Cheaha

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Both the traditional scheduler-based interface and the DRMMA API have been explored by development groups on-campus during the pilot evaluation phase of GridWay.   
 
Both the traditional scheduler-based interface and the DRMMA API have been explored by development groups on-campus during the pilot evaluation phase of GridWay.   
* UAB IT, CIS and the School of Public Health [http://www.soph.uab.edu/ssg/ Section on Statistical Genetics (SSG)] [http://projects.uabgrid.uab.edu/r-group collaborated] to  explore the scheduler-based interface to improve the performance of select [http://en.wikipedia.org/wiki/R_(programming_language) R-language] statistical analysis workflows.
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* UAB IT, UAB's School of Public Health [http://www.ssg.uab.edu/ Section on Statistical Genetics (SSG)], and  CIS and t [http://projects.uabgrid.uab.edu/r-group collaborated] to  explore the scheduler-based interface to improve the performance of select [http://en.wikipedia.org/wiki/R_(programming_language) R-language] statistical analysis workflows.
 
* The CIS Collaborative Computing Laboratory has heavily leveraged the Java DRMMA API to develop [http://www.cis.uab.edu/ccl/index.php/DynamicBLAST DynamicBLAST], a scientific workflow to orchestrate and maximize the performance of [http://en.wikipedia.org/wiki/BLAST BLAST] across multiple resources.
 
* The CIS Collaborative Computing Laboratory has heavily leveraged the Java DRMMA API to develop [http://www.cis.uab.edu/ccl/index.php/DynamicBLAST DynamicBLAST], a scientific workflow to orchestrate and maximize the performance of [http://en.wikipedia.org/wiki/BLAST BLAST] across multiple resources.
  

Revision as of 15:52, 20 January 2009

Cheaha is a campus resource dedicated to enhancing research computing productivity at UAB. Cheaha is sponsored by UAB Information Technology (UAB IT) and is available to members of the UAB community in need of increased computational capacity. Cheaha supports high-performance computing (HPC) and high-throughput computing (HTC) paradigms and is the primary interface for leveraging computational resources on UABgrid, the campus distributed research support infrastructure.

Cheaha includes a dedicated pool of local compute resources and provides seamless access to remote compute resources through the use of inter-cluster scheduling technologies. The local compute pool contains two processor banks based on the x86-64 64-bit architecture. 192 3.0Ghz cores and 120 1.6Ghz cores combine to provide nearly 3TFlops of dedicated computing power.

Use of the local compute pool is governed by scheduling policies designed to maximize availability of total capacity and ensure guaranteed access to reserved resources. Use of the remote compute pool is contingent upon allocations for individual users on specific resources. Incorporation of remote resources enables simplified management of scientific workflows and can significantly increase available compute capacity.

Cheaha is located in the UAB Shared Computing facility in BEC. Resource design and development is lead by UAB IT Infrastructure Services in open collaboration with community members. Development effort is coordinated though Cheaha's project web site. Operational support is provided by UAB's School of Engineering cluster support group.

Cheaha is named in honor of Cheaha Mountain, the highest peak in the state of Alabama. Cheaha is a popular destination whose summit offers clear vistas of the surrounding landscape. (A Cheaha Mountain photo-stream on Flikr).

Contents

History

Logical Diagram of Cheaha Configuration with Development Phase 1 and Phase 2 Highligthed

In 2002 UAB was awarded an infrastructure development grant through the NSF EPsCoR program. This led to the 2005 acquisition of a 64 node compute cluster with two AMD Opteron 242 1.6Ghz CPUs per node (128 total cores). This cluster was named Cheaha. Cheaha expanded the compute capacity available at UAB and was the first general-access resource for the community. It lead to expanded roles for UAB IT in research computing support through the development of the UAB Shared HPC Facility in BEC and provided further engagement in Globus-based grid computing resource development on campus via UABgrid and regionally via SURAgrid.

2008 Upgrade

In 2008, money was allocated by UAB IT for hardware upgrades which lead to the acquisition of an additional 192 cores based on a Dell clustering solution with Intel Quad-Core E5450 3.0Ghz CPU in August of 2008. This hardware represented a major technology upgrade that included space for additional expansion to address over-all capacity demand and enable resource reservation.

This upgrade also included enhancements to enable access to the aggregate compute power available to the UAB community and improve management of compute jobs across clusters that are part of the UABgrid computing infrastructure. 10Gigabit Ethernet connectivity to the UABgrid Research Network supports high speed data transfers between clusters connected to this network, enabling efficient job staging on multiple resources. GridWay-based meta-scheduling enables management of compute jobs across cluster boundaries and brings grid-computing into production.

Continuous Resource Improvement

The 2008 upgrade began a phased development approach for Cheaha with on-going increases in capacity and feature enhancements being brought into production via an open community process. The first two phases are represented in the diagram on the right, which highlights the logical connectivity between resources. Phase 1 is scheduled for production in January 2009.

System Profile

Hardware

Cheaha is composed of a number commodity hardware components. The current configuration is based on the Dell M1000e cluster technology which supports modular configurations of blade-based servers. The 2008 Hardware Upgrade included a 2950 head node with 24 server blades across 2 blade chassis, each with 2 3.0GHz quad-core Intel Xeon E5450 processors and 16Gb RAM (2Gb/core). This processing power is supplemented with the 1st generation system components from the original cluster acquisition which include 120 blades with 2 1.6 AMD Opteron 242 processors and 2Gb RAM (1Gb/core).

Summarized, Cheaha's dedicated compute pool includes:

  • 192 cores at 3.0GHz with 2Gb RAM per core
  • 120 cores at 1.6GhZ with 1Gb RAM per core

Software

Cheaha's software environment is built with the ROCKS cluster software, which bases the operating system environment on CentOS. The based system software profile for Cheaha includes:

A summary of the available computational software and tools available includes:

  • Amber
  • FFTW
  • Gromacs
  • GSL
  • NAMD
  • VMD
  • Intel Compilers
  • GNU Compilers
  • Java
  • R
  • OpenMPI

Details are available on the Cheaha cluster configuration page.

Availability

Cheaha is a general-purpose computer resource made available to the UAB community by UAB IT. As such, it is available for legitimate research and educational needs and is governed by UAB's Acceptable Use Policy (AUP) for computer resources.

Many software packages commonly used across UAB are available via Cheaha. For more information and introductory help on using this resource please visit the resource details page.

To request access to Cheaha, please submit an authorization request to the School of Engineering cluster support group.

Cheaha's intended use implies broad access to the community, however, no guarantees are made that specific computational resources will be available to all users. Availability guarantees can only be made for reserved resources.

Scheduling Framework

Cheaha provides performance and management improvements for scientific workflows by enabling access to the processing power of multiple clusters through the use of the GridWay scheduling framework. GridWay enables the development of scientific workflows that leverage all computing resources available to the researcher and that can be controlled through a single management interface. This feature puts state-of-the-art technology in the hands of the research community.

Enhancements to Cheaha in general, and, in particular, its scheduling framework are intended to remain transparent to the user community. Cheaha is first and foremost a resource for predictable and dependable computation. In this spirit, Cheaha can continue to be viewed as a traditional HPC cluster that supports job management via Sun Grid Engine (SGE). User's who have no need for or interest in maximized access to computational resources can continue using the familiar SGE scheduling framework to manage compute jobs on Cheaha, with the familiar restriction that SGE managed jobs can only leverage the processing power of Cheaha's local compute pool. That is, these jobs, as in the past, cannot leverage cycles available on other clusters.

SGE

Cheaha provides access to its local compute pool via the SGE scheduler. This arrangement is identical to the existing HPC clusters on campus and mirrors the long-established configuration of Cheaha. Researchers experienced with other SGE-based clusters should find no difficulty leveraging this feature. For more information on getting started with SGE on Cheaha please see the cluster resources page. For user support requests regarding SGE, please submit a support request on-line.

GridWay

Cheaha provides enhanced scientific workflow management and development capabilities via the GridWay scheduling framework. GridWay enables the orchestration of scientific workflows across multiple clusters. The pool of resources available as part in Phase 1 includes Cheaha, Olympus, Everest, and Ferrum. This pool can be monitored by any user of Cheaha by executing the gwhost command when logged into Cheaha.

The GridWay framework provides two interfaces. A scheduler interface similar to SGE is recommended for initial exploration and ordinary use. The scheduler activity can be monitored with the `gwps` command. Job submit and monitoring commands initiate and control commands described in a job description file. Outside of slightly different commands, the job description file operates as a template where specific fields are populated to affect the operation of the scheduler. These templates are less ambiguous than traditional SGE job scripts and can provide a direct migration path from SGE. A more subtle difference is that an explicit (though automated) job staging step is involved in order to start jobs. This can require more explicit handling of input and output files than is ordinarily required by SGE.

Additionally, very powerful programatic control is available via the DRMAA API. DRMMA enables the development of advanced scientific workflows that can leverage any number of computational resources. GridWay provides bindings to many popular programming languages like C, Java, Perl, Python and Ruby.

Both the traditional scheduler-based interface and the DRMMA API have been explored by development groups on-campus during the pilot evaluation phase of GridWay.

  • UAB IT, UAB's School of Public Health Section on Statistical Genetics (SSG), and CIS and t collaborated to explore the scheduler-based interface to improve the performance of select R-language statistical analysis workflows.
  • The CIS Collaborative Computing Laboratory has heavily leveraged the Java DRMMA API to develop DynamicBLAST, a scientific workflow to orchestrate and maximize the performance of BLAST across multiple resources.

GridWay Adoption

Adoption of GridWay is encouraged and future compute capacity enhancements will leverage the inherent flexibilities of GridWay. The nature of any new technology, however, implies a learning curve. The learning curve need not be steep and direct migration of basic SGE scripts is possible. Additionally, all Cheaha accounts are configured to support the use of GridWay as an alternative scheduler, empowering the adventurous.

Some important points worth considering in evaluating adoption of GridWay.

  • GridWay cannot perform magic. If you ordinarily do not have access to other clusters or your code does not (or will not) run on a targeted cluster, GridWay cannot solve these problems for you. You must ensure your codes run on all compute resources you intend to include in your scheduling pool prior to submitting jobs to those resources.
  • Migration of MPI jobs to a multi-cluster environment may involve additional effort. If you simply use MPI to coordinate the workers (rather than for low-latency peer communication), you should generally be able to structure your job to work across cluster boundaries. Otherwise, additional effort may be required to divide your data into smaller work units. It should be noted, however, that MPI itself cannot be used to communicate across cluster boundaries. Jobs distributed across cluster boundaries that leverage MPI internally must be sub-divided to run within isolated communication domains.

The UABgrid User Community stands ready to help you with GridWay adoption. If you are interested in exploring or adapting your workflows to use GridWay please subscribe to the UABgrid-User list and ask all questions there. Please do not submit GridWay-related questions via the ordinary cluster support channels. Additional on-line documentation will be developed to provide migration examples to help you further explore the power of GridWay.

Support

Operational support for Cheaha is provided by the School of Engineering's cluster support group. Please submit support requests directly via the on-line form.

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