Session

DescriptionThe advent of new supercomputing architectures often challenges the current best practices, programming paradigms and potentially renders state-of-the-art software outdated. Given the research and development man-years spent on scientific applications, this risk should make the HPC community consider more sustainable and long-term development strategies. A case in point is the GPU development, where one must decide carefully the platform to target, e.g., CUDA, OpenCL, ROCm. On the other hand, there exist few examples of software that is agnostic to the underlying architecture, offering the possibility of a single code to deal with multiple architectures. Currently, scientists and engineers often develop their applications for a very specific architecture, spending valuable time optimizing and tailoring their codes. Furthermore, as the code moves to different platforms with different accelerators, the code branches into multiple development streams, where each of them is dealing with its own platform-specific issues that are solved with diverging techniques. Thus, it is critical to open a wide discussion on frameworks, inherently parallel programming languages, compilers, platforms and a combination of them, that will help the community choose a development pipeline, leading to HPC models that favor flexible, versatile and sustainable solutions.