Implementing Implicit OpenMP Data Sharing on GPUs
Author/Presenters
Event Type
Workshop
Compiler Analysis and Optimization
Compilers
Debugging
Parallel Programming Languages, Libraries, Models
and Notations
Program Transformation
SIGHPC Workshop
TimeMonday, November 13th3:30pm -
4pm
Location710
DescriptionOpenMP 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 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 data sharing scheme offers the users a simple memory model for controlling the implicit allocation of device shared memory.
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 data sharing scheme offers the users a simple memory model for controlling the implicit allocation of device shared memory.
