14,074 research outputs found
A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters
In this work, we consider the solution of boundary integral equations by
means of a scalable hierarchical matrix approach on clusters equipped with
graphics hardware, i.e. graphics processing units (GPUs). To this end, we
extend our existing single-GPU hierarchical matrix library hmglib such that it
is able to scale on many GPUs and such that it can be coupled to arbitrary
application codes. Using a model GPU implementation of a boundary element
method (BEM) solver, we are able to achieve more than 67 percent relative
parallel speed-up going from 128 to 1024 GPUs for a model geometry test case
with 1.5 million unknowns and a real-world geometry test case with almost 1.2
million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6
minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the
setup phase and 20 seconds for the iterative solver. To the best of the
authors' knowledge, we here discuss the first fully GPU-based
distributed-memory parallel hierarchical matrix Open Source library using the
traditional H-matrix format and adaptive cross approximation with an
application to BEM problems
A minimalistic approach for fast computation of geodesic distances on triangular meshes
The computation of geodesic distances is an important research topic in
Geometry Processing and 3D Shape Analysis as it is a basic component of many
methods used in these areas. In this work, we present a minimalistic parallel
algorithm based on front propagation to compute approximate geodesic distances
on meshes. Our method is practical and simple to implement and does not require
any heavy pre-processing. The convergence of our algorithm depends on the
number of discrete level sets around the source points from which distance
information propagates. To appropriately implement our method on GPUs taking
into account memory coalescence problems, we take advantage of a graph
representation based on a breadth-first search traversal that works
harmoniously with our parallel front propagation approach. We report
experiments that show how our method scales with the size of the problem. We
compare the mean error and processing time obtained by our method with such
measures computed using other methods. Our method produces results in
competitive times with almost the same accuracy, especially for large meshes.
We also demonstrate its use for solving two classical geometry processing
problems: the regular sampling problem and the Voronoi tessellation on meshes.Comment: Preprint submitted to Computers & Graphic
Finite Element Integration on GPUs
We present a novel finite element integration method for low order elements
on GPUs. We achieve more than 100GF for element integration on first order
discretizations of both the Laplacian and Elasticity operators.Comment: 16 pages, 3 figure
GPU-driven recombination and transformation of YCoCg-R video samples
Common programmable Graphics Processing Units (GPU) are capable of more than just rendering real-time effects for games. They can also be used for image processing and the acceleration of video decoding. This paper describes an extended implementation of the H.264/AVC YCoCg-R to RGB color space transformation on the GPU. Both the color space transformation and recombination of the color samples from a nontrivial data layout are performed by the GPU. Using mid- to high-range GPUs, this extended implementation offers a significant gain in processing speed compared to an existing basic GPU version and an optimized CPU implementation. An ATI X1900 GPU was capable of processing more than 73 high-resolution 1080p YCoCg-R frames per second, which is over twice the speed of the CPU-only transformation using a Pentium D 820
OpenACC Based GPU Parallelization of Plane Sweep Algorithm for Geometric Intersection
Line segment intersection is one of the elementary operations in computational geometry. Complex problems in Geographic Information Systems (GIS) like finding map overlays or spatial joins using polygonal data require solving segment intersections. Plane sweep paradigm is used for finding geometric intersection in an efficient manner. However, it is difficult to parallelize due to its in-order processing of spatial events. We present a new fine-grained parallel algorithm for geometric intersection and its CPU and GPU implementation using OpenMP and OpenACC. To the best of our knowledge, this is the first work demonstrating an effective parallelization of plane sweep on GPUs.
We chose compiler directive based approach for implementation because of its simplicity to parallelize sequential code. Using Nvidia Tesla P100 GPU, our implementation achieves around 40X speedup for line segment intersection problem on 40K and 80K data sets compared to sequential CGAL library
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
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