Adaptive partitioning for irregular applications on heterogeneous CPU-GPU chips

Antonio Vilches, Rafael Asenjo, Angeles Navarro, Francisco Corbera, Rubén Gran, María Garzarán

Research output: Contribution to journalConference articlepeer-review


Commodity processors are comprised of several CPU cores and one integrated GPU. To fully exploit this type of architectures, one needs to automatically determine how to partition the workload between both devices. This is specially challenging for irregular workloads, where each iteration's work is data dependent and shows control and memory divergence. In this paper, we present a novel adaptive partitioning strategy specially designed for irregular applications running on heterogeneous CPU-GPU chips. The main novelty of this work is that the size of the workload assigned to the GPU and CPU adapts dynamically to maximize the GPU and CPU utilization while balancing the workload among the devices. Our experimental results on an Intel Haswell architecture using a set of irregular benchmarks show that our approach outperforms exhaustive static and adaptive state-of-the-art approaches in terms of performance and energy consumption.

Original languageEnglish (US)
Pages (from-to)140-149
Number of pages10
JournalProcedia Computer Science
Issue number1
StatePublished - 2015
EventInternational Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands
Duration: Apr 21 2002Apr 24 2002


  • Adaptive partitioning
  • Dynamic scheduling
  • Heterogeneous CPU-GPU chips
  • Parallel for

ASJC Scopus subject areas

  • Computer Science(all)


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