The new challenges presented by exascale system architectures have resulted in difficulty achieving the desired scalability using traditional distributed-memory runtimes. Asynchronous many-task systems (AMT) are based on a new paradigm showing promise in addressing these challenges, providing application developers with a productive and performant approach to programming on next generation systems. HPX is a C++ Library for concurrency and parallelism that is developed by The STE||AR Group, an international group of collaborators working in the field of distributed and parallel programming (Heller, Diehl, Byerly, Biddiscombe, & Kaiser, 2017; Kaiser et al., n.d.; Tabbal, Anderson, Brodowicz, Kaiser, & Sterling, 2011). It is a runtime system written using modern C++ techniques that are linked as part of an application. HPX exposes extended services and functionalities supporting the implementation of parallel, concurrent, and distributed capabilities for applications in any domain; it has been used in scientific computing, gaming, finances, data mining, and other fields.

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This paper presents a methodology for using LLVM-based tools to tune the DCA++ (dynamical cluster approximation) application that targets the new ARM A64FX processor. The goal is to describe the changes required for the new architecture and generate efficient single instruction/multiple data (SIMD) instructions that target the new Scalable Vector Extension instruction set. During manual tuning, the authors used the LLVM tools to improve code parallelization by using OpenMP SIMD, refactored the code and applied transformation that enabled SIMD optimizations, and ensured that the correct libraries were used to achieve optimal performance. By applying these code changes, code speed was increased by 1.98× and 78 GFlops were achieved on the A64FX processor. The authors aim to automatize parts of the efforts in the OpenMP Advisor tool, which is built on top of existing and newly introduced LLVM tooling.

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