Efficient Implementation of GPGPU Synchronization Primitives on CPUs

Jayanth Gummaraju Ben Sander Laurent Morichetti Benedict Gaster Lee Howes

ACM Conference Computing Frontiers. 2010.


The GPGPU model represents a style of execution where thousands of threads execute in a data-parallel fashion, with a large subset (typically 10s to 100s) needing frequent synchronization. As the GPGPU model evolves to target both GPUs and CPUs as acceleration targets, thread synchronization becomes an important problem when running on CPUs. CPUs have little hardware support for synchronization
and must be emulated in software, reducing application performance. This paper presents software techniques to implement the GPGPU synchronization primitives on CPUs,
while maintaining application debug-ability. Performing limit studies using real hardware, we evaluate the potential performance benefits of an efficient barrier primitive.