Kinship: efficient resource management for performance and functionally asymmetric platforms

  • Authors:
  • Vishakha Gupta;Rob Knauerhase;Paul Brett;Karsten Schwan

  • Affiliations:
  • Intel Labs, Hillsboro, OR;Intel Labs, Hillsboro, OR;Intel Labs, Hillsboro, OR;Georgia Institute of Technology, Atlanta, GA

  • Venue:
  • Proceedings of the ACM International Conference on Computing Frontiers
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

On-chip heterogeneity has become key to balancing performance and power constraints, resulting in disparate (functionally overlapping but not equivalent) cores on a single die. Requiring developers to deal with such heterogeneity can impede adoption through increased programming effort and result in cross-platform incompatibility. We propose that systems software must evolve to dynamically accommodate heterogeneity and to automatically choose task-to-resource mappings to best use these features. We describe the kinship approach for mapping workloads to heterogeneous cores. A hypervisor-level realization of the approach on a variety of experimental heterogeneous platforms demonstrates the general applicability and utility of kinship-based scheduling, matching dynamic workloads to available resources as well as scaling with the number of processes and with different types/configurations of compute resources. Performance advantages of kinship based scheduling are evident for runs across multiple generations of heterogeneous platforms.