HPC Web Portal now in Beta
The new HPC web portal is now available. We encourage you to try it out as an alternative to traditional clients. It provides file, shell, and desktop access to the cluster within your web browser.
Cheaha is a campus resource dedicated to enhancing research computing productivity at UAB. Cheaha is managed by UAB Information Technology's Research Computing group (UAB ITRC) 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.
Cheaha provides users with a traditional command-line interactive environment with access to many scientific tools that can leverage its dedicated pool of local compute resources. Alternately, users of graphical applications can start a cluster desktop. The local compute pool provides access to three generations of compute hardware based on the x86-64 64-bit architecture. It includes 576 2.66 GHz cores of Intel Westmere generation CPUs with 4GB RAM per core and 48 2.70 GHz cores of Intel xxx gneraration CPUs with 24GB RAM per core all attached via a QDR Infiniband interconnect, and 192 3.0GHz cores of Intel Nehalem generation CPUs with 2GB per core attached via a DDR Infiniband interconnect, making up a 816 core compute fabric. The compute nodes are backed by 180TB of high performance Lustre storage on a DDN hardware and an additional 20TB available for home directories on a traditional Hitachi SAN and other ancillary services. The compute nodes combine to provide over 8TFlops of dedicated computing power.
Cheaha is composed of resources that span data centers located in the UAB Shared Computing facility in the Business Engineering Complex (BEC) and the RUST Computer Center. Resource design and development is lead by UAB IT Research Computing in open collaboration with community members. Operational support is provided by UAB IT's Research Computing 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. (Cheaha Mountain photo-streams on Flikr and Picasa).
For information on getting an account, logging in, and running a job, please see Getting Started.
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.
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 uprade migrated Cheaha's core infrastructure to the Dell blade clustering solution. It provided a 3 fold increase in processor density over the original hardware and enables more computing power to be located in the same physical space with room for expansion, an important consideration in light of the continued growth in processing demand. This hardware represented a major technology upgrade that included space for additional expansion to address over-all capacity demand and enable resource reservation.
The 2008 upgrade began a continuous resource improvement plan that includes a phased development approach for Cheaha with on-going increases in capacity and feature enhancements being brought into production via an open community process.
Software improvements rolled into the 2008 upgrade included grid computing services to access distributed compute resources and orchestrate jobs using the GridWay meta-scheduler. An initial 10Gigabit Ethernet link establishing the UABgrid Research Network was designed to supports high speed data transfers between clusters connected to this network.
In 2009, annual investment funds were directed toward establishing a fully connected dual data rate Infiniband network between the compute nodes added in 2008 and laying the foundation for a research storage system with a 60TB DDN storage system accessed via the Lustre distributed file system. The Infiniband and storage fabrics were designed to support significant increases in research data sets and their associate analytical demand.
In 2010, UAB was awarded an NIH Small Instrumentation Grant (SIG) to further increase analytical and storage capacity. The grant funds were combined with the annual investment funds adding 576 cores (48 nodes) based on the Intel Westmere 2.66 GHz CPU, a quad data rate Infiniband fabric with 32 uplinks, an additional 120 TB of storage for the DDN fabric, and additional hardware to improve reliability. Additional improvements to the research compute platform involved extending the UAB Research Network to link the BEC and RUST data centers, adding 20TB of user and ancillary services storage
In 2012, UAB IT Research Computing invested in the foundation hardware to expand long term storage and virtual machine capabilities with aqcuisition of 12 Dell 720xd system, each containing 16 cores, 96GB RAM, and 36TB of storage, creating a 192 core and 432TB virtual compute and storage fabric.
Additionaly hardware investment by the School of Public Health's Section on Statistical Genetics added three 384GB large memory nodes and an additional 48 cores to the QDR Infiniband fabric.
In 2013, UAB IT Research Computing acquired an OpenStack cloud and Ceph storage software fabric through a partnership between Dell and Inktank in order to extend cloud computing solutions to the researchers at UAB and enhance the interfacing capabilities for HPC. This fabric is under active development and will see feature releases in the coming months.
Grant and Publication Resources
The following description may prove useful in summarizing the services available via Cheaha. If you are using Cheaha for grant funded research please send information about your grant (funding source and grant number), a statement of intent for the research project and a list of the applications you are using to UAB IT Research Computing. If you are using Cheaha for exploratory research, please send a similar note on your research interest. Finally, any publications that rely on computations performed on Cheaha should include a statement acknowledging the use of UAB Research Computing facilities in your research, see the suggested example below. Please note, your acknowledgment may also need to include an addition statement acknowledging grant-funded hardware. We also ask that you send any references to publications based on your use of Cheaha compute resources.
Description of Cheaha for Grants
UAB IT Research Computing maintains high performance compute and storage resources for investigators. The Cheaha compute cluster includes 192 3.0GHz Intel-based compute cores with 386GB of RAM interconnected via a DDR Infiniband network. A high-performance, 180TB Lustre parallel file system built on a Direct Data Network (DDN) hardware platform is also connected to these cores via the Infiniband fabric. An additional 20TB of traditional SAN storage is available via a 1GigE network fabric. This general access compute fabric is available to all UAB investigators.
Additionally, NIH funded investigators are granted priority access to the NIH SIG award acquired compute and storage pool that includes an additional 576 2.66GHz Intel-based compute cores, 2.3TB RAM and 180TB in the high-performance Lustre parallel file system all interconnected via a QDR Infiniband network fabric.
Acknowledgment in Publications
This work was supported in part by the research computing resources acquired and managed by UAB IT Research Computing. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the University of Alabama at Birmingham.
Additionally, NIH funded researchers using the cluster for genomics or proteomics analyses should include the following statement regarding the NIH funded gen3 (SIG) compute resources.
"Computational portions of this research were supported by NIH S10RR026723. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Institutes of Health"
|Generation||Cpu Tflops Per Node||Gpu Tflops Per Node||Tflops Per Node||Nodes||Tflops|
The Cheaha Compute Platform includes three generations of commodity compute hardware, totaling 868 compute cores, 2.8TB of RAM, and over 200TB of storage.
The hardware is grouped into generations designated gen1, gen2, and gen3 (oldest to newest). The following descriptions highlight the hardware profile for each generation.
Generation 1 (gen1) -- 64 2-CPU AMD 1.6 GHz compute nodes with Gigabit interconnect. This is the original hardware collection purchased with NSF EPSCoR funds in 2005, approx $150K. These nodes are sometimes called the "Verari" nodes. These nodes are tagged as "verari-compute-#-#" in the ROCKS naming convention.(gen1 was decommissioned in June 2013)
- Generation 2 (gen2) -- 24 2x4 core (196 cores total) Intel 3.0 GHz Intel compute nodes with dual data rate Infiniband interconnect and the initial high-perf storage implementation using 60TB DDN. This is the hardware collection purchased exclusively with the annual VPIT funds allocation, approx $150K/yr for the 2008 and 2009 fiscal years. These nodes are sometimes confusingly called "cheaha2" or "cheaha" nodes. These nodes are tagged as "cheaha-compute-#-#" in the ROCKS naming convention.
- Generation 3 (gen3) -- 48 2x6 core (576 cores total) 2.66 GHz Intel compute nodes with quad data rate Infiniband, ScaleMP, and the high-perf storage build-out for capacity and redundancy with 120TB DDN. This is the hardware collection purchased with a combination of the NIH SIG funds and some of the 2010 annual VPIT investment. These nodes were given the code name "sipsey" and tagged as such in the node naming for the queue system. These nodes are tagged as "sipsey-compute-#-#" in the ROCKS naming convention.
- Generation 4 (gen4) -- 3 16 core (48 cores total) compute nodes. This hardware collection purchase by Hemant Tiwari of SSG. These nodes were given the code name "ssg" and tagged as such in the node naming for the queue system. These nodes are tagged as "ssg-compute-0-#" in the ROCKS naming convention.
Summarized, Cheaha's compute pool includes:
- gen4 is 48 cores with 24G of RAM per core.
- gen3 is 576 cores of 2.66GHz with 4Gb RAM per core
- gen2 is 192 cores of 3.0GHz quad-core Intel Xeon E5450 processors with 2Gb RAM per core
gen1 is 100 cores of 1.6GhZ AMD Opteron 242 processors with 1Gb RAM per core(decomissioned June 2013)
Details of the software available on Cheaha can be found on the Cheaha cluster configuration page, an overview follows.
Cheaha's software stack is built with the ROCKS, a Linux-based cluster distribution. Cheaha's operating system is CentOS with the following major cluster components:
A brief summary of the some of the available computational software and tools available includes:
- Intel Compilers
- GNU Compilers
Cheaha is connected to the UAB Research Network which provides a dedicated 10Gbs networking backplane between clusters located the UAB Shared Computing Facility and Department of Computer and Information Science HPC Center. At present only Cheaha and Ferrum are connected via these 10Gbs interfaces. Data transfers rates of almost 8Gbs between these hosts have been demonstrated using Grid FTP, a multi-channel file transfer service that is used by GridWay to move data between clusters as part of the job management operations. This performance promises very efficient job management and the seamless integration of other clusters as connectivity to the research network is expanded.
The continuous resource improvement process involves collecting benchmarks of the system. One of the measures of greatest interest to users of the system are benchmarks of specific application codes. The following benchmarks have been performed on the system and will be further expanded as additional benchmarks are performed.
Cheaha uses Ganglia to report cluster performance data. This information provides a helpful overview of the current and historical operating stats for Cheaha. You can access the Ganglia monitoring page here.
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 send a request to send a request to the 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.
Secure Shell Access
Please configure you client secure shell software to use the official host name to access Cheaha:
To ensure that you are connecting to the legitimate host, you can verify that the fingerprint presented by your secure shell client for cheaha.uabgrid.uab.edu matches:
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.
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.
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 established an interest group for R which leveraged 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.
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.
Operational support for Cheaha is provided by the Research Computing group in UAB IT. For questions regarding the operational status of Cheaha please send your request to email@example.com.
We have limited capacity, however, to support non-operational issue like "How do I write a job script" or "How do I compile a program". For such requests, you may find it more fruitful to send your questions to the hpc-users email list and request help from our peers in the HPC community at UAB. As with all mailing lists, please observe common mailing etiquette.
Finally, please remember that as you learned about HPC from others it becomes part of your responsibilty to help others on their quest. You should update this documentation or respond to mailing list requests of others.
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