论文标题
GPU上的并发约束编程的变体
A Variant of Concurrent Constraint Programming on GPU
论文作者
论文摘要
如今，图形计算单元（GPU）上的核心数量已达到数千个，而处理器的时钟速度停滞不前。不幸的是，约束编程求解器尚未利用GPU并行性。原因之一是约束求解器主要是在顺序计算的心理框架中设计的。为了解决此问题，我们基于同时约束编程，退后一步，并为简单，本质上的，无锁和正式正确的编程语言做出了贡献。然后，我们在这种形式主义中重新检查对GPU的并行约束解决方案，并开发了Turbo，这是一个完全编程的gpus的简单约束求解器。 Turbo验证了我们方法的正确性，并与基于CPU的求解器进行了积极比较。
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.
