More Like this

1. Distributed Data Persistency

   https://doi.org/10.1145/3466752.3480060  

   Kokolis, Apostolos; Psistakis, Antonis; Reidys, Benjamin; Huang, Jian; Torrellas, Josep (October 2021, MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture)

   null (Ed.)
2. Distributed Data Persistency

   https://doi.org/10.1109/MM.2022.3162183  

   Kokolis, Apostolos; Psistakis, Antonis; Reidys, Benjamin; Huang, Jian; Torrellas, Josep (July 2022, IEEE Micro)
3. Lazy Persistency: A High-Performing and Write-Efficient Software Persistency Technique

   https://doi.org/10.1109/ISCA.2018.00044  

   Alshboul, Mohammad; Tuck, James; Solihin, Yan (June 2018, International Symposium on Computer Architecture)

   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. 

   more » « less
4. Yashme: detecting persistency races

   https://doi.org/10.1145/3503222.3507766  

   Gorjiara, Hamed; Xu, Guoqing Harry; Demsky, Brian (January 2022, ASPLOS)
5. Exploring Memory Persistency Models for GPUs

   https://doi.org/10.1109/PACT.2019.00032  

   Lin, Zhen; Alshboul, Mohammad; Solihin, Yan; Zhou, Huiyang (September 2019, 2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT))

   null (Ed.)