General purpose computing on low-power embedded GPUs: Has it come of age?

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Maghazeh:2013:SAMOS,
  author =       "Arian Maghazeh and Unmesh D. Bordoloi and 
                 Petru Eles and Zebo Peng",
  title =        "General purpose computing on low-power embedded
                 {GPUs}: Has it come of age?",
  booktitle =    "2013 International Conference on Embedded Computer
                 Systems: Architectures, Modeling, and Simulation (SAMOS
                 XIII)",
  year =         "2013",
  editor =       "H. Jeschke",
  address =      "Samos, Greece",
  month =        "15-18 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, GPU, GPGPU,
                 OpenCL, ARM Cortex A9 Vivante GC2000 GPU, Tesla M2050,
                 Intertwined Spiral problem, Rijndael, bitcount ,
                 convolution, pattern matching, energy consumption",
  isbn13 =       "978-1-4799-0103-6",
  DOI =          "doi:10.1109/SAMOS.2013.6621099",
  size =         "10 pages",
  abstract =     "In this paper we evaluate the promise held by
                 low-power GPUs for non-graphic workloads that arise in
                 embedded systems. Towards this, we map and implement 5
                 benchmarks, that find utility in very different
                 application domains, to an embedded GPU. Our results
                 show that apart from accelerated performance, embedded
                 GPUs are promising also because of their energy
                 efficiency which is an important design goal for
                 battery-driven mobile devices. We show that adopting
                 the same optimization strategies as those used for
                 programming high-end GPUs might lead to worse
                 performance on embedded GPUs. This is due to restricted
                 features of embedded GPUs, such as, limited or no
                 user-defined memory, small instruction-set, limited
                 number of registers, among others. We propose
                 techniques to overcome such challenges, e.g., by
                 distributing the workload between GPUs and multi-core
                 CPUs, similar to the spirit of heterogeneous
                 computation.",
  notes =        "Dept. of Comput. & Inf. Sci., Linkopings Univ.,
                 Linkopings, Sweden

                 Also known as \cite{6621099}",
}

Genetic Programming entries for Arian Maghazeh Unmesh D Bordoloi Petru Eles Zebo Peng

Citations