390 research outputs found
Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures
For reasons of both performance and energy efficiency, high-performance
computing (HPC) hardware is becoming increasingly heterogeneous. The OpenCL
framework supports portable programming across a wide range of computing
devices and is gaining influence in programming next-generation accelerators.
To characterize the performance of these devices across a range of applications
requires a diverse, portable and configurable benchmark suite, and OpenCL is an
attractive programming model for this purpose. We present an extended and
enhanced version of the OpenDwarfs OpenCL benchmark suite, with a strong focus
placed on the robustness of applications, curation of additional benchmarks
with an increased emphasis on correctness of results and choice of problem
size. Preliminary results and analysis are reported for eight benchmark codes
on a diverse set of architectures -- three Intel CPUs, five Nvidia GPUs, six
AMD GPUs and a Xeon Phi.Comment: 10 pages, 5 figure
Design and evaluation of a cloud native data analysis pipeline for cyber physical production systems
Since 1991 with the birth of the World Wide Web the rate of data growth has been growing with a record level in the last couple of years. Big companies
tackled down this data growth with expensive and enormous data centres to process and get value of this data. From social media, Internet of Things (IoT), new business process, monitoring and multimedia, the capacities of
those data centres started to be a problem and required continuos and expensive expansion. Thus, Big Data was something that only a few were able to access. This changed fast when Amazon launched Amazon Web Services (AWS) around 15 years ago and gave the origins to the public cloud.
At that time, the capabilities were still very new and reduced but 10 years later the cloud was a whole new business that changed for ever the Big Data business. This not only commoditised computer power but it was
accompanied by a price model that let medium and small players the possibility to access it. In consequence, new problems arised regarding the nature of these distributed systems and the software architectures required
for proper data processing. The present job analyse the type of typical Big Data workloads and propose an architecture for a cloud native data analysis
pipeline. Lastly, it provides a chapter for tools and services that can be used in the architecture taking advantage of their open source nature and the cloud
price models.Fil: Ferrer Daub, Facundo Javier. Universidad Católica de Córdoba. Instituto de Ciencias de la Administración; Argentin
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