Big data processing requires good system implementation. Emerging non-volatile memory (NVM) is considered as a key enabler for big data systems because it offers non-volatility, byte-addressability and fast access at the same time. To make the best use of these properties, programs should access NVM directly through CPU load and store instructions. To this end, durable transactions become a common choice of applications for accessing persistent memory data in a crash consistent manner. On the other side, In-Memory cluster Computing (IMC) frameworks such as Spark achieve much higher performance than traditional On-Disk cluster Computing (ODC), e.g., MapReduce/Hadoop and Dryad, for iterative and interactive applications.
Our research group made research contributions to improve the efficiency of big data and NVM systems. DudeTM [ASPLOS'17] is a crash-consistent durable transaction system that avoids the drawbacks of both undo logging and redo logging. DudeTM uses shadow DRAM to decouple the execution of a durable transaction into three fully asynchronous steps. This design also enables an out-of-the-box transactional memory (TM) to be used as an independent component in our system. DAC [ASPLOS'18] is a datasize-aware auto-tuning approach to efficiently identify the high dimensional configuration for the in-memory cluster computing framework to achieve optimal performance on a given cluster.
A Time-Space Sharing Selected Scheduling Abstraction for Next Generation of Shared Cloud via Vertical Labels
Yuzhao Wang, Lele Li, You Wu, Junqing Yu, Zhibin Yu, Xuehai Qian
ISCA'19The 46th International Symposium on Computer Architecture, 2019
CounterMiner: Mining Big Performance Data from Hardware Counters
Yirong Lv, Bin Sun, Qinyi Luo, Zhibin Yu, Xuehai Qian
The 51st IEEE/ACM International Symposium on Microarchitecture
DudeTx: Durable Transactions Made Decoupled
Mengxing Liu, Mingxing Zhang, Kang Chen, Xuehai Qian, Yongwei Wu, Weimin Zheng, Jinglei Ren
ACM Transaction on Storage, 2018
DAC: Data-Aware Auto-Tuning High Dimensional Configurations of In-Memory Cluster Computing.
Zhibin Yu, Zhendong Bei, Xuehai Qian
ASPLOS'18 The 23rd ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2018
DudeTM: Building Durable Transactions with Decoupling for Persistent Memory
Mengxing Liu, Mingxing Zhang, Kang Chen, Xuehai Qian, Yongwei Wu, Weimin Zheng and Jinglei Ren
ASPLOS'17 The 22nd ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2017