DIPP—An LLC Replacement Policy for On-chip Dynamic Heterogeneous Multi-core Architecture

• 作者: Ma Xiao Xing Zuocheng Zhang Yang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 文学
• 关键词: BIG data HETEROGENEOUS MULTICORE REPLACEMENT Policy DIPP.
• 年，卷（期）: 2015,（1）
• 页码范围: 112-113
• 页数: 2页
• 分类号: H313

摘要

As the big data era is coming, it brings new challenges to the massive data processing. A combination of GPU and CPU on chip is the trend to release the pressure of large scale computing. We found that there are different memory access characteristics between GPU and CPU. The most important one is that the programs of GPU include a large number of threads, which lead to higher access frequency in cache than the CPU programs. Although the LRU policy favors the programs with high memory access frequency, the programs of GPU can’t get the corresponding performance boost even more cache resources are provided. So LRU policy is not suitable for heterogeneous multi-core processor. Based on the different characteristics of GPU and CPU programs on memory access, this paper proposes an LLC dynamic replacement policy--DIPP (Dynamic Insertion / Promotion Policy) for heterogeneous multi-core processors.The core idea of the replacement policy is to reduce the miss rate of the program and enhance the overall system performance by limiting the cache resources that GPU can acquire and reducing the thread interferences between programs. Experiments compare the DIPP replacement policy with LRU and we conduct a classified discussion according to the program results of GPU. Friendly programs enhance 23.29% on the average performance (using arithmetic mean).Large working sets programs can improve 13.95%, compute-intensive programs enhance 9.66% and stream class programs improve 3.8%.