Tasking framework for Adaptive Speculative Parallel Mesh Generation

TL;DR

This paper introduces a tasking framework for adaptive speculative parallel mesh generation that separates functionality from performance, improving flexibility and speedup while maintaining portability.

Contribution

It presents a reusable tasking framework that abstracts load balancing and thread management for mesh generation, enhancing flexibility and performance.

Findings

Achieved up to 13% speedup in mesh operations.

Realized up to 5.8% overall application speedup.

Improved task creation strategies reduced overhead by up to 1200%.

Abstract

Handling the ever-increasing complexity of mesh generation codes along with the intricacies of newer hardware often results in codes that are both difficult to comprehend and maintain. Different facets of codes such as thread management and load balancing are often intertwined, resulting in efficient but highly complex software. In this work, we present a framework which aids in establishing a core principle, deemed separation of concerns, where functionality is separated from performance aspects of various mesh operations. In particular, thread management and scheduling decisions are elevated into a generic and reusable tasking framework. The results indicate that our approach can successfully abstract the load balancing aspects of two case studies, while providing access to a plethora of different execution back-ends. One would expect, this new flexibility to lead to some additional cost. However, for the configurations studied in this work, we observed up to 13% speedup for some meshing operations and up to 5.8% speedup over the entire application runtime compared to hand-optimized code. Moreover, we show that by using different task creation strategies, the overhead compared to straight-forward task execution models can be improved dramatically by as much as 1200% without compromises in portability and functionality.