A Comparative Study of Asynchronous Many-Tasking Runtimes: Cilk, Charm++, ParalleX and AM++

TL;DR

This paper compares four high-performance computing runtimes—Cilk, Charm++, ParalleX, and AM++—evaluating their programming and execution models, implementations, and suitability for future supercomputing challenges.

Contribution

It provides a comprehensive qualitative and quantitative comparison of these runtimes, highlighting their expressivity, programmability, and performance characteristics.

Findings

Charm++ and Cilk++ show high programmability and performance.

Differences in execution models impact scalability and efficiency.

Insights guide future development of parallel programming frameworks.

Abstract

We evaluate and compare four contemporary and emerging runtimes for high-performance computing(HPC) applications: Cilk, Charm++, ParalleX and AM++. We compare along three bases: programming model, execution model and the implementation on an underlying machine model. The comparison study includes a survey of each runtime system's programming models, their corresponding execution models, their stated features, and performance and productivity goals. We first qualitatively compare these runtimes with programmability in mind. The differences in expressivity and programmability arising from their syntax and semantics are clearly enunciated through examples common to all runtimes. Then, the execution model of each runtime, which acts as a bridge between the programming model and the underlying machine model, is compared and contrasted to that of the others. We also evaluate four mature implementations of these runtimes, namely: Intel Cilk++, Charm++ 6.5.1, AM++ and HPX, that embody the principles dictated by these models. With the emergence of the next generation of supercomputers, it is imperative for parallel programming models to evolve and address the integral challenges introduced by the increasing scale. Rather than picking a winner out of these four models under consideration, we end with a discussion on lessons learned, and how such a study is instructive in the evolution of parallel programming frameworks to address the said challenges.