@article {,
	title = {Ginkgo{\textemdash}A math library designed for platform portability},
	journal = {Parallel Computing},
	volume = {111},
	year = {2022},
	month = {2022-02},
	pages = {102902},
	abstract = {In an era of increasing computer system diversity, the portability of software from one system to another plays a central role. Software portability is important for the software developers as many software projects have a lifetime longer than a specific system, e.g., a supercomputer, and it is important for the domain scientists that realize their scientific application in a software framework and want to be able to run on one or another system. On a high level, there exist two approaches for realizing platform portability: (1) implementing software using a portability layer leveraging any technique which always generates specific kernels from another language or through an interface for running on different architectures; and (2) providing backends for different hardware architectures, with the backends typically differing in how and in which programming language functionality is realized due to using the language of choice for each hardware (e.g., CUDA kernels for NVIDIA GPUs, SYCL (DPC++) kernels to targeting Intel GPUs and other supported hardware, {\textellipsis}). In practice, these two approaches can be combined in applications to leverage their respective strengths. In this paper, we present how we realize portability across different hardware architectures for the Ginkgo library by following the second strategy and the goal to not only port to new hardware architectures but also achieve good performance. We present the Ginkgo library design, separating algorithms from hardware-specific kernels forming the distinct hardware executors, and report our experience when adding execution backends for NVIDIA, AMD, and Intel GPUs. We also present the performance we achieve with this approach for distinct hardware backends.},
	keywords = {AMD, Intel, nVidia, performance portability, Platform Portability, Porting to GPU accelerators},
	issn = {0167-8191},
	doi = {https://doi.org/10.1016/j.parco.2022.102902},
	url = {https://www.sciencedirect.com/science/article/pii/S0167819122000096},
	author = {Terry Cojean and Yu-Hsiang Mike Tsai and Hartwig Anzt}
}
@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}
}
