Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications
The emergence of big data processing and machine learning has triggered the exponential growth of the working set sizes of applications. In addition, several modern applications are memory intensive with irregular memory access patterns. Therefore, we propose the concept of adaptive granularities to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/3/991 |
| _version_ | 1827598724662034432 |
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| author | Sae-Gyeol Choi Jeong-Geun Kim Shin-Dug Kim |
| author_facet | Sae-Gyeol Choi Jeong-Geun Kim Shin-Dug Kim |
| author_sort | Sae-Gyeol Choi |
| collection | DOAJ |
| description | The emergence of big data processing and machine learning has triggered the exponential growth of the working set sizes of applications. In addition, several modern applications are memory intensive with irregular memory access patterns. Therefore, we propose the concept of adaptive granularities to develop a prefetching methodology for analyzing memory access patterns based on a wider granularity concept that entails both cache lines and page granularity. The proposed prefetching module resides in the last-level cache (LLC) to handle the large working set of memory-intensive workloads. Additionally, to support memory access streams with variable intervals, we introduced an embedded-DRAM based LLC prefetch buffer that consists of three granularity-based prefetch engines and an access history table. By adaptively changing the granularity window for analyzing memory streams, the proposed model can swiftly and appropriately determine the stride of memory addresses to generate hidden delta chains from irregular memory access patterns. The proposed model achieves 18% and 15% improvements in terms of energy consumption and execution time compared to global history buffer and best offset prefetchers, respectively. In addition, our model reduced the total execution time and energy consumption by approximately 6% and 2.3%, compared to those of the Markov prefetcher and variable-length delta prefetcher. |
| first_indexed | 2024-03-09T03:58:58Z |
| format | Article |
| id | doaj.art-e74eb8df981741748e9b7496db3159b5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:58:58Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e74eb8df981741748e9b7496db3159b52023-12-03T14:17:10ZengMDPI AGApplied Sciences2076-34172021-01-0111399110.3390/app11030991Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing ApplicationsSae-Gyeol Choi0Jeong-Geun Kim1Shin-Dug Kim2Department of Computer Science, Yonsei University, Seoul 03722, KoreaDepartment of Computer Science, Yonsei University, Seoul 03722, KoreaDepartment of Computer Science, Yonsei University, Seoul 03722, KoreaThe emergence of big data processing and machine learning has triggered the exponential growth of the working set sizes of applications. In addition, several modern applications are memory intensive with irregular memory access patterns. Therefore, we propose the concept of adaptive granularities to develop a prefetching methodology for analyzing memory access patterns based on a wider granularity concept that entails both cache lines and page granularity. The proposed prefetching module resides in the last-level cache (LLC) to handle the large working set of memory-intensive workloads. Additionally, to support memory access streams with variable intervals, we introduced an embedded-DRAM based LLC prefetch buffer that consists of three granularity-based prefetch engines and an access history table. By adaptively changing the granularity window for analyzing memory streams, the proposed model can swiftly and appropriately determine the stride of memory addresses to generate hidden delta chains from irregular memory access patterns. The proposed model achieves 18% and 15% improvements in terms of energy consumption and execution time compared to global history buffer and best offset prefetchers, respectively. In addition, our model reduced the total execution time and energy consumption by approximately 6% and 2.3%, compared to those of the Markov prefetcher and variable-length delta prefetcher.https://www.mdpi.com/2076-3417/11/3/991computer architecturememory architecturememory managementcache memoryprefetchinghigh-performance computing |
| spellingShingle | Sae-Gyeol Choi Jeong-Geun Kim Shin-Dug Kim Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications Applied Sciences computer architecture memory architecture memory management cache memory prefetching high-performance computing |
| title | Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications |
| title_full | Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications |
| title_fullStr | Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications |
| title_full_unstemmed | Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications |
| title_short | Adaptive Granularity Based Last-Level Cache Prefetching Method with eDRAM Prefetch Buffer for Graph Processing Applications |
| title_sort | adaptive granularity based last level cache prefetching method with edram prefetch buffer for graph processing applications |
| topic | computer architecture memory architecture memory management cache memory prefetching high-performance computing |
| url | https://www.mdpi.com/2076-3417/11/3/991 |
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