GPGPU |
General-Purpose Computation Using Graphics Hardware
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IntroductionGPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.
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Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests Abstract: "In this paper, we describe the architecture and performance
of the GraCCA system, a Graphic-Card Cluster for Astrophysics simulations.
It consists of 16 nodes, with each node equipped with 2 modern graphic cards,
the NVIDIA GeForce 8800 GTX. This computing cluster provides a theoretical
performance of 16.2 TFLOPS. To demonstrate its performance in astrophysics
computation, we have implemented a parallel direct N-body simulation program
with shared time-step algorithm in this system. Our system achieves a measured
performance of 7.1 TFLOPS and a parallel efficiency of 90% for simulating a
globular cluster of 1024K particles. In comparing with the GRAPE-6A cluster at
RIT (Rochester Institute of Technology), the GraCCA system achieves a more than
twice higher measured speed and an even higher performance-per-dollar ratio.
Moreover, our system can handle up to 320M particles and can serve as a
general-purpose computing cluster for a wide range of astrophysics problems.
(Hsi-Yu Schive, Chia-Hung Chien, Shing-Kwong Wong, Yu-Chih Tsai, Tzihong Chiueh.
Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests.
submitted to New Astronomy, 20 July, 2007.)
Posted: 27 Jul 2007 [GPGPU /Scientific Computing] # Abstract: "We present the results of gravitational direct N-body simulations using the Graphics Processing Unit (GPU)
on a commercial NVIDIA GeForce 8800GTX designed for gaming computers. The force evaluation of the N-body problem is
implemented in "Compute Unified Device Architecture" (CUDA) using the GPU to speed-up the calculations. We tested the
implementation on three different N-body codes: two direct N-body integration codes, using the 4th order
predictor-corrector Hermite integrator with block time-steps, and one Barnes-Hut treecode, which uses a 2nd order
leapfrog integration scheme. The integration of the equations of motions for all codes is performed on the host CPU.
We find that for N > 512 particles the GPU outperforms the GRAPE-6Af, if some softening in the force calculation is
accepted. Without softening and for very small integration time steps the GRAPE still outperforms the GPU. We conclude
that modern GPUs offer an attractive alternative to GRAPE-6Af special purpose hardware. Using the same time-step
criterion, the total energy of the N-body system was conserved better than to one in 10^6 on the GPU, only about an
order of magnitude worse than obtained with GRAPE-6Af. For N \apgt 10^5 the 8800GTX outperforms the host CPU by a factor
of about 100 and runs at about the same speed as the GRAPE-6Af."
(Robert G. Belleman, Jeroen Bedorf, Simon Portegies Zwart. High Performance
Direct Gravitational N-body Simulations on Graphics Processing Units -- II: An implementation in CUDA.
Accepted for publication in New Astronomy.)
Posted: 27 Jul 2007 [GPGPU /Scientific Computing] # |
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