3,336 research outputs found
GAMER: a GPU-Accelerated Adaptive Mesh Refinement Code for Astrophysics
We present the newly developed code, GAMER (GPU-accelerated Adaptive MEsh
Refinement code), which has adopted a novel approach to improve the performance
of adaptive mesh refinement (AMR) astrophysical simulations by a large factor
with the use of the graphic processing unit (GPU). The AMR implementation is
based on a hierarchy of grid patches with an oct-tree data structure. We adopt
a three-dimensional relaxing TVD scheme for the hydrodynamic solver, and a
multi-level relaxation scheme for the Poisson solver. Both solvers have been
implemented in GPU, by which hundreds of patches can be advanced in parallel.
The computational overhead associated with the data transfer between CPU and
GPU is carefully reduced by utilizing the capability of asynchronous memory
copies in GPU, and the computing time of the ghost-zone values for each patch
is made to diminish by overlapping it with the GPU computations. We demonstrate
the accuracy of the code by performing several standard test problems in
astrophysics. GAMER is a parallel code that can be run in a multi-GPU cluster
system. We measure the performance of the code by performing purely-baryonic
cosmological simulations in different hardware implementations, in which
detailed timing analyses provide comparison between the computations with and
without GPU(s) acceleration. Maximum speed-up factors of 12.19 and 10.47 are
demonstrated using 1 GPU with 4096^3 effective resolution and 16 GPUs with
8192^3 effective resolution, respectively.Comment: 60 pages, 22 figures, 3 tables. More accuracy tests are included.
Accepted for publication in ApJ
Acceleration of Coarse Grain Molecular Dynamics on GPU Architectures
Coarse grain (CG) molecular models have been proposed to simulate complex sys- tems with lower computational overheads and longer timescales with respect to atom- istic level models. However, their acceleration on parallel architectures such as Graphic Processing Units (GPU) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU-accelerated version of a CG molecular dynamics simulator, to which we applied specic optimizations for CG models, such as dedicated data structures to handle dierent bead type interac- tions, obtaining a maximum speed-up of 14 on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed-up and accuracy of results, using three dierent GPU architectures as case studie
Linear solvers for power grid optimization problems: a review of GPU-accelerated linear solvers
The linear equations that arise in interior methods for constrained
optimization are sparse symmetric indefinite and become extremely
ill-conditioned as the interior method converges. These linear systems present
a challenge for existing solver frameworks based on sparse LU or LDL^T
decompositions. We benchmark five well known direct linear solver packages
using matrices extracted from power grid optimization problems. The achieved
solution accuracy varies greatly among the packages. None of the tested
packages delivers significant GPU acceleration for our test cases
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