Writeback-Aware LLC Management for PCM-Based Main Memory Systems

Author:

Pourshirazi Bahareh1,Beigi Majed Valad2,Zhu Zhichun1,Memik Gokhan2

Affiliation:

1. University of Illinois at Chicago, Chicago, USA

2. Northwestern University, Evanston, USA

Abstract

With the increase in the number of data-intensive applications on today's workloads, DRAM-based main memories are struggling to satisfy the growing data demand capacity. Phase Change Memory (PCM) is a type of non-volatile memory technology that has been explored as a promising alternative for DRAM-based main memories due to its better scalability and lower leakage energy. Despite its many advantages, PCM also has shortcomings such as long write latency, high write energy consumption, and limited write endurance, which are all related to the write operations. In this article, we propose a novel writeback-aware Last Level Cache (LLC) management scheme named WALL to reduce the number of LLC writebacks and consequently improve performance, energy efficiency, and lifetime of a PCM-based main memory system. First, we investigate the writeback behavior of LLC sets and show that writebacks are not uniformly distributed among sets; some sets observe much higher writeback rates than others. We then propose a writeback-aware set-balancing mechanism, which employs the underutilized LLC sets with few writebacks as an auxiliary storage for the evicted dirty lines from sets with frequent writebacks. We also propose a simple and effective writeback-aware replacement policy to avoid the eviction of the dirty blocks that are highly reused after being evicted from the cache. Our experimental results show that WALL achieves an average of 30.9% reduction in the total number of LLC writebacks, compared to the baseline scheme, which uses the LRU replacement policy. As a result, WALL can reduce the memory energy consumption by 23.1% and enhance PCM lifetime by 1.29×, on average, on an 8-core system with a 4GB PCM main memory, running memory-intensive applications.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference38 articles.

1. Boosting Access Parallelism to PCM-Based Main Memory

2. TAPAS

3. Thermal-aware optimizations of reRAM-based neuromorphic computing systems

4. The PARSEC benchmark suite

5. CACTI-6.5. Retrieved from http: //hpl .hp.com: research /cacti/. CACTI-6.5. Retrieved from http: //hpl .hp.com: research /cacti/.

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