4,626 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
Accelerating incoherent dedispersion
Incoherent dedispersion is a computationally intensive problem that appears
frequently in pulsar and transient astronomy. For current and future transient
pipelines, dedispersion can dominate the total execution time, meaning its
computational speed acts as a constraint on the quality and quantity of science
results. It is thus critical that the algorithm be able to take advantage of
trends in commodity computing hardware. With this goal in mind, we present
analysis of the 'direct', 'tree' and 'sub-band' dedispersion algorithms with
respect to their potential for efficient execution on modern graphics
processing units (GPUs). We find all three to be excellent candidates, and
proceed to describe implementations in C for CUDA using insight gained from the
analysis. Using recent CPU and GPU hardware, the transition to the GPU provides
a speed-up of 9x for the direct algorithm when compared to an optimised
quad-core CPU code. For realistic recent survey parameters, these speeds are
high enough that further optimisation is unnecessary to achieve real-time
processing. Where further speed-ups are desirable, we find that the tree and
sub-band algorithms are able to provide 3-7x better performance at the cost of
certain smearing, memory consumption and development time trade-offs. We finish
with a discussion of the implications of these results for future transient
surveys. Our GPU dedispersion code is publicly available as a C library at:
http://dedisp.googlecode.com/Comment: 15 pages, 4 figures, 2 tables, accepted for publication in MNRA
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Practical Gauss-Newton Optimisation for Deep Learning
We present an efficient block-diagonal ap- proximation to the Gauss-Newton
matrix for feedforward neural networks. Our result- ing algorithm is
competitive against state- of-the-art first order optimisation methods, with
sometimes significant improvement in optimisation performance. Unlike
first-order methods, for which hyperparameter tuning of the optimisation
parameters is often a labo- rious process, our approach can provide good
performance even when used with default set- tings. A side result of our work
is that for piecewise linear transfer functions, the net- work objective
function can have no differ- entiable local maxima, which may partially explain
why such transfer functions facilitate effective optimisation.Comment: ICML 201
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