61,930 research outputs found
Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability
Heterogeneous computing, which combines devices with different architectures,
is rising in popularity, and promises increased performance combined with
reduced energy consumption. OpenCL has been proposed as a standard for
programing such systems, and offers functional portability. It does, however,
suffer from poor performance portability, code tuned for one device must be
re-tuned to achieve good performance on another device. In this paper, we use
machine learning-based auto-tuning to address this problem. Benchmarks are run
on a random subset of the entire tuning parameter configuration space, and the
results are used to build an artificial neural network based model. The model
can then be used to find interesting parts of the parameter space for further
search. We evaluate our method with different benchmarks, on several devices,
including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970
GPU. Our model achieves a mean relative error as low as 6.1%, and is able to
find configurations as little as 1.3% worse than the global minimum.Comment: This is a pre-print version an article to be published in the
Proceedings of the 2015 IEEE International Parallel and Distributed
Processing Symposium Workshops (IPDPSW). For personal use onl
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
An ever increasing number of configuration parameters are provided to system
users. But many users have used one configuration setting across different
workloads, leaving untapped the performance potential of systems. A good
configuration setting can greatly improve the performance of a deployed system
under certain workloads. But with tens or hundreds of parameters, it becomes a
highly costly task to decide which configuration setting leads to the best
performance. While such task requires the strong expertise in both the system
and the application, users commonly lack such expertise.
To help users tap the performance potential of systems, we present
BestConfig, a system for automatically finding a best configuration setting
within a resource limit for a deployed system under a given application
workload. BestConfig is designed with an extensible architecture to automate
the configuration tuning for general systems. To tune system configurations
within a resource limit, we propose the divide-and-diverge sampling method and
the recursive bound-and-search algorithm. BestConfig can improve the throughput
of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce
the running time of Hive join job by about 50% and that of Spark join job by
about 80%, solely by configuration adjustment
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
To support the variety of Big Data use cases, many Big Data related systems
expose a large number of user-specifiable configuration parameters. Highlighted
in our experiments, a MySQL deployment with well-tuned configuration parameters
achieves a peak throughput as 12 times much as one with the default setting.
However, finding the best setting for the tens or hundreds of configuration
parameters is mission impossible for ordinary users. Worse still, many Big Data
applications require the support of multiple systems co-deployed in the same
cluster. As these co-deployed systems can interact to affect the overall
performance, they must be tuned together. Automatic configuration tuning with
scalability guarantees (ACTS) is in need to help system users. Solutions to
ACTS must scale to various systems, workloads, deployments, parameters and
resource limits. Proposing and implementing an ACTS solution, we demonstrate
that ACTS can benefit users not only in improving system performance and
resource utilization, but also in saving costs and enabling fairer
benchmarking
Is One Hyperparameter Optimizer Enough?
Hyperparameter tuning is the black art of automatically finding a good
combination of control parameters for a data miner. While widely applied in
empirical Software Engineering, there has not been much discussion on which
hyperparameter tuner is best for software analytics. To address this gap in the
literature, this paper applied a range of hyperparameter optimizers (grid
search, random search, differential evolution, and Bayesian optimization) to
defect prediction problem. Surprisingly, no hyperparameter optimizer was
observed to be `best' and, for one of the two evaluation measures studied here
(F-measure), hyperparameter optimization, in 50\% cases, was no better than
using default configurations.
We conclude that hyperparameter optimization is more nuanced than previously
believed. While such optimization can certainly lead to large improvements in
the performance of classifiers used in software analytics, it remains to be
seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of
parameters, making them difficult to be deployed and updated on devices with
limited resources such as mobile phones and other smart embedded systems. In
this paper, we propose a scalable representation of the network parameters, so
that different applications can select the most suitable bit rate of the
network based on their own storage constraints. Moreover, when a device needs
to upgrade to a high-rate network, the existing low-rate network can be reused,
and only some incremental data are needed to be downloaded. We first
hierarchically quantize the weights of a pre-trained deep neural network to
enforce weight sharing. Next, we adaptively select the bits assigned to each
layer given the total bit budget. After that, we retrain the network to
fine-tune the quantized centroids. Experimental results show that our method
can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201
Automatic LQR Tuning Based on Gaussian Process Global Optimization
This paper proposes an automatic controller tuning framework based on linear
optimal control combined with Bayesian optimization. With this framework, an
initial set of controller gains is automatically improved according to a
pre-defined performance objective evaluated from experimental data. The
underlying Bayesian optimization algorithm is Entropy Search, which represents
the latent objective as a Gaussian process and constructs an explicit belief
over the location of the objective minimum. This is used to maximize the
information gain from each experimental evaluation. Thus, this framework shall
yield improved controllers with fewer evaluations compared to alternative
approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is
used as the experimental demonstrator. Results of a two- and four-dimensional
tuning problems highlight the method's potential for automatic controller
tuning on robotic platforms.Comment: 8 pages, 5 figures, to appear in IEEE 2016 International Conference
on Robotics and Automation. Video demonstration of the experiments available
at https://am.is.tuebingen.mpg.de/publications/marco_icra_201
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training
For a data holder, such as a hospital or a government entity, who has a
privately held collection of personal data, in which the revealing and/or
processing of the personal identifiable data is restricted and prohibited by
law. Then, "how can we ensure the data holder does conceal the identity of each
individual in the imagery of personal data while still preserving certain
useful aspects of the data after de-identification?" becomes a challenge issue.
In this work, we propose an approach towards high-resolution facial image
de-identification, called k-Same-Siamese-GAN, which leverages the
k-Same-Anonymity mechanism, the Generative Adversarial Network, and the
hyperparameter tuning methods. Moreover, to speed up model training and reduce
memory consumption, the mixed precision training technique is also applied to
make kSS-GAN provide guarantees regarding privacy protection on close-form
identities and be trained much more efficiently as well. Finally, to validate
its applicability, the proposed work has been applied to actual datasets - RafD
and CelebA for performance testing. Besides protecting privacy of
high-resolution facial images, the proposed system is also justified for its
ability in automating parameter tuning and breaking through the limitation of
the number of adjustable parameters
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