40,585 research outputs found
GPU in Physics Computation: Case Geant4 Navigation
General purpose computing on graphic processing units (GPU) is a potential
method of speeding up scientific computation with low cost and high energy
efficiency. We experimented with the particle physics simulation toolkit Geant4
used at CERN to benchmark its geometry navigation functionality on a GPU. The
goal was to find out whether Geant4 physics simulations could benefit from GPU
acceleration and how difficult it is to modify Geant4 code to run in a GPU.
We ported selected parts of Geant4 code to C99 & CUDA and implemented a
simple gamma physics simulation utilizing this code to measure efficiency. The
performance of the program was tested by running it on two different platforms:
NVIDIA GeForce 470 GTX GPU and a 12-core AMD CPU system. Our conclusion was
that GPUs can be a competitive alternate for multi-core computers but porting
existing software in an efficient way is challenging
Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units
Basic uniform pseudo-random number generators are implemented on ATI Graphics
Processing Units (GPU). The performance results of the realized generators
(multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR,
RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The
obtained speed-up factor is hundreds of times in comparison with CPU. RANLUX
generator is found to be the most appropriate for using on GPU in Monte Carlo
simulations. The brief review of the pseudo-random number generators used in
modern software packages for Monte Carlo simulations in high-energy physics is
present.Comment: 31 pages, 9 figures, 3 table
Strong scaling of general-purpose molecular dynamics simulations on GPUs
We describe a highly optimized implementation of MPI domain decomposition in
a GPU-enabled, general-purpose molecular dynamics code, HOOMD-blue (Anderson
and Glotzer, arXiv:1308.5587). Our approach is inspired by a traditional
CPU-based code, LAMMPS (Plimpton, J. Comp. Phys. 117, 1995), but is implemented
within a code that was designed for execution on GPUs from the start (Anderson
et al., J. Comp. Phys. 227, 2008). The software supports short-ranged pair
force and bond force fields and achieves optimal GPU performance using an
autotuning algorithm. We are able to demonstrate equivalent or superior scaling
on up to 3,375 GPUs in Lennard-Jones and dissipative particle dynamics (DPD)
simulations of up to 108 million particles. GPUDirect RDMA capabilities in
recent GPU generations provide better performance in full double precision
calculations. For a representative polymer physics application, HOOMD-blue 1.0
provides an effective GPU vs. CPU node speed-up of 12.5x.Comment: 30 pages, 14 figure
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