@article {1269, title = {Evaluation of Directive-Based Performance Portable Programming Models}, journal = {International Journal of High Performance Computing and Networking}, volume = {14}, year = {2019}, month = {2019{\textendash}07}, pages = {165-182}, abstract = {We present an extended exploration of the performance portability of directives provided by OpenMP 4 and OpenACC to program various types of node architecture with attached accelerators, both self-hosted multicore and offload multicore/GPU. Our goal is to examine how successful OpenACC and the newer offload features of OpenMP 4.5 are for moving codes between architectures, and we document how much tuning might be required and what lessons we can learn from these experiences. To do this, we use examples of algorithms with varying computational intensities for our evaluation, as both compute and data access efficiency are important considerations for overall application performance. To better understand fundamental compute vs. bandwidth bound characteristics, we add the compute-bound Level 3 BLAS GEMM kernel to our linear algebra evaluation. We implement the kernels of interest using various methods provided by newer OpenACC and OpenMP implementations, and we evaluate their performance on various platforms including both x86_64 and Power8 with attached NVIDIA GPUs, x86_64 multicores, self-hosted Intel Xeon Phi KNL, as well as an x86_64 host system with Intel Xeon Phi coprocessors. We update these evaluations with the newest version of the NVIDIA Pascal architecture (P100), Intel KNL 7230, Power8+, and the newest supporting compiler implementations. Furthermore, we present in detail what factors affected the performance portability, including how to pick the right programming model, its programming style, its availability on different platforms, and how well compilers can optimise and target multiple platforms.}, keywords = {OpenACC, OpenMP 4, performance portability, Programming models}, doi = {http://dx.doi.org/10.1504/IJHPCN.2017.10009064 }, author = {M. Graham Lopez and Wayne Joubert and Ver{\'o}nica Larrea and Oscar Hernandez and Azzam Haidar and Stanimire Tomov and Jack Dongarra} } @techreport {1171, title = {POMPEI: Programming with OpenMP4 for Exascale Investigations}, journal = {Innovative Computing Laboratory Technical Report}, number = {ICL-UT-17-09}, year = {2017}, month = {2017-12}, publisher = {University of Tennessee}, abstract = {The objective of the Programming with OpenMP4 for Exascale Investigations (POMPEI) project is to explore new task-based programming techniques together with data structure centric programming for scientific applications to harness the potential of extreme-scale systems. Tasking is a well established by now approach on such systems as it has been used successfully to handle their large-scale parallelism and heterogeneity, which are leading challenges on the way to exascale computing. The approach is to harness the latest features of OpenMP4.5 and OpenACC2.5 to design abstractions shared among tasks and mapped efficiently to data-structure driven programming paradigms. This technical report describes the approach, along with its reference implementation and results for dense linear algebra algorithms.}, author = {Jack Dongarra and Azzam Haidar and Oscar Hernandez and Stanimire Tomov and Manjunath Gorentla Venkata} } @article {icl:417, title = {Performance Instrumentation and Compiler Optimizations for MPI/OpenMP Applications}, journal = {Lecture Notes in Computer Science, OpenMP Shared Memory Parallel Programming}, volume = {4315}, year = {2008}, month = {2008-00}, publisher = {Springer Berlin / Heidelberg}, author = {Oscar Hernandez and Fengguang Song and Barbara Chapman and Jack Dongarra and Bernd Mohr and Shirley Moore and Felix Wolf} } @inproceedings {icl:319, title = {Performance Instrumentation and Compiler Optimizations for MPI/OpenMP Applications}, journal = {Second International Workshop on OpenMP}, year = {2006}, month = {2006-01}, address = {Reims, France}, keywords = {kojak}, author = {Oscar Hernandez and Fengguang Song and Barbara Chapman and Jack Dongarra and Bernd Mohr and Shirley Moore and Felix Wolf} }