(Georgia Institute of Technology, 2015)
Lee, Joo Hwan; Nigania, Nimit; Kim, Hyesoon; Brett, Bevin
Heterogeneous computing has emerged as one of
the major computing platforms in many domains. Although
there have been several proposals to aid programming for
heterogeneous computing platforms, optimizing applications
on heterogeneous computing platforms is not an easy task.
Identifying which parallel regions (or tasks) should run on
GPUs or CPUs is one of the critical decisions to improve
performance. In this paper, we propose a profiler, HPerf, to identify
an efficient task distribution on CPUs+GPUs system with
low profiling overhead. HPerf is a hierarchical profiler. First
it performs lightweight profiling and then if necessary, it
performs detailed profiling to measure caching and data
transfer cost. Compared to a brute-force approach, HPerf
reduces the profiling overhead significantly and compared to
a naive decision, HPerf improves the performance of OpenCL
applications up to 25%.