TL;DR
This paper introduces Turbo, a GPU-based constraint solver built on a new parallel, lock-free programming language, demonstrating improved performance and correctness over CPU-based solvers.
Contribution
It presents a novel intrinsically parallel, lock-free programming language for constraint programming and develops Turbo, a GPU-based solver that outperforms CPU counterparts.
Findings
Turbo is correct and efficient.
GPU constraint solving can outperform CPU solutions.
The new language simplifies parallel constraint programming.
Abstract
The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed of processors stagnates. Unfortunately, constraint programming solvers do not take advantage yet of GPU parallelism. One reason is that constraint solvers were primarily designed within the mental frame of sequential computation. To solve this issue, we take a step back and contribute to a simple, intrinsically parallel, lock-free and formally correct programming language based on concurrent constraint programming. We then re-examine parallel constraint solving on GPUs within this formalism, and develop Turbo, a simple constraint solver entirely programmed on GPUs. Turbo validates the correctness of our approach and compares positively to a parallel CPU-based solver.
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
