StarCluster Brings HPC to the Amazon Cloud

By Justin Riley

May 18, 2010

Setting up an HPC cluster in the cloud can be a daunting task for new users looking to utilize the cloud to run their HPC applications. Learning the ins and outs of the infrastructure as a service (IaaS) model in addition to configuring and installing a typical HPC system is not an easy task.

In order to use the cloud effectively users need to be able to automate the process of requesting and configuring new resources and also terminate resources when they’re no longer required without losing data. These concerns can be a challenge even for advanced users and require some level of cloud programming in order to get it right. In an effort to improve this situation, the Software Tools for Academics and Researchers (STAR) group at MIT has created an open-source project called StarCluster that allows anyone to create and manage their own HPC clusters hosted on Amazon’s Elastic Compute Cloud (EC2) without needing to be a cloud expert.

StarCluster Configuration

One of StarCluster’s primary goals is to be simple to use and to hide as many of the cloud computing details from users as possible. When a new user attempts to use StarCluster for the first time an example configuration file is created that is ready to be used out-of-the-box. The user simply needs to fill in the EC2 account information and optionally customize the number of machines to use before he or she is ready to start a cluster. Starting a cluster with the example configuration will launch a two-machine cluster using the cheapest instance types available on EC2. This allows users to experiment with StarCluster for the first time without dramatic up-front costs.

The group of cluster-specific settings in the configuration file is known as a “cluster template”. StarCluster supports defining multiple cluster templates which can be used when launching a cluster. For example, it’s often useful to have separate templates for different cluster sizes such as a template that defines a small two-machine cluster and another template that defines a large ten-machine cluster. These templates can be specified at runtime to allow a variety of configurations to be used when starting a cluster.

Starting an HPC Cluster on EC2

Once the configuration file has been created, starting a cluster is as simple as running “starcluster start mynewcluster” at the command line. This command will first verify that all settings in the configuration file are valid and are likely to create a working system. Once the settings in the configuration file have been verified, the “start” command creates a new cluster based on these settings with a tag-name of “mynewcluster” on EC2.

Once the “start” command has finished the user can login to the “master” machine as root by running “starcluster sshmaster mynewcluster”. At this point the user has the (root) keys to the cluster just as they would with their own local resources.

StarCluster also has the ability to create multiple HPC clusters. Running the same “start” command again with a different tag-name will launch another HPC cluster in the cloud using the same settings as the previous run. If you’ve defined additional cluster templates in the configuration file these can optionally be used to specify a different group of settings to use when starting the next cluster.

Once the user has finished using a cluster they simply specify its tag-name to StarCluster’s “stop” command to shut it down. For the “mynewcluster” example above the command would be “starcluster stop mynewcluster”. The “stop” command will shutdown the entire cluster and terminate the billing period.

Automated HPC Cluster Configuration

StarCluster automatically configures each machine with the appropriate networking settings needed to communicate with the rest of the cluster. On top of this, StarCluster also fully configures password-less SSH communication for both the root user and a normal user on the cluster. Password-less SSH allows a user to login remotely between machines in the cluster without using a password. This is useful when administering the machines in the cloud and is also a necessary requirement for OpenMPI communication.

Most clusters usually have some form of a queuing system for submitting and load-balancing many computationally intensive tasks or “jobs” and StarCluster is no exception. Out-of-the-box, StarCluster installs and configures the open-source version of the Sun Grid Engine (SGE) queuing system for running distributed and parallel jobs on the cluster. A parallel queue is also configured by default that enables SGE to monitor and account for parallel tasks that use more than one machine in a single job.

Many parallel tasks are commonly written using the Message Passing Interface (MPI). For MPI users, StarCluster includes an SGE-aware OpenMPI installation that provides tight integration between the SGE job scheduler and MPI applications. This integration removes the need for users to specify a list of hosts to use when running an MPI job. Rather, OpenMPI will automatically fetch the host info it needs directly from SGE and begin execution. This allows all machines involved in the MPI calculation to be correctly accounted for by the queuing system.

Sharing files between machines without manually copying files around is a requirement for most HPC systems. Typically this is done using a shared folder via the network file system (NFS). StarCluster automatically configures /home on each “worker” machine of the cluster to be NFS-shared from the “master” machine. This allows users to see their files on any machine in the cluster and also provides a globally accessible place for jobs to read input data and write their finished results.

The StarCluster Amazon Machine Image (AMI)

Amazon Machine Images are used by EC2 to load an entire operating system along with various applications, libraries, and data onto a newly requested virtual machine. Machine images are publicly available for just about any Linux distribution, Solaris, and even Microsoft Windows. New images can be created with custom software configurations by launching a new virtual machine from an existing AMI, installing your new software, and then running an AMI creation process on the machine to create a new AMI.

StarCluster comes with a publicly available custom-tailored AMI, in both 32bit and 64bit flavors, that contains the entire OS and software configuration needed for an HPC cluster on Amazon. The StarCluster AMI is Ubuntu Linux 9.10 based and includes the Sun Grid Engine queuing system (open-source edition), the network file system, and OpenMPI along with common development tools and libraries to compile new software from source. The StarCluster AMI also includes a custom-compiled installation of the Automatically Tuned Linear Algebra Subroutines (ATLAS) and Linear Algebra PACKage (LAPACK) libraries that have been optimized for the larger high-CPU instance types on EC2. For numerical python users, the AMI contains both NumPy and SciPy installations that have been custom compiled against the optimized LAPACK/ATLAS installations. These optimized libraries provide a significant performance improvement when running linear algebra routines in the cloud.

Of course, StarCluster does not limit you to only these software installations. The StarCluster AMIs can easily be extended with your own software to create a brand-new AMI tailored for a specific need. To simplify the AMI creation process StarCluster provides a “createimage” command that will automatically create a new AMI from a running Amazon EC2 virtual machine in the cloud. This allows you to launch a single virtual machine, install your software, and easily create a new AMI from this machine. Using a new customized AMI with StarCluster is as simple as updating the configuration file with the new AMI’s identifier.

Using EBS Volumes for Persistent Storage

Amazon also provides a service called Elastic Block Storage (EBS) which allows users to create virtual block storage volumes that are similar in functionality to a USB pen-drive. These volumes can be anywhere from 1GB to 1TB in size and can be attached to a single virtual machine in the cloud at a time. The benefit of using these volumes is that any data written to EBS is automatically stored and persisted in the cloud even after all virtual machines have been terminated. This means the next time you start a cluster and attach the EBS volume, all of your data will be available as it was the last time you launched a cluster. Another benefit of using EBS volumes is that they’re easy to snapshot and duplicate which allows for backing up large amounts of data in the cloud.

StarCluster has the ability to utilize Amazon’s EBS volumes to provide persistent data storage for a given cluster. To use EBS with StarCluster you must first create an EBS volume. For new users, this process is simplified by using StarCluster’s “createvolume” command. This command automates the process of creating, partitioning, and formatting a new EBS volume.

Using a new volume with StarCluster involves adding additional volume settings to the configuration file. These settings specify the volume to use and the location on the cluster’s file system to attach the volume. This file system location is then NFS-shared from the “master” machine to all “worker” machines. StarCluster does not limit you to using a single EBS volume. Multiple EBS volumes can be configured, attached, and shared on the cluster. This allows up to several terabytes of data to be stored on the cluster.Getting Started with StarCluster

StarCluster is open-source software and can be downloaded for free from the StarCluster website at http://web.mit.edu/starcluster or from the Python Package Index (PyPI) at http://pypi.python.org/pypi/StarCluster.

UPDATE: We now have a video screencast of StarCluster in action that can be viewed here.

About the Author

Justin Riley is a software developer for the Software Tools for Academics and Researchers (STAR) group at the Massachusetts Institute of Technology (MIT). The STAR group seeks to bridge the divide between scientific research and the classroom by collaborating with faculty from MIT and other educational institutions to design software that explores core scientific research concepts. The STAR group works out of the Office of Educational Innovation and Technology (OEIT) under the Dean for Undergraduate Education (DUE) at MIT.

Justin has been developing with the Amazon cloud for the past three years and has successfully used the cloud to support the “Introduction to Modeling and Simulation” and “Intro to Parallel Programming for Multicore Machines using OpenMP and OpenMPI” courses at MIT. His work with StarCluster came directly from the need to provide a sustainable solution to the issues associated with bringing computational resources into the classroom. Justin created StarCluster to automate the process of locating, configuring, and maintaining computational resources without needing to be a 24/7 system administrator and without having to make a physical appearance to address potential hardware and software issues.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

HPE Extreme Performance Solutions

Object Storage is the Ideal Storage Method for CME Companies

The communications, media, and entertainment (CME) sector is experiencing a massive paradigm shift driven by rising data volumes and the demand for high-performance data analytics. Read more…

Weekly Twitter Roundup (Feb. 16, 2017)

February 16, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

Alexander Named Dep. Dir. of Brookhaven Computational Initiative

February 15, 2017

Francis Alexander, a physicist with extensive management and leadership experience in computational science research, has been named Deputy Director of the Computational Science Initiative at the U.S. Read more…

Here’s What a Neural Net Looks Like On the Inside

February 15, 2017

Ever wonder what the inside of a machine learning model looks like? Today Graphcore released fascinating images that show how the computational graph concept maps to a new graph processor and graph programming framework it’s creating. Read more…

By Alex Woodie

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

HPC Cloud Startup Launches ‘App Store’ for HPC Workflows

February 9, 2017

“Civilization advances by extending the number of important operations which we can perform without thinking about them,” Read more…

By Tiffany Trader

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Leading Solution Providers

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This