Algorithmic and software development advances for next‐generation heterogeneous platforms

Abstract

Heterogeneity is emerging as one of the most profound and challenging characteristics of today's and tomorrow's parallel and distributed computing environments, presenting new and exciting opportunities for their development. Most modern computing systems are heterogeneous, either for organic reasons because components grew independently, as is the case of desktop grids, by design to leverage the strength of specific hardware, as is the case of accelerated systems, or both. The impact of heterogeneity on all forms of parallel and distributed computing is increasing rapidly.Traditional algorithms, programming environments, and tools designed for legacy homogeneous systems will at best achieve a small fraction of the efficiency and the potential performance expected from parallel computing in tomorrow's highly diversified and mixed architectures. Innovative ideas, fresh models, novel algorithms, and other specialized or unified programming environments and tools are needed to efficiently use these new and increasingly diverse computing systems-for accelerating scientific discovery and impactful innovation.The International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HeteroPar) has been the premier forum over the last 20 years, bringing together researchers to discuss these challenges and the solutions. The wide range of topics includes achieving performance portability on heterogeneous architectures, advances in software environments that facilitate efficient use of heterogeneous systems, performance and energy optimization of numerical and machine learning algorithms on heterogeneous platforms, to name a few.The works presented at the HeteroPar'2020 workshop covered topics clearly exhibiting the significance and growth of the heterogeneous computing field. However, one general trend is apparent: the broad adoption of Graphics Processing Units (GPU) accelerators. Over the last decade, GPUs have been established as the main powerhouse in leadership supercomputers and an invaluable component to accelerate computations for a vast spectrum of applications-from numerical linear algebra libraries powering computational science to various machine learning workloads. This trend is evidenced by the increasing number of GPU-related publications submitted to HeterPar and supported by growing diversity within the GPU world, where AMD accelerator architectures start to compete with Nvidia's comprehensive solutions, along with the third GPU accelerator option-from Intel-available soon.This special issue of Concurrency and Computation: Practice and Experience contains six selected papers from the HeteroPar'2020 workshop.We hope you find them interesting and stimulating new ideas and future advancements for next-generation heterogeneous platforms.