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浙江大学学报(工学版)
浙江大学学报(工学版)  2019, Vol. 53 Issue (12): 2348-2356    DOI: 10.3785/j.issn.1008-973X.2019.12.012
计算机科学与人工智能     
基于动态任务迁移的近数据处理方法
华幸成(),刘鹏*()
浙江大学 信息与电子工程学院，浙江 杭州 310027
Near-data processing based on dynamic task offloading
Xing-cheng HUA(),Peng LIU*()
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
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摘要：

为了应对大数据应用中数据移动对系统性能和能耗造成的负面影响，基于3D存储器集成存储与逻辑电路的特点和MapReduce模型的并发特性，提出一种基于动态任务迁移的近数据处理（NDP）方法. 对MapReduce应用的工作流解耦以获取核心计算任务，提供迁移机制将计算任务动态迁移到NDP单元中；采用原子操作优化数据访问，从而大幅度减少数据移动. 实验结果表明，对于MapReduce应用，提出的近数据处理方法将75%的数据移动约束在存储单元内部，有效减少了主处理单元与存储单元之间的数据移动. 与目前最先进的工作相比，所提方法在系统性能和系统能效上分别有70%和44%的提升.
关键词： 近数据处理（NDP）;  MapReduce;  3D存储器;  动态任务迁移    
Abstract:

A near-data processing (NDP) approach based on dynamic task offloading was proposed to address the adverse effects on system performance and energy consumption incurred by data movements in big data applications. The approach leveraged the capability of 3D memory to integrate both memory and logic circuits and the data parallelization of the MapReduce model. The workflow of MapReduce workloads was decoupled to extract the key computation tasks, and the task offloading mechanism was provided to migrate the computation tasks to NDP units dynamically. Atomic operations were employed to optimize the memory accesses, thus reducing data movements dramatically. The experimental results demonstrate that for MapReduce workloads the proposed near-data processing approach restricts 75% of the data movements within the memory module, indicating that the data movements between the main memory and the host processors are significantly reduced. Compared with the state-of-the-art, the proposed approach improved system performance and energy efficiency by 70% and 44%, respectively.
Key words: near-data processing (NDP)    MapReduce    3D memory    dynamic task offloading
收稿日期: 2018-10-24 出版日期: 2019-12-17
CLC:  TP 302  
基金资助: 国家自然科学基金?广东联合基金重点资助项目（U1401253）；国家自然科学基金资助项目（61573153）；广东省应用型科技研发专项资金资助项目（2016B020243011，2016B090927007）；广东省自然科学基金资助项目（2016A030313510）
通讯作者: 刘鹏     E-mail: hua2009x@zju.edu.cn;liupeng@zju.edu.cn
作者简介: 华幸成（1990—），男，博士生，从事体系结构研究. orcid.org/0000-0003-2428-5787. E-mail： hua2009x@zju.edu.cn
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引用本文:

华幸成,刘鹏. 基于动态任务迁移的近数据处理方法[J]. 浙江大学学报(工学版), 2019, 53(12): 2348-2356.

Xing-cheng HUA,Peng LIU. Near-data processing based on dynamic task offloading. Journal of ZheJiang University (Engineering Science), 2019, 53(12): 2348-2356.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.12.012    或    http://www.zjujournals.com/eng/CN/Y2019/V53/I12/2348

图 1  近数据处理（NDP）模块结构
图 2  NDP系统中MapReduce应用的工作流程
函数 功能描述
高层API split 对输入数据进行分片
shuffle 将Map任务的输出数据地址传递给Reduce任务
map 用户定义的Map方法
reduce 用户定义的Reduce方法
map_worker 调用NDP单元，执行用户定义的Map方法
reduce_worker 调用NDP单元，执行用户定义的Reduce方法
底层API offload_kernel 将计算核写入NDP单元
offload_data 将数据传递给NDP单元
start_computation 启动计算任务
wait_for_completion 等待计算任务完成
write_reg 向NDP单元的寄存器写入数据
read_reg 从NDP单元的寄存器读取数据
表 1  MapReduce框架的应用程序编程接口（API）
图 3  NDP单元中常驻程序的运行流程
图 4  计算核提取和迁移示例
模块 配置
主处理器 2x ARM Cortex-A15 core
32 KB L1 I Cache, 64 KB L1 D Cache
2 MB L2 Cache
近数据处理单元 1x ARM Cortex-A15 core
1MB SPM, 16-Entry TLB
DMA: Burst size 256 Bytes
3D存储单元 4 layers, 16 Vaults, 512 MB
Timing（ns）：tRP = 13.75, tRCD = 13.75,
tCL = 13.75, tWR = 15, tRAS = 27.5,
tCK = 0.8, ttCCD = 5, tBURST = 3.2
表 2  近数据处理（NDP）系统的配置
图 5  NDP系统中的数据移动分布
图 6  Host和NMR系统中各个模块的能量消耗
图 7  NMR系统中Map任务和Reduce任务的加速比
图 8  Host、NDC和NMR系统的性能对比
图 9  NDP单元数量对系统性能的影响
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