UAB Condor Pilot
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=== Condor Testbeds ===
=== Condor Testbeds ===
=== Autodock Workflow ===
=== Autodock Workflow ===
Revision as of 06:16, 1 June 2012
The UAB Condor Pilot explored the utility to research applications of aggregated unused compute cycles harvested from many computers. The pilot established a demonstration spare-cycle compute fabric using the Condor scheduler and compared the performance of a molecular docking workflow on this fabric and several larger production Condor fabrics to the performance of the same workflow running on our campus compute cluster Cheaha.
The UAB Condor Pilot successfully demonstrated the value of harvested unused compute cycles to the molecular docking workflow. The results suggest that similar applications, especially those that can scale by repeating the same task on distinct data sets, will likewise benefit from the abundant compute resources that can be harvested via a Condor compute fabric. The UAB Condor Pilot ended in May 2012 with the presentation of our results (pdf) at Condor Week 2012.
Condor is a resource allocation and management system designed to simplify harvesting idle compute cycles from under-utilized computers. Condor is a production-quality software system developed by researchers at the University of Wisconsin. It is deployed in a wide range of environments from lab or departmental compute pools with 10's of processors to global compute fabrics such as the Open Science Grid (OSG) harnessing between 80,000 and 100,000 processors.
There is an active user and developer community around Condor. Condor is supported on Linux, Mac and Windows ensuring utility to a broad spectrum of user communities, from scientific computations to large scale statistical analyses. There are personal instances to support individuals migrating or developing their own workflows. The loosely-coupled nature of the resource collections also makes it straight forward to dynamically scale out on popular cloud computing fabrics such as Amazon's EC2 fabric.
Molecular docking is a process for discovering an ideal orientation between two molecules, the receptor and the ligand. There are a number of approaches which can be taken to explore how ligand (drug) can bind to a receptor (protein). The approach used in this pilot was a conformational space search using genetic algorithms which evolve the orientation of the molecules to find the most likely orientation for docking to occur, as implemented by the AutoDock application from The Scripps Research Institute.
This virtual screening of protein-drug interactions is computationally intense and can incorporate large databases of chemical compounds. This makes it an ideal candidate for finding as many compute resources as possible to leverage during the screening process. The structure of this workflow using AutoDock analyzes each receptor-ligand pair can independently, making it an ideal candidate for leveraging the loosely couple collection of computers made available through the Condor scheduler.
Molecular docking is the computational part of a much larger workflow that involves discovery of the protein structure via X-ray crystallography. This process is nicely described in this video describing X-ray crystallography at the Institute of Molecular and Cell Biology in Strasbourg, France. Similar facilities at Argonne National Laboratory are used by researchers at UAB's Center for Biophysical Sciences and Engineering to explore the structure of proteins.
Components of Pilot
The pilot leveraged three representative Condor implementations to assess the performance of the molecular docking workflow. The first testbed was a pilot UAB campus Condor pool established as part of the pilot itself. This pool included approximately 40 64bit Linux workstations from labs in the Department of Computer and Information Sciences and 40 32bit Windows desktops from UAB IT Desktop Support. These systems are representative of the type of computers available on campus that typically have long idle periods and which could potentially offer those cycles to compute tasks which need them.
The second testbed was the University of Wisconsin campus Condor pool. This is a production Condor pool that has over 1000 64bit Linux workstations. This pool is operated by the Center for High-Throughput Computing (CHTC) at the University of Wisconsin and is part of the production compute fabric available to researchers at the University of Wisconsin. It was made available to us as part of our pilot through the generous support of CHTC and Dr. Miron Livny.
The third testbed was the dynamically provisioned Condor pool made available by the Engage VO (Virtual Organization) which leverages compute cycles provided by the Open Science Grid (OSG). The Engage VO was established to encourage exploration of OSG by new users. It operates a dynamically provisioned Condor pool using a technology called glidin-WMS to affiliate available compute cycles in OSG with a virtual Condor pool. This is the essence of the on-demand resource allocation of cloud computing. The Engage VO is operated by RENCI (RENaissance Computing Institute) a multi-institutional research resource for North Carolina. Engage VO access and support was provided by John McGee, PI of the grant that established the Engage VO.