7,426 research outputs found
A pilgrimage to gravity on GPUs
In this short review we present the developments over the last 5 decades that
have led to the use of Graphics Processing Units (GPUs) for astrophysical
simulations. Since the introduction of NVIDIA's Compute Unified Device
Architecture (CUDA) in 2007 the GPU has become a valuable tool for N-body
simulations and is so popular these days that almost all papers about high
precision N-body simulations use methods that are accelerated by GPUs. With the
GPU hardware becoming more advanced and being used for more advanced algorithms
like gravitational tree-codes we see a bright future for GPU like hardware in
computational astrophysics.Comment: To appear in: European Physical Journal "Special Topics" : "Computer
Simulations on Graphics Processing Units" . 18 pages, 8 figure
High Performance Direct Gravitational N-body Simulations on Graphics Processing Units
We present the results of gravitational direct -body simulations using the
commercial graphics processing units (GPU) NVIDIA Quadro FX1400 and GeForce
8800GTX, and compare the results with GRAPE-6Af special purpose hardware. The
force evaluation of the -body problem was implemented in Cg using the GPU
directly to speed-up the calculations. The integration of the equations of
motions were, running on the host computer, implemented in C using the 4th
order predictor-corrector Hermite integrator with block time steps. We find
that for a large number of particles (N \apgt 10^4) modern graphics
processing units offer an attractive low cost alternative to GRAPE special
purpose hardware. A modern GPU continues to give a relatively flat scaling with
the number of particles, comparable to that of the GRAPE. Using the same time
step criterion the total energy of the -body system was conserved better
than to one in on the GPU, which is only about an order of magnitude
worse than obtained with GRAPE. For N\apgt 10^6 the GeForce 8800GTX was about
20 times faster than the host computer. Though still about an order of
magnitude slower than GRAPE, modern GPU's outperform GRAPE in their low cost,
long mean time between failure and the much larger onboard memory; the
GRAPE-6Af holds at most 256k particles whereas the GeForce 8800GTF can hold 9
million particles in memory.Comment: Submitted to New Astronom
Harvesting graphics power for MD simulations
We discuss an implementation of molecular dynamics (MD) simulations on a
graphic processing unit (GPU) in the NVIDIA CUDA language. We tested our code
on a modern GPU, the NVIDIA GeForce 8800 GTX. Results for two MD algorithms
suitable for short-ranged and long-ranged interactions, and a congruential
shift random number generator are presented. The performance of the GPU's is
compared to their main processor counterpart. We achieve speedups of up to 80,
40 and 150 fold, respectively. With newest generation of GPU's one can run
standard MD simulations at 10^7 flops/$.Comment: 12 pages, 5 figures. Submitted to Mol. Si
Scalable Interactive Volume Rendering Using Off-the-shelf Components
This paper describes an application of a second generation implementation of the Sepia architecture (Sepia-2) to interactive volu-metric visualization of large rectilinear scalar fields. By employingpipelined associative blending operators in a sort-last configuration a demonstration system with 8 rendering computers sustains 24 to 28 frames per second while interactively rendering large data volumes (1024x256x256 voxels, and 512x512x512 voxels). We believe interactive performance at these frame rates and data sizes is unprecedented. We also believe these results can be extended to other types of structured and unstructured grids and a variety of GL rendering techniques including surface rendering and shadow map-ping. We show how to extend our single-stage crossbar demonstration system to multi-stage networks in order to support much larger data sizes and higher image resolutions. This requires solving a dynamic mapping problem for a class of blending operators that includes Porter-Duff compositing operators
A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units
Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations
High Performance Direct Gravitational N-body Simulations on Graphics Processing Units -- II: An implementation in CUDA
We present the results of gravitational direct -body simulations using the
Graphics Processing Unit (GPU) on a commercial NVIDIA GeForce 8800GTX designed
for gaming computers. The force evaluation of the -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
-body codes: two direct -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 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
-body system was conserved better than to one in 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.Comment: Accepted for publication in New Astronom
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
Achieving High Speed CFD simulations: Optimization, Parallelization, and FPGA Acceleration for the unstructured DLR TAU Code
Today, large scale parallel simulations are fundamental tools to handle complex problems. The number of processors in current computation platforms has been recently increased and therefore it is necessary to optimize the application performance and to enhance the scalability of massively-parallel systems. In addition, new heterogeneous architectures, combining conventional processors with specific hardware, like FPGAs, to accelerate the most time consuming functions are considered as a strong alternative to boost the performance.
In this paper, the performance of the DLR TAU code is analyzed and optimized. The improvement of the code efficiency is addressed through three key activities: Optimization, parallelization and hardware acceleration. At first, a profiling analysis of the most time-consuming processes of the Reynolds Averaged Navier Stokes flow solver on a three-dimensional unstructured mesh is performed. Then, a study of the code scalability with new partitioning algorithms are tested to show the most suitable partitioning algorithms for the selected applications. Finally, a feasibility study on the application of FPGAs and GPUs for the hardware acceleration of CFD simulations is presented
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