Name
Abstract | ||
|---|---|---|
Recent advances in non-volatile memory (NVM) technology offer more capacity, scalability, and durability than regular volatile memory. However, adopting emerging memory introduces new challenges such as endurance issue and performance degradation. This paper proposes a software wear leveling technique that is designed specifically for a scenario, where the NVM is managed by generational garbage collectors that divide heap into separate young and old space. Prior work found that the majority of write traffic occurs to young generation heap, thus proposed a hybrid generational heap, where the NVM is utilized as old space and DRAM works as young space. We further investigate the access pattern in NVM-contained old generation. This paper first highlights a common observation across various workloads that the write distribution of old space is highly unbalanced. Specifically, among all writes to the NVM, 56%-96% occurs to 2% memory pages, and in worst cases, 83%-96% traffic occurs to only 0.5% pages. This leveling can be achieved by only swapping a very small fraction of NVM pages during the garbage collection cycle, which makes a low performance overhead runtime approach possible. We maintain a write intensity counter for each thread and insert a new garbage collection phase, where a few hottest pages are selected and remapped. Results show our approach significantly mitigates the skewness indicated by Gini coefficient dropping from 0.95 to 0.60, leading to extending lifetime 41 times on average. Meanwhile, it incurs a performance cost of 7.2% longer mutation time measured on emulated memory without performance penalty, and 3.31 % extra stop time for garbage collection cycles. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2875820 | IEEE ACCESS |
Keywords | Field | DocType |
Garbage collection,non-volatile memory,runtime system,wear leveling | Garbage,Computer science,Wear leveling,Heap (data structure),Non-volatile memory,Garbage collection,Volatile memory,Runtime system,Distributed computing,Embedded system,Scalability | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingyu Zhu | 1 | 1 | 2.71 |
| Zhiguang Chen | 2 | 79 | 18.83 |
| Fang Liu | 3 | 1188 | 125.46 |
| Nong Xiao | 4 | 649 | 116.15 |