MC2 Energy Consumption Model for Massively Distributed Data Parallel Training of Deep Neural Network
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摘要:
深度神经网络（deep neural network，DNN）在许多现代人工智能（artificial intelligence，AI）任务中取得了最高的精度. 近年来，使用高性能计算平台进行大规模分布式并行训练DNN越来越普遍. 能耗模型在设计和优化DNN大规模并行训练和抑制高性能计算平台过量能耗方面起着至关重要的作用. 目前，大部分的能耗模型都是从设备的角度出发对单个设备或多个设备构成的集群进行能耗建模，由于缺乏从能耗角度对分布式并行DNN应用进行分解剖析，导致罕有针对分布式DNN应用特征进行建模的能耗模型. 针对目前最常用的DNN分布式数据并行训练模式，从DNN模型训练本质特征角度出发，提出了“数据预处理（materials preprocessing）–前向与反向传播（computing）–梯度同步与更新（communicating）”三阶段MC2能耗模型，并通过在国产E级原型机天河三号上使用最多128个MT节点和32个FT节点训练经典的VGG16和ResNet50网络以及最新的Vision Transformer网络验证了模型的有效性和可靠性. 实验结果表明，MC2与真实能耗测量结果相差仅为2.84%，相较4种线性比例能耗模型以及AR，SES，ARIMA时间预测模型准确率分别提升了69.12个百分点，69.50个百分点，34.58个百分点，13.47个百分点，5.23个百分点，22.13个百分点，10.53个百分点. 通过使用的模型可以在超算平台得到DNN模型的各阶段能耗和总体能耗结果，为评估基于能耗感知的DNN大规模分布式数据并行训练及推理各阶段任务调度、作业放置、模型分割、模型裁剪等优化策略的效能提供了基础.
Abstract:Deep neural network (DNN) have achieved state-of-the-art accuracy in many modern artificial intelligence (AI) tasks. In recent years, it has become increasingly popular to use high performance computing platforms for massively distributed parallel training of DNN. Energy consumption models have been crucial in designing and optimizing DNN for massively parallel training and restraining excessive energy consumption on HPC (high performance computing) platforms. Currently, most energy consumption models model the energy consumption of a single device or a cluster of multiple devices from a hardware perspective. From an energy consumption perspective, the need for disaggregated analysis of distributed parallel DNN applications has resulted in a dearth of energy consumption models that model the characteristics of distributed DNN applications. In this paper, we propose the “materials preprocessing-computing-communicating” three-stage MC2 model from the perspective of the essential features of DNN model training for the most commonly used DNN distributed data parallel training model. The model is validated by training the classical VGG16, ResNet50 networks and the latest Vision Transformer network using up to 128 MT nodes and 32 FT nodes on the domestic E-class prototype Tianhe-3. The experimental results show that the difference between MC2 and the actual energy measurements is only 2.84%. Compared with the four linear proportional energy models and the AR, SES, and ARIMA time prediction models, the accuracy of the model proposed is improved by 69.12%, 69.50%, 34.58%, 13.47%, 5.23%, 22.13%, and 10.53%, respectively. By using the models proposed in this paper, the energy consumption of DNN models at each stage and the overall energy consumption can be obtained on a supercomputer platform, which provides a basis for evaluating the efficiency of DNN energy-aware massively distributed parallel training and inference, as well as optimizing the strategies of task scheduling, job scheduling, model partitioning, and model pruning.
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图 8 MTP 128计算、通信和数据读取时间
Figure 8. Time for MTP 128 calculation, communication, and data I/O
表 1 相关工作中的关键变量
Table 1 Key Variables in Related Work
符号 含义 W 神经网络权值 λlr 神经网络学习率 n 批大小 E 能耗 P 功率 T 时间 t 时刻 Ptotal 总功率 Pdynamic 动态功率 Pstatic 静态功率 A 每个时钟周期内的开关数 V 电压 C 电容 Istatic 漏电电流 Td 温度 Ki 技术常数 αi 某个事件发生的次数 Ci 某个事件所花费的能耗 m 处理器核数 ρ 处理器核利用率 Ps 速度为s时的处理器功率 λ 任务到达率 R 每个任务平均执行指令数目 αai 活动因子 fmaxi 处理器组件最大频率 Pidlesm 空闲流处理组件功率 EDRAMtoSRAM 从DRAM读写数据到SRAM的能耗 ESRAM SRAM实现激活和权值缓冲区的能耗 Erest TPU其余组件能耗 PDRAM 动态随机存储器（DRAM）功率 μwrite 写吞吐量 μread 读吞吐量 Eicache 指令高速缓存能耗 Edcache 数据高速缓存能耗 Ebuses 高速缓存指令开销 Epads 主存与外设间总线能耗 Emem 内存能耗 α(A) 算法A激活阶段指令周期数 R(A) 算法A激活阶段指令读次数 W(A) 算法A激活阶段指令写次数 Pcke 内存静态功率 Pstby 激活存储组件和等待指令功率 Eact 激活时能耗 Prdwr 内存读写功率 Pser 整个服务器的功率 Pcpu 服务器中CPU功率 Ccpu 每台服务器CPU的数量 Cmem 每个服务器中内存DIMMs的数量 Cd 每个服务器磁盘数量 Pd 每个磁盘的功率 (Pi,Ti) MapReduce初始化阶段 (Pm,Tm) MapReduce映射阶段 (Pshu,Tshu) MapReduce洗牌阶段 (Pr,Tr) MapReduce规约阶段 Elayer 每一层的能耗 Ecomp 计算开销 Edata 数据输入、数据输出以及卷积核的访存开销 
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表 2 天河三号原型机基本信息
Table 2 Basic Information of Tianhe-3 Prototype
类别 属性 FT-2000+ MT-2000+ 硬件 节点数 128 512 单节点核数 32 32 频率/GHz 2.4 2.0 内存容量/GB 64 16 网络互联带宽/Gbps 200 软件 操作系统 kylin 4.0-1a OS with kernel v4.4.0 文件系统 lustre MPI版本 mpich v3.2.1 编译器 gcc v4.9.1/v4.9.3 库 Boost，BLAS，openBLAS，scalapack等
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表 3 各组件功耗
Table 3 Power Consumption of Each Component
组件类型 功率/W MT-Pca 60 MT-Pci 15 MT-Pma 75 MT-Pmi 18.75 FT-Pca 50 FT-Pci 12.5 FT-Pma 46 FT-Pmi 11.5 注：Pca和Pci分别表示处理器正常工作和空闲时的开销；Pma和Pmi分别表示内存设备正常工作和空闲时的功率.
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表 4 VGG16-MT128各阶段训练时间
Table 4 Training Time of Each Stage in VGG16-MT128
类型 训练时间/s Tsetdataset 4.5603 Tloaddata 0.1891 Tpreprocess 0.7302 Tinoutmodel 0.5636 Tf 21.2910 Tb 28.6782 Tcommunication 2.1815 Tupdate 0.2425 Tadd 1.8468
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表 5 ViT-MT128各阶段训练时间
Table 5 Training Time of Each Stage in ViT-MT128
类型 训练时间/s Tsetdataset 13.6556 Tloaddata 1.0237 Tpreprocess 1.8221 Tinoutmodel 0.9213 Tf 140.0465 Tb 323.5359 Tcommunication 21.2551 Tupdate 16.7213 Tadd 20.0640
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表 6 VGG16-FT32各阶段训练时间
Table 6 Training Time of Each Stage in VGG16-FT32
类型 训练时间/s Tsetdataset 3.2940 Tloaddata 0.1932 Tpreprocess 0.4508 Tinoutmodel 0.2546 Tf 76.4447 Tb 100.1702 Tcommunication 6.3135 Tupdate 0.6579 Tadd 4.7971
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表 7 VGG MT128训练中的能耗
Table 7 Energy Consumption in VGG MT128 Training
阶段 能耗/(kW·h) 总能耗/(kW·h) MT128-数据预处理 0.0205 0.2743 MT128-前向与反向传播 0.2399 MT128-梯度同步与更新 0.0139 FT32-数据预处理 0.0023 0.1600 FT32-前向与反向传播 0.1507 FT32-梯度同步与更新 0.0070
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表 8 ViT MT128训练中的能耗
Table 8 Energy Consumption in ViT MT128 Training
类别 能耗/(kW·h) Esetdataset 0.065546674 Eloaddata 0.003275884 Epreprocess 0.00874608 Einoutmodel 0.00294816 Ef 0.672223157 Eb 1.552972412 Ecommunication 0.10202448 Eupdate 0.080256 Etotal 2.5120
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表 9 MT128 ResNet50各阶段训练时间
Table 9 Training Time of Each Stage in MT128 ResNet50
阶段 训练时间/min Tsetdataset 40.1352 Tloaddata 6.0920 Tpreprocess 4.3322 Tinoutmodel 2.2662 Tf 115.5126 Tb 157.6198 Tcommunication 13.0800 Tupdate 4.9034 Tadd 17.9000
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表 10 ResNet50训练能耗
Table 10 Energy Consumption in ResNet50 Training
阶段 能耗/(kW·h) MT128-数据预处理 6.28 MT128-前向与反向传播 64.69 MT128-梯度同步与更新 6.47 MT128-总能耗 77.44
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表 11 能耗模型对比
Table 11 Comparison of Energy Consumption Model
类别 速度 成本 部署难易程度 准确性 测量方法 慢 高 困难 高 CPU估算 慢 高 简单 低 MC2 快 低 中等 较高
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