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Reading List
  • Starting point
  • Reference list
  • PhD application guidelines
  • Big Data System
    • Index
      • Architecture
        • Storage
          • Sun's Network File System (NFS)
      • Execution Engine, Resource Negotiator, Schedulers
        • Execution Engines
        • Resource Negotiator
        • Schedulers
      • Machine Learning
      • SQL Framework
      • Stream Processing
      • Graph Processing
      • Potpourri: Hardware, Serverless and Approximation
  • Operating System
    • Index
      • OSTEP
        • Virtualization
          • CPU Abstraction: the Process
          • Interlude: Process API
          • Mechanism: Limited Direct Execution
        • Intro
  • Networking
    • Index
      • CS 294 (Distributed System)
        • Week 1 - Global State and Clocks
          • Distributed Snapshots: Determining Global States of Distributed Systems
          • Time, Clocks, and the Ordering of Events in a Distributed System
        • Weak 5 - Weak Consistency
          • Dynamo: Amazon's Highly Available Key-value Store
          • Replicating Data Consistency Explained Through Baseball
          • Managing update conflicts in Bayou, a weakly connected replicated storage system
      • CS 268 (Adv Network)
        • Intro
        • Internet Architecture
          • Towards an Active Network Architecture
          • The Design Philosophy of the DARPA Internet Protocols
        • Beyond best-effort/Unicast
          • Core Based Trees (CBT)
          • Multicast Routing in Internetworks and Extended LANs
        • Congestion Control
        • SDN
          • ONIX: A Distributed Control Platform for Large-scale Production Networks
          • B4: Experience with a Globally-Deployed Software Defined WAN
          • How SDN will shape networking
          • The Future of Networking, and the Past of Protocols
        • Datacenter Networking
          • Fat tree
          • Jellyfish
        • BGP
          • The Case for Separating Routing from Routers
        • Programmable Network
          • NetCache
          • RMT
        • Datacenter Congestion Control
          • Swift
          • pFabric
        • WAN CC
          • Starvation (Sigcomm 22)
        • P2P
          • Design and Evaluation of IPFS: A Storage Layer for the Decentralized Web
          • The Impact of DHT Routing Geometry on Resilience and Proximity
        • Net SW
          • mTCP
          • The Click modular router
        • NFV
          • Performance Interfaces for Network Functions
          • Making Middleboxes Someone Else's Problem: Network Processing as a Cloud Service
        • Ethics
          • On the morals of network research and beyond
          • The collateral damage of internet censorship by DNS injection
          • Encore: Lightweight Measurement of Web Censorship with Cross-Origin Requests
        • Low Latency
          • Aquila: A unified, low-latency fabric for datacenter networks
          • cISP: A Speed-of-Light Internet Service Provider
        • Disaggregation
          • Network Requirements for Resource Disaggregation
        • Tenant Networking
          • Invisinets
          • NetHint: While-Box Networking for Multi-Tenant Data Centers
        • Verification
          • A General Approach to Network Configuration Verification
          • Header Space Analysis: Static Checking for Networks
        • ML
          • SwitchML
          • Fast Distributed Deep Learning over RDMA
      • Computer Networking: A Top-Down Approach
        • Chapter 1. Computer Network and the Internet
          • 1.1 What Is the Internet?
          • 1.2 The Network Edge
          • 1.3 The Network Core
        • Stanford CS144
          • Chapter 1
            • 1.1 A Day in the Life of an Application
            • 1.2 The 4-Layer Internet Model
            • 1.3 The IP Service Model
            • 1.4 A Day in the Life of a Packet
            • 1.6 Layering Principle
            • 1.7 Encapsulation Principle
            • 1.8 Memory layout and Endianness
            • 1.9 IPv4 Addresses
            • 1.10 Longest Prefix Match
            • 1.11 Address Resolution Protocol (ARP)
            • 1.12 The Internet and IP Recap
      • Reading list
        • Elastic hyperparameter tuning on the cloud
        • Rethinking Networking Abstractions for Cloud Tenants
        • Democratizing Cellular Access with AnyCell
        • Dagger: Efficient and Fast RPCs in Cloud Microservices in Near-Memory Reconfigurable NICs
        • Sage: Practical & Scalable ML-Driven Performance Debugging in Microservices
        • Faster and Cheaper Serverless Computing on Harvested Resources
        • Network-accelerated Distributed Machine Learning for Multi-Tenant Settings
        • User-Defined Cloud
        • LegoOS: A Disseminated Distributed OS for Hardware Resource Disaggregation
        • Beyond Jain's Fairness Index: Setting the Bar For The Deployment of Congestion Control Algorithms
        • IncBricks: Toward In-Network Computation with an In-Network Cache
  • Persistence
    • Index
      • Hardware
        • Enhancing Lifetime and Security of PCM-Based Main Memory with Start-Gap Wear Leveling
        • An Empirical Guide to the Behavior and Use of Scalable Persistent Memory
  • Database
    • Index
  • Group
    • WISR Group
      • Group
        • Offloading distributed applications onto smartNICs using iPipe
        • Semeru: A memory-disaggregated managed runtime
      • Cache
        • Index
          • TACK: Improving Wireless Transport Performance by Taming Acknowledgements
          • LHD: Improving Cache Hit Rate by Maximizing Hit Density
          • AdaptSize: Orchestrating the Hot Object Memory Cache in a Content Delivery Network
          • Clustered Bandits
          • Important Sampling
          • Contexual Bandits and Reinforcement Learning
          • Reinforcement Learning for Caching with Space-Time Popularity Dynamics
          • Hyperbolic Caching: Flexible Caching for Web Applications
          • Learning Cache Replacement with CACHEUS
          • Footprint Descriptors: Theory and Practice of Cache Provisioning in a Global CDN
      • Hyperparam Exploration
        • Bayesian optimization in cloud machine learning engine
    • Shivaram's Group
      • Tools
      • Group papers
        • PushdownDB: Accelerating a DBMS using S3 Computation
        • Declarative Machine Learning Systems
        • P3: Distributed Deep Graph Learning at Scale
        • Accelerating Graph Sampling for Graph Machine Learning using GPUs
        • Unicorn: A System for Searching the Social Graph
        • Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless
        • Garaph: Efficient GPU-accelerated GraphProcessing on a Single Machine with Balanced Replication
        • MOSAIC: Processing a Trillion-Edge Graph on a Single Machine
        • Fluid: Resource-aware Hyperparameter Tuning Engine
        • Lists
          • Wavelet: Efficient DNN Training with Tick-Tock Scheduling
          • GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability
          • ZeRO-Infinity and DeepSpeed: Unlocking unprecedented model scale for deep learning training
          • ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning
          • KungFu: Making Training inDistributed Machine Learning Adaptive
        • Disk ANN
      • Queries Processing
        • Building An Elastic Query Engine on Disaggregated Storage
        • GRIP: Multi-Store Capacity-Optimized High-Performance NN Search
        • Milvus: A Purpose-Built Vector Data Management System
        • Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
        • Billion-scale Approximate Nearest Neighbor Search
        • DiskANN: Fast accurate billion-point nearest neighbor search on a single node
        • KGvec2go - Knowledge Graph Embeddings as a Service
    • Seminar & Talk
      • Berkeley System Seminar
        • RR: Engineering Record and Replay for Deployability
        • Immortal Threads: Multithreaded Event-driven Intermittent Computing on Ultra-Low-Power Microcontroll
      • Berkeley DB Seminar
        • TAOBench: An End-to-End Benchmark for Social Network Workloads
      • PS2
      • Sky Seminar Series
        • Spring 23
          • Next-Generation Optical Networks for Emerging ML Workloads
      • Reading List
        • Confluo: Distributed Monitoring and Diagnosis Stack for High-speed Networks
        • Rearchitecting Linux Storage Stack for µs Latency and High Throughput
        • eBPF: rethinking the linux kernel
        • BPF for Storage: An Exokernel-Inspired Approach
        • High Velocity Kernel File Systems with Bento
        • Incremental Path Towards a Safe OS Kernel
        • Toward Reconfigurable Kernel Datapaths with Learned Optimizations
        • A Vision for Runtime Programmable Networks
        • The Demikernel and the future of kernal-bypass systems
        • Floem: A programming system for NIC-accelerated network applications
        • High Performance Data Center Operating Systems
        • Leveraging Service Meshes as a New Network Layer
        • Automatically Discovering Machine Learning Optimizations
        • Beyond Data and Model Parallelism for Deep Neural Networks
        • IOS: Inter-Operator Scheduler for CNN Acceleration
        • Building An Elastic Query Engine on Disaggregated Storage
        • Sundial: Fault-tolerant Clock Synchronization for Datacenters
        • MIND: In-Network Memory Management for Disaggregated Data Centers
        • Understanding host network stack overheads
        • From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers
        • Redesigning Storage Systems for Future Workloads Hardware and Performance Requirements
        • Are Machine Learning Cloud APIs Used Correctly?
        • Fault-tolerant and transactional stateful serverless workflows
      • Reading Groups
        • Network reading group
          • Recap
          • ML & Networking
            • Video Streaming
              • Overview
              • Reducto: On-Camera Filtering for Resource Efficient Real-Time Video Analytics
              • Learning in situ: a randomized experiment in video streaming
              • SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity
              • Neural Adaptive Video Streaming with Pensieve
              • Server-Driven Video Streaming for Deep Learning Inference
            • Congestion Control
              • ABC: A Simple Explicit Congestion Controller for Wireless Networks
              • TCP Congestion Control: A Systems Approach
                • Chapter 1: Introduction
              • A Deep Reinforcement Learning Perspective on Internet Congestion Control
              • Pantheon: the training ground for Internet congestion-control research
            • Other
              • On the Use of ML for Blackbox System Performance Prediction
              • Marauder: Synergized Caching and Prefetching for Low-Risk Mobile App Acceleration
              • Horcrux: Automatic JavaScript Parallelism for Resource-Efficient Web Computation
              • Snicket: Query-Driven Distributed Tracing
            • Workshop
          • Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities
        • DB reading group
          • CliqueMap: Productionizing an RMA-Based Distributed Caching System
          • Hash maps overview
          • Dark Silicon and the End of Multicore Scaling
        • WISR
          • pFabric: Minimal Near-Optimal Datacenter Transport
          • Scaling Distributed Machine Learning within-Network Aggregation
          • WCMP: Weighted Cost Multipathing for Improved Fairness in Data Centers
          • Data center TCP (DCTCP)
      • Wisconsin Seminar
        • Enabling Hyperscale Web Services
        • The Lottery Ticket Hypothesis
        • External Merge Sort for Top-K Queries: Eager input filtering guided by histograms
      • Stanford MLSys Seminar
        • Episode 17
        • Episode 18
  • Cloud Computing
    • Index
      • Cloud Reading Group
        • Owl: Scale and Flexibility in Distribution of Hot Contents
        • RubberBand: cloud-based hyperparameter tuning
  • Distributed System
    • Distributed Systems Lecture Series
      • 1.1 Introduction
  • Conference
    • Index
      • Stanford Graph Learning Workshop
        • Overview of Graph Representation Learning
      • NSDI 2022
      • OSDI 21
        • Graph Embeddings and Neural Networks
        • Data Management
        • Storage
        • Preview
        • Optimizations and Scheduling for ML
          • Oort: Efficient Federated Learning via Guided Participant Selection
          • PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections
      • HotOS 21
        • FlexOS: Making OS Isolation Flexible
      • NSDI 21
        • Distributed System
          • Fault-Tolerant Replication with Pull-Based Consensus in MongoDB
          • Ownership: A Distributed Futures System for Fine-Grained Tasks
          • Caerus: NIMBLE Task Scheduling for Serverless Analytics
          • Ship Computer or Data? Why not both?
          • EPaxos Revisited
          • MilliSort and MilliQuery: Large-Scale Data-Intensive Computing in Milliseconds
        • TEGRA: Efficient Ad-Hoc Analytics on Evolving Graphs
        • GAIA: A System for Interactive Analysis on Distributed Graphs Using a High-Level Language
      • CIDR 21
        • Cerebro: A Layered Data Platform for Scalable Deep Learning
        • Magpie: Python at Speed and Scale using Cloud Backends
        • Lightweight Inspection of Data Preprocessingin Native Machine Learning Pipelines
        • Lakehouse: A New Generation of Open Platforms that UnifyData Warehousing and Advanced Analytics
      • MLSys 21
        • Chips and Compilers Symposium
        • Support sparse computations in ML
      • SOSP 21
        • SmartNic
          • LineFS: Efficient SmartNIC offload of a distributed file system with pipeline parallelism
          • Xenic: SmartNIC-accelerated distributed transacitions
        • Graphs
          • Mycelium: Large-Scale Distributed Graph Queries with Differential Privacy
          • dSpace: Composable Abstractions for Smart Spaces
        • Consistency
          • Efficient and Scalable Thread-Safety Violation Detection
          • Understanding and Detecting Software Upgrade Failures in Distributed Systems
        • NVM
          • HeMem: Scalable Tiered Memory Management for Big Data Applications and Real NVM
        • Learning
          • Bladerunner: Stream Processing at Scale for a Live View of Backend Data Mutations at the Edge
          • Faster and Cheaper Serverless Computing on Harvested Resources
  • Random
    • Reading List
      • Random Thoughts
      • Hesse
      • Anxiety
  • Grad School
    • Index
      • Resources for undergraduate students
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  1. Random
  2. Reading List

Anxiety

Coping with Anxiety

和焦虑已经是好多年的朋友啦,长久以来,一直靠这个朋友推着我走路,活得仿佛像个巨婴。这几个月以来,我的情况越来越严重,所以我越来越饥渴地寻求出路。感谢所有在艰难时刻仍然支持我的老师,组里的学长学姐,朋友,室友 (cc. PP, psychology expert!),也感谢我自己一直没有放弃。每天读的这些书对我来说都是意义非凡的。这本书在我最最支撑不下去的时候帮到我很多,希望也能帮到那些需要帮助的人,如果他们在读的话。

你非常辛苦了,已经做得很好了,不是你的错。但你是唯一那个对自己生活负有责任的人,不要放弃,勇敢一点去面对!:)

《反焦虑思维》

获得适当的精神状态 -- 不管是勇气,好奇,还是坚信 -- 是战胜所有逆境的前提条件。

Power Thought: 通过大脑练习,你能够改变你的精神状态,从而获得更多积极的行为,想法,情绪和态度的模式。

A sense of self: 自我感稳定

Power Thought 心理控制力

  1. 把注意力从那些令你压抑和焦虑的事情上移开

    1. 麻烦,焦虑,反刍思维 --> 大脑在神经通路中进行标记。在过去的某件事上反复纠结,试图找出你还能做的事情,这样只会让那些神经通道更加活跃,让你困在同样的思维模式里。

    2. 无反应的思维状态

    3. 大脑电波

    4. 练习:侧卧,半个鼻孔呼吸

  2. 关注大图像

    1. 运用周边视觉

      1. 关注前方,注意力关注周边情况,试着回想焦虑的事情

      2. 关注点在宽与窄之间来回转移,从狭窄转变到分散性时,能获得平静,融入平静,感到更安全,平和

  3. 改变语言

    1. 积极的词汇来称呼它

    2. “威胁论思想”转变成一种挑战心态

  4. 暖起来,静下去

    1. 练习:让双手变暖,想象在海滩上或壁炉前,想象血液流经手臂,涌向双手

    2. 不要通过搓动双手让手变暖,靠自己的精神力

  5. 散步或转动眼球

  6. 学会装腔作势

    1. 训练大脑,让自己进入英雄人物式的心态 --> 自信,力量

Mind Wondering 心智游移

  • 过度的 β 波会刺激大脑的边缘系统 (limbic system),而整个边缘系统会触发 ”逃跑,搏斗,或僵住不动” 的保护性反应。

  • 进入 α 波,它会使大脑前额皮层拥有 “思路清晰,冲动控制,和延迟欲望” 的能力。无意识是我们可以建立新连接,找到新思路和开发直觉的那部分思维。

    • 在这个状态下进行心智游移可以激发创造性思维,帮助你找到解决方案。

    • “α 波 闪烁”

    • 马赛克式决策构建过程 mosaic decision constructing process

      • 给无意识一次机会,浏览一幅由可能性的解决办法而拼凑而成的马赛克,并找到其中的最佳办法。而我们被记录在无意识中的经验则成为可能帮助自己的来源。

  • 做出那个选择,承担后果,一路向前,不回头看。

  • 超日节律 (Ultradian Rhythm)

    • 90-120 mins

    • 心智游移发生在这个循环的末端

      • 每隔45分钟,休息5分钟,将注意力放在昔日一场愉快的假期体验上。

      • 意识会不断随着千变外化的内容而移动,也会像海浪一样褪去,向外扩张,然后向内撤退。

      • 要避免

        • 精神不集中的心智游移

          • 集中精神的practice: 留意窗户的图案和对称性,数量,在观察的过程中留意你的注意力是如何变得集中和清晰的

        • 报复性的心智游移

          • 健身,运动,倾诉以缓解情绪化思维

          • "perceptual position": 三张椅子,三角形。第四张椅子放在三角形之外,每一张代表一种不同的观点。

            • 第一张:你自己的观点

            • 第二张:倾听他人并给予反馈和建议的角色

            • 第三张:对话题感兴趣,但站的很远的观察者的角色

            • 第四张:远离三张椅子,注意整个系统是如何运行,以及它是否在解决问题

    • 无思 ("Non-thinking")

      • 确定一个目标,不受结果的钳制,只是让任何可以让你实现目标的想法“浮出水面”,不加分析地将它们记下来。然后,让它们在大脑的后方慢慢酝酿,看看它们是不是最佳的解决办法。关键不是要实现目标,而是激发创造性思维。

以上对焦虑症发作初期可能是有用的。但是更有用的可能是接下来这本书:

  1. 焦虑是后天习得的行为

    1. 镜像神经元 --> 本能

  2. 不是困难比办法多,而是思考方式只能看得见困难

    1. 神经可塑性

    2. "use it or lose it" 训练大脑肌肉

  3. 转移注意力:只能推迟下一次发作,不能彻底解决问题

    1. 不去驱赶恐慌想法,而是把它控制住。

    2. 接受恐惧并不意味着从此要扮演受害者的角色,对一切逆来顺受。恐惧是潜意识的爱心提醒。接受恐惧后的患者可以开始积极地去处理诱发惊恐发作的一系列因素。

    3. “好的,我已经明白我必须要改变自己的生活,让潜意识停止一次次拉响警报”

  4. 打破引发恐惧连锁反应的关键环节

    1. 改变生活状态

    2. 与恐惧的直接交锋

根据4的两点

  1. 十句话描述理想人生 -- 为您的大脑重新编程

    1. 永久摆脱恐慌焦虑症的最佳方案是运用双重策略。一方面通过模式中断快速阻断恐慌感,另一方面是建立新的神经元联结,由此铲除恐惧产生的土壤,防止已经克服的恐慌感重新萌芽。

    2. 三分钟:不满意 v.s. 理想人生的样子

    3. 十句话描述理想人生的规则

      1. 组织句子时不要用消极负面的表达方式

        1. 不用否定句,比如“没有恐惧”,“没有忧虑”,这样会加强与惊恐发作有关的神经元联结

      2. 只使用积极正面的表达方式

        1. "我不想有恐慌感“ --> ”我既勇敢又自信“

        2. 也不能用所谓”放松“,因为它隐含着”不再紧张“的意思,用”轻快“,”快乐“类似的字眼

        3. 如果大脑每秒钟发出8万次信号去寻找让生活变得更美好更愉快的可能性,生活会有什么不同?

      3. 用现在时态去写十个句子

      4. 句子的内容要具体

      5. 句子的内容必须能够”自我实现“

        1. 个人目标的实现除了自己不依赖于任何人。任何其他人都对你的健康不负责任。

        2. 不是不想要什么,而是想要什么

    4. 每天花5分钟琢磨十句话中的一句(最好在睡前),在这个过程中轮流运用五种感官 -- 视觉,听觉,触觉,嗅觉,和味觉。五种感官不要同时使用,中间应有适当间隔。

5. 正确的思考方向

  • 企鹅🐧跑到沙漠里了,不要想它是怎么跑到沙漠里的,而是要把它尽快弄到水里去。

  • 停止探究到底是哪一次伤害或者不愉快埋下了惊恐发作的伏笔。重要的是当下,把时间和精力用于认真练习。

  • 阻碍康复的最大屏障可能是固有成见。

    • ”惊恐症绝不可能在几周之内痊愈“

    • ”必须先解决童年伤害才能摆脱惊恐发作“

  • 思考是一个听觉过程

    • 有意识地注意是什么触发了恐慌反应机制,是通过一个画面,还是某种感觉,或者内心的声音?

      • 只有思考时,会听到脑海中有声音在说话

      • 完全能控制内心的声音!不要用想法来使你绝望,消耗心理能量

  • 想象旋转木马往反方向转

  • 寻找惊恐发作的”模式中断开关“

    • 三种感官:听觉,视觉,触觉

      • 仔细考察

      • 掌握针对每种感官的模式中断技巧

  • 大脑的想法会朝一个特定方向旋转,大脑两个半球倾向于处理不同性质的感觉

  • 视觉型恐慌感的三个阻断技巧

    • 视觉推移法:把负面信息画面从”消极脑半球“推移到”积极脑半球“

    • 变焦法:已经从惊恐症发展成抑郁症的患者更容易掌控

      • 想象惊恐发作的画面,集中注意力想象这个画面在不断缩小,直到缩成一个小点,然后让脑海中迅速出现那个充满积极的画面,这个画面应当是彩色的,看上去让人心情愉快的,并且越大越好

    • 慢放法:把诱发恐慌发作的画面放慢,几秒内就可以阻断恐慌感

  • 粉碎使你一直陷入持续焦虑状态的幻想

  • 思路清晰且充满自信地迎接每天的挑战

  • 为了更加充实而愉悦地生活,放飞你的思想

  • 构建坚韧的内在生活,应对生活冲突(i.e. 不愉快的波折)

  • 学会勇敢,敢于尝试挑战那些你从未做过的事情。学会坚持,在每一次跌倒的时候都要爬起来,并且挺过了疼痛。学会如何而去冒险,哪怕知道可能会失败,也要再次尝试。学会如何面对恐惧,并战胜它们。还学会了如何遵守承诺,努力实现目标。

  • 反刍思维:更加强烈的焦虑形式,强迫性地,反复地查找令人痛苦的因素,却没有能力将注意力集中在解决方案上。

  • 无休止地纠结于过去,不断抱怨所有的不公平,并不能让你有所进步,也不会帮助你找到解决途径,更不会让你过上更加快乐的生活。打破思维模式,摆脱过去的束缚。

自己的一些想法 (from talks with senior people):

  • 独立的自我评价标准

  • 做research

    • Put things in context instead of only looking at result

    • Compare to yesterday

    • No harm asking, there are no bad questions

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