Evolving N-Body Simulations to Determine the Origin and Structure of the Milky Way Galaxy's Halo Using Volunteer Computing

This work describes research done by the MilkyWay@Home project to use N-Body simulations to model the formation of the Milky Way Galaxy's halo. While there have been previous efforts to use N-Body simulations to perform astronomical modeling, to our knowledge this is the first to use evolutionary algorithms to discover the initial parameters to the N-Body simulations so that they accurately model astronomical data. Performing a single 32,000 body simulation can take up to 200 hours on a typical processor, with an average of 15 hours. As optimizing the input parameters to these N-Body simulations typically takes at least 30,000 or more simulations, this work is made possible by utilizing the computing power of the 35,000 volunteered hosts at the MilkyWay@Home project, which are currently providing around 800 teraFLOPS. This work also describes improvements to an open-source framework for generic distributed optimization (FGDO), which provide more efficient validation in performing these evolutionary algorithms in conjunction the Berkeley Open Infrastructure for Network Computing (BOINC).

[1]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[2]  Andrew Lewis,et al.  An evolutionary programming algorithm for multi-objective optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[3]  Byung-Il Koh,et al.  Parallel asynchronous particle swarm optimization , 2006, International journal for numerical methods in engineering.

[4]  Travis Desell,et al.  Asynchronous global optimization for massive-scale computing , 2009 .

[5]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[6]  J. Berntsson,et al.  A convergence model for asynchronous parallel genetic algorithms , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[7]  B J Fregly,et al.  Parallel global optimization with the particle swarm algorithm , 2004, International journal for numerical methods in engineering.

[8]  S. White,et al.  A Universal Density Profile from Hierarchical Clustering , 1996, astro-ph/9611107.

[9]  Carlos A. Varela,et al.  Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails , 2008, 0805.2121.

[10]  Fengming Zhang,et al.  Parallel Particle Swarm Optimization for Attribute Reduction , 2007 .

[11]  Jaroslaw Sobieszczanski-Sobieski,et al.  A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations , 2005 .

[12]  K. Abazajian,et al.  THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY , 2008, 0812.0649.

[13]  Giandomenico Spezzano,et al.  A Jxta Based Asynchronous Peer-to-Peer Implementation of Genetic Programming , 2006, J. Softw..

[14]  E. K. Grebel,et al.  Modeling the Disruption of the Globular Cluster Palomar 5 by Galactic Tides , 2004 .

[15]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[16]  Benjamin A. Willett,et al.  AN ORBIT FIT FOR THE GRILLMAIR DIONATOS COLD STELLAR STREAM , 2009, 0901.4046.

[17]  E. McIntosh,et al.  MASSIVE TRACKING ON HETEROGENEOUS PLATFORMS , 2006 .

[18]  David N. Spergel,et al.  The Disruption of the Sagittarius Dwarf Galaxy. , 1995 .

[19]  Ponnuthurai N. Suganthan,et al.  Concurrent PSO and FDR-PSO based reconfigurable phase-differentiated antenna array design , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[20]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[21]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[22]  C. Grillmair.  Detection of a New, 60 Degree-Long Dwarf Galaxy Debris Stream , 2006 .

[23]  Cambridge,et al.  CONSTRAINING THE MILKY WAY POTENTIAL WITH A SIX-DIMENSIONAL PHASE-SPACE MAP OF THE GD-1 STELLAR STREAM , 2009, 0907.1085.

[24]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..

[25]  Boleslaw K. Szymanski,et al.  An asynchronous hybrid genetic-simplex search for modeling the Milky Way galaxy using volunteer computing , 2008, GECCO '08.

[26]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[27]  L. Hernquist,et al.  Performance characteristics of tree codes , 1987 .

[28]  Fnal,et al.  The Field of Streams: Sagittarius and its Siblings , 2006, astro-ph/0605025.

[29]  David P. Anderson,et al.  Validating evolutionary algorithms on volunteer computing grids , 2010, DAIS'10.

[30]  S. Majewski,et al.  A Two Micron All-Sky Survey View of the Sagittarius Dwarf Galaxy. IV. Modeling the Sagittarius Tidal Tails , 2004, astro-ph/0407566.

[31]  Isao Ono,et al.  A grid-oriented genetic algorithm framework for bioinformatics , 2009, New Generation Computing.

[32]  Carlos A. Coello Coello,et al.  A comparative study of differential evolution variants for global optimization , 2006, GECCO.

[33]  D. Kaltchev,et al.  LARGE SCALE BEAM-BEAM SIMULATIONS FOR THE CERN LHC USING DISTRIBUTED COMPUTING , 2006 .

[34]  Andrew Lewis,et al.  Model Optimization and Parameter Estimation with Nimrod/O , 2006, International Conference on Computational Science.

[35]  Boleslaw K. Szymanski,et al.  The Effects of Heterogeneity on Asynchronous Panmictic Genetic Search , 2007, PPAM.

[36]  David P. Anderson.  Volunteer computing , 2010, CROS.

[37]  S. Majewski,et al.  THE SAGITTARIUS DWARF GALAXY: A MODEL FOR EVOLUTION IN A TRIAXIAL MILKY WAY HALO , 2010, 1003.1132.

[38]  Boleslaw K. Szymanski,et al.  Asynchronous genetic search for scientific modeling on large-scale heterogeneous environments , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[39]  David P. Anderson,et al.  High-performance task distribution for volunteer computing , 2005, First International Conference on e-Science and Grid Computing (e-Science'05).

[40]  Michael R. Shirts,et al.  Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. , 2003, Biopolymers.

[41]  H. Plummer.  On the Problem of Distribution in Globular Star Clusters: (Plate 8.) , 1911 .

[42]  Márk Jelasity,et al.  Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms , 2009, EvoWorkshops.

[43]  M. Miyamoto,et al.  Three-Dimensional Models for the Distribution of Mass in Galaxies , 1975 .

[44]  Jose Basterrechea,et al.  Particle swarms applied to array synthesis and planar near‐field antenna measurements , 2008 .

[45]  David P. Anderson,et al.  Homogeneous redundancy: a technique to ensure integrity of molecular simulation results using public computing , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[46]  DETECTION OF A 63 COLD STELLAR STREAM IN THE SLOAN DIGITAL SKY SURVEY , 2006, astro-ph/0604332.

[47]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[48]  Benjamin A. Willett,et al.  THE ORBIT OF THE ORPHAN STREAM , 2010, 1001.0576.

[49]  Enrique Alba,et al.  Analyzing synchronous and asynchronous parallel distributed genetic algorithms , 2001, Future Gener. Comput. Syst..

[50]  H. Rix,et al.  The Milky Way’s Circular Velocity Curve to 60 kpc and an Estimate of the Dark Matter Halo Mass from the Kinematics of ~2400 SDSS Blue Horizontal-Branch Stars , 2008, 0801.1232.

[51]  John Dubinski.  A parallel tree code , 1996 .

[52]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.