Title: Implementing implicit OpenMP data sharing on GPUs,
Abstract: OpenMP is a shared memory programming model which supports the offloading of
target regions to accelerators such as NVIDIA GPUs. The implementation in
Clang/LLVM aims to deliver a generic GPU compilation toolchain that supports
both the native CUDA C/C++ and the OpenMP device offloading models. There are
situations where the semantics of OpenMP and those of CUDA diverge. One such
example is the policy for implicitly handling local variables. In CUDA, local
variables are implicitly mapped to thread local memory and thus become private
to a CUDA thread. In OpenMP, due to semantics that allow the nesting of regions
executed by different numbers of threads, variables need to be implicitly
\emph{shared} among the threads of a contention group. In this paper we
introduce a re-design of the OpenMP device data sharing infrastructure that is
responsible for the implicit sharing of local variables in the Clang/LLVM
toolchain. We introduce a new data sharing infrastructure that lowers
implicitly shared variables to the shared memory of the GPU. We measure the
amount of shared memory used by our scheme in cases that involve scalar
variables and statically allocated arrays. The evaluation is carried out by
offloading to K40 and P100 NVIDIA GPUs. For scalar variables the pressure on
shared memory is relatively low, under 26\% of shared memory utilization for
the K40, and does not negatively impact occupancy. The limiting occupancy
factor in that case is register pressure. The data sharing scheme offers the
users a simple memory model for controlling the implicit allocation of device
shared memory.