GPU computing and Many Integrated Core Computing

For the next decade, Moore's Law is still going to bring higher transistor densities allowing Billions of transistors to be integrated on a single chip. However, it becomes obvious that exploiting significant amounts of instruction-level parallelism with deeper pipelines and more aggressive wide-issue superscalar techniques, and using most of the transistor budget for large on-chip caches has come to a dead end. Especially, scaling performance with higher clock frequencies is getting more and more difficult because of heat dissipation problems and too high energy consumption. The latter is not only a technical problem for mobile systems, but is even going to become a severe problem for computing centers because high energy consumption leads to significant cost factors in the budget. For the moment, improving performance can only be achieved by exploiting parallelism on all system levels. Multicore architectures like Graphics Processing Unit (GPU) offer a better performance/Watt ratio than single core architectures with similar performance. Combining multicore and coprocessor technology promises extreme computing power for highly CPU-time-consuming applications like in image processing. The Special Session on GPU Computing and Many Integrated Core Computing aims at providing a forum for scientific researchers and engineers on hot topics related to GPU computing and hybrid computing with special emphasis on applications, performance analysis, programming models and mechanisms for mapping codes.

Topics of interest include, but are not limited to:

  • GPU computing, multi GPU processing, hybrid computing
  • Programming models, programming frameworks, CUDA, OpenCL, communication libraries
  • Mechanisms for mapping codes
  • Task allocation
  • Fault tolerance
  • Performance analysis
  • Many Integrated Core architecture, MIC
  • Intel coprocessor, Xeon Phi
  • Vectorization
  • Applications: image processing, signal processing, linear algebra, numerical simulation, optimisation
  • Domains: computer science, electronic, embedded systems, telecommunication, medical imaging, finance

Programme Committee:

  • Grey Ballard, UC Berkeley, USA
  • Daniel Becker, Siemens AG, Germany
  • Matthias Birk, Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Germany
  • Vincent Boyer, University of Nuevo Leon, Mexico
  • David Defour, France
  • Dominik Goddeke, TU Dortmund, Germany
  • Antonio Gomez Iglesias, CSIRO, Australia
  • Fumihiko Ino, Osaka University, Japan
  • Volodymyr Kindratenko, NCSA University of Illinois, USA
  • Dimitri Komatish, Universite Aix-Marseille, France
  • Jens Kruger, IVDA Saarbrucken, Germany
  • Jacobo Lobeiras Blanco, University of A Coruna, Spain
  • Roland Martin, University of Toulouse, France
  • Mathias Paulin, IRIT, France
  • Everett Phillips, NVIDIA, USA
  • Bastien Plazolles, France
  • Enrique Quintana-Orti, Universidad Jaime ISpain
  • Girish Ravunnikutty, University of Florida, USA
  • Premysl Sucha, Technical University In Prague, Czech Republic
  • C Vuik, TU Delft, Netherlands
  • Daniel Weiskopf, VISUS, Germany

Important dates


Paper submission: 06 September 2015
Acceptance notification: 19th Oct. 2015
Camera ready due: 20th Nov 2015
Registration due: 20th Nov. 2015
Conference: 17th - 19th Feb 2016

Submission guidelines

Prospective authors should submit a full paper not exceeding 8 pages in the Conference proceedings format (double-column, 10pt) to the conference main track or to Special Sessions through the EasyChair conference submission system with an indication of the main track or the name of the Special Session.

Publication

All accepted papers will be included in the same volume, published by the Conference Publishing Services (CPS). The Final Paper Preparation and Submission Instructions will be published after the notification of acceptance. Authors of accepted papers are expected to register and present their papers at the Conference. Conference proceedings will be submitted to IEEE explore, CSDL, and for indexing among others to DBLP, Scopus ScienceDirect, and ISI Web of Knowledge.