Title
Lazy Persistency: A High-Performing and Write-Efficient Software Persistency Technique.
Abstract
Emerging Non-Volatile Memories (NVMs) are expected to be included in future main memory, providing the opportunity to host important data persistently in main memory. However, achieving persistency requires that programs be written with failure-safety in mind. Many persistency models and techniques have been proposed to help the programmer reason about failure-safety. They require that the programmer eagerly flush data out of caches to make it persistent. Eager persistency comes with a large overhead because it adds many instructions to the program for flushing cache lines and incurs costly stalls at barriers to wait for data to become durable. To reduce these overheads, we propose Lazy Persistency (LP), a software persistency technique that allows caches to slowly send dirty blocks to the NVMM through natural evictions. With LP, there are no additional writes to NVMM, no decrease in write endurance, and no performance degradation from cache line flushes and barriers. Persistency failures are discovered using software error detection (checksum), and the system recovers from them by recomputing inconsistent results. We describe the properties and design of LP and demonstrate how it can be applied to loop-based kernels popularly used in scientific computing. We evaluate LP and compare it to the state-of-the-art Eager Persistency technique from prior work. Compared to it, LP reduces the execution time and write amplification overheads from 9% and 21% to only 1% and 3%, respectively.
Year
DOI
Venue
2018
10.1109/ISCA.2018.00044
ISCA
Keywords
Field
DocType
Emerging Memory Technology, Memory Systems, Multi-core and Parallel Architectures
Checksum,Programmer,Computer science,Cache,CPU cache,Parallel computing,Write amplification,Error detection and correction,Software,Operating system,Overhead (business)
Conference
ISSN
ISBN
Citations 
1063-6897
978-1-5386-5984-7
2
PageRank 
References 
Authors
0.35
22
3
Name
Order
Citations
PageRank
Mohammad A. Alshboul1143.26
James Tuck256433.06
Yan Solihin32057111.56