Comparison of Parallelization Approaches, Languages, and
Compilers for Unstructured Mesh Algorithms on GPUs
Author/Presenters
Event Type
Workshop
Accelerators
Benchmarks
Compiler Analysis and Optimization
Deep Learning
Effective Application of HPC
Energy
Exascale
GPU
I/O
Parallel Application Frameworks
Parallel Programming Languages, Libraries, Models
and Notations
Performance
Simulation
Storage
TimeMonday, November 13th11am -
11:30am
Location704-706
DescriptionEfficiently exploiting GPUs is increasingly essential
in scientific computing, as many current and upcoming
supercomputers are built using them. To facilitate this,
there are a number of programming approaches, such as
CUDA, OpenACC and OpenMP 4, supporting different
programming languages (mainly C/C++ and Fortran). There
are also several compiler suites (clang, nvcc, PGI, XL)
each supporting different combinations of languages. In
this study, we take a detailed look at some of the
currently available options, and carry out a
comprehensive analysis and comparison using
computational loops and applications from the domain of
unstructured mesh computations. Beyond runtimes and
performance metrics (GB/s), we explore factors that
influence performance such as register counts,
occupancy, usage of different memory types, instruction
counts, and algorithmic differences. Results of this
work show how clang's CUDA compiler frequently
outperform NVIDIA's nvcc, performance issues with
directive-based approaches on complex kernels, and
OpenMP 4 support maturing in clang and XL; currently
around 10% slower than CUDA.
