Topic 16: GPU and Accelerators Computing


GPU, FPGA, Cell and other computing chips are advanced parallel architectures which can strongly accelerate computation of highly demanding application. Indeed, scientific applications of various scientific areas are leaning towards these novel architectures. However, programming to efficiently use such accelerators is a very hard challenge resulting from their inherent parallelism and their specific architecture. Moreover, to get best performance it is necessary to conjointly use regular CPUs or several accelerator simultaneously.

The goal of this topic is to provide a forum for exchanging new ideas and progress in the domain of accelerator-based computing. We encourage submissions in all areas related to accelerators: architecture, languages, compilers, libraries, runtime, debugging and profiling tools, algorithms, applications, etc.


  • New accelerator architectures
  • Language, Compilers, and Runtime environments for accelerator programming
  • Programing clusters of accelerators
  • Tools for debugging, profiling, and optimizing programs on accelerator
  • Hybrid applications using several accelerator and/or CPUs
  • Parallel algorithms for accelerators
  • Models and benchmarks for accelerators
  • Manual optimization and auto-tuning
  • Library support for accelerators

Topic Committee

Global chair

Wolfgang Karl, University of Karlsruhe, Germany

Local chair

Samuel Thibault, University of Bordeaux, France


Stan Tomov, University of Tennessee, USA
Taisuke Boku, University of Tsukuba, Japan