Hot Topics on Data Center (HotDC) 2018

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Keynote Session

Accelerate Machine Intelligence: An Edge to Cloud Continuum

Hadi Esmaeilzadeh - UCSD

Background

open source: http://act-lab.org/artifacts

• Data grows at an unprecedented rate

• new landscape of computing: personalize and targeted experience for users

• growing gap between data and compute

• power/energy efficiency is a primary concern

• approximate computing

• AxGames: https://www.researchgate.net/publication/303905276_AxGames_Towards_Crowdsourcing_Quality_Target_Determination_in_Approximate_Computing

• machines learn to extract insights from data - two disjoin solutions for ml

• distrubute computer + FPGA / ASIC chips
  don‘t use vhdl / verlog language in the full stack for normal user

CoSMIC stack

how to distribute

• understanding machine learning - solving optimize problem
• abstraction between algorithm and acceleration system - parallelized stochastic gradient descent solver(to fpga gpu asic cgra xeon phi)
• leverage linearity of differentiation for distributed learning
• programming and compilation
  • build a new language for math
  • dataflow graph generation

how to design customizable accelerator

• multi-threading acceleration
• connectivity and bussing
• PE architecture - make hardware simple

how to reduce overhead of distributed coordination

specialized system software in CoSIMC

benchmarks

• 16-node CoSIMC with UltraScale+FPGA offer 18.8x speedup over 16-node spark with E3 skylake cpu
• using FPGA (66%) and software (34%) for speedup

RoboX Accelerator Architecture

DNNs tolerate low-bitwidth operations - bit-level

Making Cloud Systems Reliable and Dependable: Challenges and Opportunities

Lidong Zhou- MSRA

Background

system reliability:

• Fault Tolerance
• Redundancies
• State Machine Replication
• Paxos
• Erasure Coding

Real-World Gray Failures in Cloud

• redundancies in data center networking
  
• active device and link failure localization in data center
• NetBouncer: large-Scale path probing and diagnosis
• NetBouncer: leverage the power of scale
• root cause of the gray failure - stuck due to network issue - heart beat still normal (request stuck)
• Insight: should detect what the requesters errors
  • critical gray failure are ovserviable
  • from error handling to error reporting

Solution - Panorama

• Analysis - automatically covert a software component into an in-situ observer
• Runtime - observer send to local observation store(LOS)
  • locate ob-boundary
  • observations not always direct
  • observations split to ob-origin & ob-sink
  • match ob-origin & ob-sink
• Detect what "requesters" see
  • failure that matter are observable to requesters
  • turn error handlers into error reporters
  • enables construction of in-situ observers
  • https://github.com/ryanphuang/panorma

Reliability of Large-Scale Distributed Systems

• foundation reliability
• rethink cloud reliability: new theory & new method
  
• understand gray failure
• systematic and comprehensive observations

paper: Gray Failure: The Achilles‘ Heel of Cloud-Scale Systems

Deconstructing RDMA-enabled Distributed Transactions: Hybrid is Better!

Haibo Chen - SJTU

Background

• (Distributed) Transactions were slow
• High cost for distributed TX - Usually 10s~100s of thousands of TPS - (SIGMOD‘12)
• only 4% of wall-clock time spent in useful data processing

new features:

• RDMA: remote direct memory access
  • ultra low latency(5us)
  • ultra high throughput
    
• NVM: Non-volatile memory

An Active Line of Research of RDMA-enabled TX

• DrTM - DrTM(SOSP 2015) DrTM-R(EuroSys 2016) DrTM-B(USENIX ATC 2017)
• FaRM - FaRM-KV(NSDI 2014) FaRM-TX(SOSP 2015)
• FaSST(OSDI 2016)
• LITE(SOSP 2017)

Transaction(TX)s

• protocols - OCC,2PL,SI...
• impl on hardware devices - CX3,CX4,CX5,ROCE, one-side, two-side....
• OLTP workloads - TPC-C, TPC-E, TATP, Smallbank

Main: Use RDMA in TXs

outlet:

• RDMA primitive-level analysis
• Phase-by-phase analysis for TX
• DrTM+H: Putting it all together

content:

• phase: Exe/Val/Log/Commit
• offloading with one-side improves the performance
• one-sided primitive has good scalability on modern RNIC
• Execution framework & DrTM+H:https://github/com/SJTU-IPADS/drtmh
  

RDMA in Data Centers: from Cloud Computing to Machine Learning

Chuanxiong Guo - ByteDance

Background

• Data Center Network (DCN) offer lot services
  • single ownership
  • large scale
  • bisection bandwidth
• TCP/IP not working well
  • latency
  • bandwidth
  • processing overhead(40G) - 12% CPU at receiver & 6% CPU at sender

RDMA over Commodity Ethernet (RoCEv2)

• no CPU overhead
• single QP, 88Gb/s 1.7% CPU usage (TCP 8 connection 30-50Gb/s, client 2.6% & server 4.3% CPU)
• RoCEv2 needs a lossless ethernet network
  • PFC(priority-based flow control) hop-by-hop flow control
  • DCQCN - sender-switch-receiver (RP-CP-NP)
• the slow-receiver symptom - ToR tot NIC is 40Gb/s & NIC to server is 64Gb/s. NIC may generate large number of PFC pause frames

RDMA for DNN Training Acceleration

• understanding using DNN
• DNN Training: BP
• Distributed ML training, GPUs, with mini-batch
• RDMA acceleration : ResNet \ RNNs \ DNN (rdma performance better than tcp)

Highlighted Research Session

Congestion Control Mechanisms in Data Center Networks

Wei Bai - MSRA

DCN中实现低时延

• 排队时延 -PIAS(NSDI 2015)
• 丢包重发时延 - TLT

PIAS

• Flow completion Time (FCT)是关键问题
• 流信息不能假设为已知、可以在现有设备上快速部署
• PIAS performs Multi-level feedback queue (MLFQ) to emulate shortest job first (SJF)
• three function in pias:
  • package tagging
  • switch
  • rate control

TLT

• 同时达到Lossy & Loss-Less两种网络的好处
• using PFC to eliminate congestion packet losses
• packet loss :
  • middle - fast retransmissions
  • tail - Timeout retransmissions
  • 识别重要包, 当交换机队列超过阈值时丢掉非重要包

Understanding the challenges of Scaling Distributed DNN Training

Cheng Li - USTC

• Deep Learning growth fast
• DNN - Deep Neural Networks
• benefit: more data / bigger models / more computation
• Jeff Dean - Google

Distributed DNN

• Model or data parallelism
  • data parallelism is a primary choice
• BSP / ASP - BSP is choice (ASP可能不收敛)
  • Bulk Synchronous Parallel - 确定时间同步
  • Asynchronous Parallel
• net \ server \ other bottlenecks for parallelism
• 通过测试确定影响计算能力的制约条件
  • 数据压缩传输带来的压缩开销
• 系统设计
  • 弹性系统设计
  • 短板效应 - 最终计算速度的制约
  • 如何快速调整系统的规模等 - message bus流处理 - 用生产者消费者模型

Octopus: an RDMA-enable Distributed Persistent Memory File System

Youyou Lu - Tsinghua

• 分布式文件系统设计
• 非易失性内存 - 内存存储
• DRAM Limitations
  • Cell Density
  • Refresh - 性能/功耗
• NVDIMM内存 - 断电后存储数据
• Intel 3D Xpoint - 接近内存的延迟, 高容量, 断电非易失
• RDMA - 高性能环境下使用
• DiskGluster - latency来自于HDD | MemGluster - latency来自于软件
• RDMA-enable Distributed File System
  • shard data mamangment
  • New data flow strategies
  • Efficient RPC design
  • Concurrent control

Design

• I/O处理
  • 将所有NVMM组织为同一空间
  • 降低DFS中的数据拷贝(7次降到4次)
  • server扫描数据存储地址,client获取地址之后自己获取(将任务转嫁给client)
• Metadata RPC
• Collect-Dispatch Distributed Transaction
• 性能测试
  • 局域网服务期间测试 - 带宽可以达到网络带宽的88%
  • 在Hadoop平台下进行测试

Short Talk

Computer Organization and Design Course with FPGA Cloud, Ke Zhang (ICT, CAS)

新的技术AI \ IOT
提高新的软硬协同设计能力 - CPU\GPU\FPGA\GPU\ASIC
ZyForce平台 - 虚拟FPGA实验

ActionFlow：A Framework for Fast Multi-Robots Application Development, Jimin Han (UCAS)

国科大大四 - 2018.8开始
机器人应用快速开发

Labeled Network Stack, Yifan Shen (ICT, CAS)

Caching or Not: Rethinking Virtual File System for Non-Volatile Main Memory, Ying Wang (ICT, CAS)

Data Motif-based Proxy Benchmarks for Big Data and AI Workloads, Chen Zheng (ICT, CAS)

Hot Topics on Data Center (HotDC) 2018

标签：problem   get   eve   unp   git   eterm   fit   one   growth   

原文地址：https://www.cnblogs.com/tinoryj/p/Hot-Topics-on-Data-Center-HotDC-2018.html