Simulation of HP24stab with AWE and Work Queue

The villin headpiece subdomain "HP24stab" is a recently discovered 24-residue stable supersecondary structure that consists of two helices joined by a turn. Simulating 1μs of motion for HP24stab can take days or weeks depending on the available hardware, and folding events take place on a scale of hundreds of nanoseconds to microseconds.  Using the Accelerated Weighted Ensemble (AWE), a total of 19us of trajectory data were simulated over the course of two months using the OpenMM simulation package. These trajectories were then clustered and sampled to create an AWE system of 1000 states and 10 models per state. A Work Queue master dispatched 10,000 simulations to a peak of 1000 connected 4-core workers, for a total of 250ns of concurrent simulation time and 2.5μs per AWE iteration. As of August 8, 2016, the system has run continuously for 18 days and completed 71 iterations, for a total of 177.5μs of simulation time. The data gathered from these simulations will be used to report the mean first passage time, or average time to fold, for HP24stab, as well as the major folding pathways.  - Jeff Kinnison and Jesús Izaguirre, University of Notre Dame

Work Queue from Raspberry Pi to Azure at SPU

"At Seattle Pacific University we have used Work Queue in the CSC/CPE 4760 Advanced Computer Architecture course in Spring 2014 and Spring 2016.  Work Queue serves as our primary example of a distributed system in our “Distributed and Cloud Computing” unit for the course.  Work Queue was chosen because it is easy to deploy, and undergraduate students can quickly get started working on projects that harness the power of distributed resources."

The main project in this unit had the students obtain benchmark results for three systems: a high performance workstation; a cluster of 12 Raspberry Pi 2 boards, and a cluster of A1 instances in Microsoft Azure.  The task for each benchmark used Dr. Peter Bui’s Work Queue MapReduce framework; the students tested both a Word Count and Inverted Index on the Linux kernel source. In testing the three systems the students were exposed to the principles of distributed computing and the MapReduce model as they investigated tradeoffs in price, performance, and overhead.
 - Prof. Aaron Dingler, Seattle Pacific University. 

CMS Analysis on 10K Cores Using Lobster

We have been working closely with the CMS physics group at Notre Dame for the last year to build Lobster, a data analysis system that runs on O(10K) cores to process data produced by the CMS experiment at the LHC.  At peak, Lobster at ND delivers capacity equal to that of a dedicated CMS Tier-2 facility!

Existing data analysis systems for CMS generally require that the user be running in a cluster that has been set up just so for the purpose: exactly the right operating system, certain software installed, various user identities present, and so on. This is fine for the various clusters dedicated to the CMS experiment, but it leaves unused the enormous amount of computing power that can be found at university computing centers (like the ND CRC), national computing resources (like XSEDE or the Open Science Grid), and public cloud systems.

Lobster is designed to harness clusters that are not dedicated to CMS.  This requires solving two problems:

1. The required software and data are not available on every node.  Instead, Lobster must bring them in at runtime and create the necessary execution system on the fly.
2. A given machine may only be available for a short interval of time before it is taken away and assigned to another user, so Lobster must be efficient at getting things set up, and handy at dealing with disconnections and failures.

To do this, we build upon a variety of technologies for distributed computing.  Lobster uses Work Queue to dispatch tasks to thousands of machines, Parrot with CVMFS to deliver the complex software stack from CERN, XRootD to deliver the LHC data, and Chirp and Hadoop to manage the output data.

Lobster runs effectively on O(10K) cores so far, depending on the CPU/IO ratio of the jobs.  These two graphs show the behavior of a production run on top of HTCondor at Notre Dame hitting up to 10K cores over the course of a 48-hour run.  The top graph shows the number of tasks running simultaneously, while the bottom shows the number of tasks completed or failed in each 10-minute interval.  Note that about two thirds of the way through, there is a big hiccup, due to an external network outages.  Lobster accepts the failures and keeps on going.

Lobster has been a team effort between Physics, Computer Science, and the Center for Research Computing: Anna Woodard and Matthias Wolf have taken the lead in developing the core software; Ben Tovar, Patrick Donnelly, and Peter Ivie have improved and debugged Work Queue, Parrot, and Chirp along the way; Charles Mueller, Nil Valls, Kenyi Anampa, and Paul Brenner have all worked to deploy the system at scale in production; Kevin Lannon, Michael Hildreth, and Douglas Thain provide the project leadership.

Anna Woodard, Matthias Wolf, Charles Nicholas Mueller, Ben Tovar, Patrick Donnelly, Kenyi Hurtado Anampa, Paul Brenner, Kevin Lannon, and Michael Hildreth, Exploiting Volatile Opportunistic Computing Resources with Lobster, Computing in High Energy Physics, January, 2015.

Anna Woodard, Matthias Wolf, Charles Mueller, Nil Valls, Ben Tovar, Patrick Donnelly, Peter Ivie, Kenyi Hurtado Anampa, Paul Brenner, Douglas Thain, Kevin Lannon and Michael Hildreth, Scaling Data Intensive Physics Applications to 10k Cores on Non-Dedicated Clusters with Lobster, IEEE Conference on Cluster Computing, September, 2015.

CMS Analysis on 10K Cores with Lobster

The CMS physics group at Notre Dame has created Lobster, a data analysis system that runs on O(10K) cores to process data produced by the CMS experiment at the LHC.  Lobster uses Work Queue to dispatch tasks to thousands of machines, Parrot with CVMFS to deliver the complex software stack from CERN, XRootD to deliver the LHC data, and Chirp and Hadoop to manage the output data. By using these technologies, Lobster is able to harness arbitrary machines and bring along the CMS computing environment wherever it goes.   At peak, Lobster at ND delivers capacity equal to that of a dedicated CMS Tier-2 facility!     (read more here)