Title
Preserving Row Buffer Locality for PCM Wear-Leveling under Massive Parallelism
Abstract
Phase Change Memory (PCM) is a promising alternative technology for DRAM because of its advantages in terms of transistor density and energy consumption. It has been exploited to work in concert or alone inside various memory systems to meet the growing bandwidth needs of massive parallelism. PCM memory cells, however, have a common problem of limited write endurance. Various wear-leaving techniques have been employed for uniform distribution of memory writes, typically through address transformation schemes such as randomization to avoid hot writes. Unfortunately, such address transformation can have the undesirable consequence of disrupting the row buffer locality in sequential memory accesses, resulting in the loss of memory performance. Our analysis reveals that this situation is particularly severe under massive parallelism of manycore processors such as GPUs. In this paper, we introduce a combination of two techniques, matrix-based partial randomization and rowbuffer locality-aware rotation, to alleviate the locality disruption of address transformation and preserve the row buffer locality of PCM-based global memory in GPU. Our evaluation results show that, compared to existing techniques, our techniques can adequately preserve the row buffer locality and minimize the loss of memory performance, while achieving similar endurance and better energy efficiency for a variety of GPGPU applications.
Year
DOI
Venue
2015
10.1109/MASCOTS.2015.39
IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Keywords
Field
DocType
GPU,NVM,Row Buffer Locality,Endurance,Wear Leveling,Address Mapping
Registered memory,Phase-change memory,Interleaved memory,Physical address,Wear leveling,Computer science,Parallel computing,Real-time computing,Write buffer,Flat memory model,Sequential access memory
Conference
ISSN
Citations 
PageRank 
1526-7539
1
0.35
References 
Authors
22
4
Name
Order
Citations
PageRank
Xinning Wang190.82
Bin Wang21208.13
Zhuo Liu311816.03
Weikuan Yu4104277.40