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| Content Provider | ACM Digital Library |
|---|---|
| Author | Xie, Yuan Li, Sheng Brockman, Jay B. Muralimanohar, Naveen O, Seongil Zhao, Jishen Xu, Cong Ahn, Jung Ho Jouppi, Norman P. Chen, Ke |
| Abstract | 3D-stacked DRAM has the potential to provide high performance and large capacity memory for future high performance computing systems and datacenters, and the integration of a dedicated logic die opens up opportunities for architectural enhancements such as DRAM row-buffer caches. However, high performance and cost-effective row-buffer cache designs remain challenging for 3D memory systems. In this paper, we propose History-Assisted Adaptive-Granularity Cache (HAAG\$) that employs an adaptive caching scheme to support full associativity at various granularities, and an intelligent history-assisted predictor to support a large number of banks in 3D memory systems. By increasing the row-buffer cache hit rate and avoiding unnecessary data caching, HAAG\$ significantly reduces memory access latency and dynamic power. Our design works particularly well for manycore CPUs running (irregular) memory intensive applications where memory locality is hard to exploit. Evaluation results show that with memory-intensive CPU workloads, HAAG\$ can outperform the state-of-the-art row buffer cache by 33.5%. |
| Starting Page | 251 |
| Ending Page | 261 |
| Page Count | 11 |
| File Format | |
| ISBN | 9781450335591 |
| DOI | 10.1145/2751205.2751227 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-06-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Haag$ Row buffer cache Adaptive granularity 3d dram |
| Content Type | Text |
| Resource Type | Article |
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