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 or from the Python Package Index (PyPI) at

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!

Cray Introduces All Flash Lustre Storage Solution Targeting HPC

June 19, 2018

Citing the rise of IOPS-intensive workflows and more affordable flash technology, Cray today introduced the L300F, a scalable all-flash storage solution whose primary use case is to support high IOPS rates to/from a scra Read more…

By John Russell

Lenovo to Debut ‘Neptune’ Cooling Technologies at ISC

June 19, 2018

Lenovo today announced a set of cooling technologies, dubbed Neptune, that include direct to node (DTN) warm water cooling, rear door heat exchanger (RDHX), and hybrid solutions that combine air and liquid cooling. Lenov Read more…

By John Russell

World Cup is Lame Compared to This Competition

June 18, 2018

So you think World Cup soccer is a big deal? While I’m sure it’s very compelling to watch a bunch of athletes kick a ball around, World Cup misses the boat because it doesn’t include teams putting together their ow Read more…

By Dan Olds

HPE Extreme Performance Solutions

HPC and AI Convergence is Accelerating New Levels of Intelligence

Data analytics is the most valuable tool in the digital marketplace – so much so that organizations are employing high performance computing (HPC) capabilities to rapidly collect, share, and analyze endless streams of data. Read more…

IBM Accelerated Insights

Banks Boost Infrastructure to Tackle GDPR

As banks become more digital and data-driven, their IT managers are challenged with fast growing data volumes and lines-of-businesses’ (LoBs’) seemingly limitless appetite for analytics. Read more…

IBM Demonstrates Deep Neural Network Training with Analog Memory Devices

June 18, 2018

From smarter, more personalized apps to seemingly-ubiquitous Google Assistant and Alexa devices, AI adoption is showing no signs of slowing down – and yet, the hardware used for AI is far from perfect. Currently, GPUs Read more…

By Oliver Peckham

Cray Introduces All Flash Lustre Storage Solution Targeting HPC

June 19, 2018

Citing the rise of IOPS-intensive workflows and more affordable flash technology, Cray today introduced the L300F, a scalable all-flash storage solution whose p Read more…

By John Russell

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

Xiaoxiang Zhu Receives the 2018 PRACE Ada Lovelace Award for HPC

June 13, 2018

Xiaoxiang Zhu, who works for the German Aerospace Center (DLR) and Technical University of Munich (TUM), was awarded the 2018 PRACE Ada Lovelace Award for HPC for her outstanding contributions in the field of high performance computing (HPC) in Europe. Read more…

By Elizabeth Leake

U.S Considering Launch of National Quantum Initiative

June 11, 2018

Sometime this month the U.S. House Science Committee will introduce legislation to launch a 10-year National Quantum Initiative, according to a recent report by Read more…

By John Russell

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

Exascale USA – Continuing to Move Forward

June 6, 2018

The end of May 2018, saw several important events that continue to advance the Department of Energy’s (DOE) Exascale Computing Initiative (ECI) for the United Read more…

By Alex R. Larzelere

Exascale for the Rest of Us: Exaflops Systems Capable for Industry

June 6, 2018

Enterprise advanced scale computing – or HPC in the enterprise – is an entity unto itself, situated between (and with characteristics of) conventional enter Read more…

By Doug Black

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Sympo Read more…

By Staff

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17


AMD @ SC17


ASRock Rack @ SC17

ASRock Rack



DDN Storage @ SC17

DDN Storage

Huawei @ SC17


IBM @ SC17


IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17


Lenovo @ SC17


Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17


Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17


Tyan @ SC17


Univa @ SC17


Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

Google I/O 2018: AI Everywhere; TPU 3.0 Delivers 100+ Petaflops but Requires Liquid Cooling

May 9, 2018

All things AI dominated discussion at yesterday’s opening of Google’s I/O 2018 developers meeting covering much of Google's near-term product roadmap. The e Read more…

By John Russell

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This