22,203 research outputs found
Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling
Though the GPGPU concept is well-known
in image processing, much more work remains to be done
to fully exploit GPUs as an alternative computation
engine. This paper investigates the computation-to-core
mapping strategies to probe the efficiency and scalability
of the robust facet image modeling algorithm on GPUs.
Our fine-grained computation-to-core mapping scheme
shows a significant performance gain over the standard
pixel-wise mapping scheme. With in-depth performance
comparisons across the two different mapping schemes,
we analyze the impact of the level of parallelism on
the GPU computation and suggest two principles for
optimizing future image processing applications on the
GPU platform
One machine, one minute, three billion tetrahedra
This paper presents a new scalable parallelization scheme to generate the 3D
Delaunay triangulation of a given set of points. Our first contribution is an
efficient serial implementation of the incremental Delaunay insertion
algorithm. A simple dedicated data structure, an efficient sorting of the
points and the optimization of the insertion algorithm have permitted to
accelerate reference implementations by a factor three. Our second contribution
is a multi-threaded version of the Delaunay kernel that is able to concurrently
insert vertices. Moore curve coordinates are used to partition the point set,
avoiding heavy synchronization overheads. Conflicts are managed by modifying
the partitions with a simple rescaling of the space-filling curve. The
performances of our implementation have been measured on three different
processors, an Intel core-i7, an Intel Xeon Phi and an AMD EPYC, on which we
have been able to compute 3 billion tetrahedra in 53 seconds. This corresponds
to a generation rate of over 55 million tetrahedra per second. We finally show
how this very efficient parallel Delaunay triangulation can be integrated in a
Delaunay refinement mesh generator which takes as input the triangulated
surface boundary of the volume to mesh
pocl: A Performance-Portable OpenCL Implementation
OpenCL is a standard for parallel programming of heterogeneous systems. The
benefits of a common programming standard are clear; multiple vendors can
provide support for application descriptions written according to the standard,
thus reducing the program porting effort. While the standard brings the obvious
benefits of platform portability, the performance portability aspects are
largely left to the programmer. The situation is made worse due to multiple
proprietary vendor implementations with different characteristics, and, thus,
required optimization strategies.
In this paper, we propose an OpenCL implementation that is both portable and
performance portable. At its core is a kernel compiler that can be used to
exploit the data parallelism of OpenCL programs on multiple platforms with
different parallel hardware styles. The kernel compiler is modularized to
perform target-independent parallel region formation separately from the
target-specific parallel mapping of the regions to enable support for various
styles of fine-grained parallel resources such as subword SIMD extensions, SIMD
datapaths and static multi-issue. Unlike previous similar techniques that work
on the source level, the parallel region formation retains the information of
the data parallelism using the LLVM IR and its metadata infrastructure. This
data can be exploited by the later generic compiler passes for efficient
parallelization.
The proposed open source implementation of OpenCL is also platform portable,
enabling OpenCL on a wide range of architectures, both already commercialized
and on those that are still under research. The paper describes how the
portability of the implementation is achieved. Our results show that most of
the benchmarked applications when compiled using pocl were faster or close to
as fast as the best proprietary OpenCL implementation for the platform at hand.Comment: This article was published in 2015; it is now openly accessible via
arxi
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