Tag Archives: GPU

9-13 Jul 2012

  • (abs, pdf) Clementini et al., Variability and star formation in Leo T, the lowest luminosity star-forming galaxy known today
  • (abs, pdf) Peñarrubia et al., The coupling between the core/cusp and missing satellite problems
  • (abs, pdf) Schaerer et al., Properties of z~3-6 Lyman break galaxies. II. Testing star formation histories and the SFR-mass relation with ALMA and near-IR spectroscopy
  • (abs, pdf) Gerke et al., Improved Mock Galaxy Catalogs for the DEEP2 Galaxy Redshift Survey from Subhalo Abundance and Environment Matching
  • (abs, pdf) Maxwell et al., Building the Stellar Halo Through Feedback in Dwarf Galaxies
  • (abs, pdf) Brooks & Zolotov, Why Baryons Matter: The Kinematics of Dwarf Spheroidal Satellites
  • (abs, pdf) Umbreit & Rasio, Constraining Intermediate-Mass Black Holes in Globular Clusters
  • (abs, pdf) Schnittman et al., X-ray Spectra from MHD Simulations of Accreting Black Holes
  • (abs, pdf) Capuzzo-Dolcetta et al., A fully parallel, high precision, N-body code running on hybrid computing platforms

21 Dec 2011

  • (abs, pdf) Turk et al., Magnetic Fields in Population III Star Formation
  • (abs, pdf) Turk & Smith, High-Performance Astrophysical Simulations and Analysis with Python
  • (abs, pdf) Nakasato, Implementation of a Parallel Tree Method on a GPU
  • (abs, pdf) Whalen & Fryer, The Formation of Supermassive Black Holes from Low-Mass Pop III Seeds
  • (abs, pdf) Ciardi et al., The effect of intergalactic helium on hydrogen reionisation: implications for the sources of ionising photons at z > 6

30 Nov 2011

  • (abs, pdf) Haschke et al., Metallicity distribution functions of the old populations of the Magellanic Clouds from RR Lyrae stars
  • (abs, pdf) Hopkins et al., Realistic Stellar Feedback & Bulge Formation in Clumpy Disks
  • (abs, pdf) Salvadori & Ferrara, First stars in Damped Lyman Alpha systems
  • (abs, pdf) Hassan et al., Unleashing the Power of Distributed CPU/GPU Architectures: Massive Astronomical Data Analysis and Visualization case study
  • (abs, pdf) Yajima et al., Sub-millimeter brightness of early star-forming galaxies

GPU Meeting (04 Oct 2011)

Lionel London (CRA) – GPU Computing in Matlab

  • Matlab Parallel Computing Toolbox (PCT) vs. Jacket
  • PCT allows for multi-cpu and GPU computing. Limited to 12 cores on the local machine. Very high level.
  • GPU computing with PCT requires an nVidia card with v1.3 compute capabilities. GPGPU with Jacket has relaxed requirements but is very expensive ($4k for 5 licenses!)
  • PCT GPU can run external .cu files.
  • Jacket has 10x more CUDA-enabled functions than Matlab.  It’s cluster capable.