Juggle: proactive load balancing on multicore computers

  • Authors:

  • Steven Hofmeyr;Juan A. Colmenares;Costin Iancu;John Kubiatowicz

  • Affiliations:

  • Lawrence Berkeley National Laboratory, Berkeley, CA, USA;University of California, Berkeley, Berkeley, CA, USA;Lawrence Berkeley National Laboratory, Berkeley, CA, USA;University of California, Berkeley, Berkeley, CA, USA

  • Venue:
  • Proceedings of the 20th international symposium on High performance distributed computing
  • Year:
  • 2011

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Abstract

We investigate proactive dynamic load balancing on multicore systems, in which threads are continually migrated to reduce the impact of processor/thread mismatches to enhance the flexibility of the SPMD-style programming model, and enable SPMD applications to run efficiently in multiprogrammed environments. We present Juggle, a practical decentralized, user-space implementation of a proactive load balancer that emphasizes portability and usability. Juggle shows performance improvements of up to 80% over static balancing for UPC, OpenMP, and pthreads benchmarks. We analyze the impact of Juggle on parallel applications and derive lower bounds and approximations for thread completion times. We show that results from Juggle closely match theoretical predictions across a variety of architectures, including NUMA and hyper-threaded systems. We also show that Juggle is effective in multiprogrammed environments with unpredictable interference from unrelated external applications.