, Engineering runs applications that are CPU and memory intensive, best served by creating compute farms incorporating queue policies. These days there are alternatives for compute farms, from traditional farms of physical servers, to virtualized solutions, hosted solutions, or Cloud computing solutions. Deciding among these requires a knowledge of the Engineering application requirements, hardware, operating system parameters, and other variables. CPU intensive batch jobs such as simulations and analysis runs must be properly scheduled to maximize utilization. SPK can help you with system management and server monitoring. SPK and Associates can design and implement an efficient compute farm use model to maximize your investment. Ba_paper. Eetimes.