There is growing interest in accelerating irregular data-parallel
algorithms on GPUs. These algorithms are typically
blocking
, so
they require fair scheduling. But GPU programming models (e.g.
OpenCL) do not mandate fair scheduling, and GPU schedulers are
unfair in practice. Current approaches avoid this issue by exploit-
ing scheduling quirks of today’s GPUs in a manner that does not
allow the GPU to be shared with other workloads (such as graphics
rendering tasks). We propose
cooperative kernels
, an extension to
the traditional GPU programming model geared towards writing
blocking algorithms. Workgroups of a cooperative kernel
are
fairly
scheduled, and multitasking is supported via a small set of language
extensions through which the kernel and scheduler cooperate. We
describe a prototype implementation of a cooperative kernel frame-
work implemented in OpenCL 2.0 and evaluate our approach by
porting a set of blocking GPU applications to cooperative kernels
and examining their performance under multitasking.