and Volunteer Computing at RPI Travis Desell RCOS, April 23, 2010

& Open Source Software Milky Way Modeling Application Multi-Platform Development: ATI, CUDA, OpenCL, Windows, OS X, Linux Generic Optimization Code (in the works) BOINC - Berkeley Open Infrastructure for Network Computing

Statistics ~20,000 active users ~30,000 active hosts ~1.6 petaflops: most powerful BOINC project 3rd most powerful computing system (behind and the fastest supercomputer) most of this from GPU computing

Courtesy of

GPU Application First GPU implementation was user- contributed Compared to 3.0Ghz AMD Phenom(tm) II X4 940: ATI HD5870 GPU - 109x speedup NVidia GeForce GTX 285 GPU - 17x speedup Requires double-precision calculations: NVidia GPUs have less double precision real estate Application would be 6.2x faster on the ATI GPU, 7.8x faster on the NVidia GPU using single-precision math Travis Desell, Anthony Waters, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Matthew Newby, Heidi Newberg, Andreas Przystawik and Dave Anderson. Accelerating the volunteer computing project with GPUs. In the 8th International Conference on Parallel Processing and Applied Mathematics (PPAM 2009), Wroclaw, Poland, September 2009.Accelerating the volunteer computing project with GPUs

The Sagittarius Dwarf Tidal Stream Image (above): [Ibata et al. 1997, AJ] Image (below): David Martinez-Delgado (MPIA) & Gabriel Perez (IAC) The Sagittarius Dwarf Galaxy is merging with the Milky Way The dwarf is being tidally disrupted by the Milky Way, creating long “tails.” Provide information on matter distribution in Milky Way Provide constraints on Galactic Halo Mapping the Tidal Stream will:

Image: sdss.org 230+ million objects 8,400 square degrees in the sky Large percentage of north galactic cap Very little data in galactic plane (too much dust) Several hundred thousand stars SLOAN Digital Sky Survey

The Milky Way Halo Bulge Thin Disk Thick Disk ~30 kiloparsecs (100,000 light-years) Sun Sagittarius Dwarf Galaxy Tidal Stream Data Wedge Image: Matthew Newby

Sagittarius Stream Model Assume stream is a cylinder Radial drop-off given by a Gaussian Distribution 2 background parameters (new model has 4): r0, q 6 parameters per stream: ε, μ, r, θ, φ, σ A single stream with the old model has an 8 dimensional search space Often fit multiple streams for search spaces with more than 20 dimensions! Background distribution:

BOINC Active development mailing lists: Client Development: Supporting different architectures (GPUs) Awarding fair “credit” for work done Server Development: Scheduling Validation & Verification

Find protein binding sites using Gibbs sampling Use random walks (Markov chains) which result in sites distributed according to their actual probability of being the correct binding site Initial sequences: Mycobacterium tuberculosis Yersinia pestis (cause of the Bubonic plague)

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