面向异构多核处理器的并行代价模型

Parallel cost model for heterogeneous multi-core processors

现有的并行代价模型大多是面向共享存储或分布存储结构设计的,不完全适合异构多核处理器。为解决这个问题,提出了面向异构多核处理器的并行代价模型,通过定量刻画计算核心运算能力、存储访问延迟和数据传输开销对循环并行执行时间的影响,提高加速并行循环识别的准确性。实验结果表明,提出的并行代价模型能有效识别加速并行循环,将其识别结果作为后端生成并行代码的依据,可有效提高并行程序在异构多核处理器上的性能。

The existing parallel cost models are mostly devised for shared memory or distributed memory architecture, thus not suitable for heterogeneous multi-core processors. In order to solve the problem, a new parallel cost model for heterogeneous multi-cores was proposed. It described the impact of computing capacity, memory access delay and data transfer cost on parallel execution time of loops quantitatively, thus improving the veracity of accelerated parallel loop recognition. The experimental results show that the proposed model can effectively recognize the accelerated parallel loops. Using its recognition results to generate parallel codes can improve the performance of parallel programs on heterogeneous multi-core processors significantly.