2,185 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
LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing
LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft
Enabling electronic prognostics using thermal data
Prognostics is a process of assessing the extent of deviation or degradation
of a product from its expected normal operating condition, and then, based on
continuous monitoring, predicting the future reliability of the product. By
being able to determine when a product will fail, procedures can be developed
to provide advanced warning of failures, optimize maintenance, reduce life
cycle costs, and improve the design, qualification and logistical support of
fielded and future systems. In the case of electronics, the reliability is
often influenced by thermal loads, in the form of steady-state temperatures,
power cycles, temperature gradients, ramp rates, and dwell times. If one can
continuously monitor the thermal loads, in-situ, this data can be used in
conjunction with precursor reasoning algorithms and stress-and-damage models to
enable prognostics. This paper discusses approaches to enable electronic
prognostics and provides a case study of prognostics using thermal data.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
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