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*,Click to edit the title text format,Click to edit the outline text format,Second Outline Level,Third Outline Level,Fourth Outline Level,Fifth Outline Level,Sixth Outline Level,Seventh Outline Level,Eighth Outline Level,Ninth Outline Level,*,Click to edit the title text format,Click to edit the outline text format,Second Outline Level,Third Outline Level,Fourth Outline Level,Fifth Outline Level,Sixth Outline Level,Seventh Outline Level,Eighth Outline Level,Ninth Outline Level,Distributed Computing Seminar,Lecture 1: Introduction to Distributed,Computing & Systems Background,Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet,Summer 2007,Except where otherwise noted, the contents of this presentation are Copyright 2007 University of Washington and are licensed under the Creative Commons Attribution 2.5 License.,Course Overview,5 lectures,1 Introduction,2 Technical Side: MapReduce & GFS,2 Theoretical: Algorithms for distributed computing,Readings + Questions nightly,Outline,Introduction to Distributed Computing,Parallel vs. Distributed Computing,History of Distributed Computing,Parallelization and Synchronization,Networking Basics,Computer Speedup,Moores Law: “,The density of transistors on a chip doubles every 18 months, for the same cost”,(1965),Image: Toms Hardware and not subject to the Creative Commons license applicable to the rest of this work.,Image: Toms Hardware,Scope of problems,What can you do with 1 computer?,What can you do with 100 computers?,What can you do with an entire data center?,Distributed problems,Rendering multiple frames of high-quality animation,Image: DreamWorks Animation and not subject to the Creative Commons license applicable to the rest of this work.,Distributed problems,Simulating several hundred or thousand characters,Happy Feet, Kingdom Feature Productions;,Lord of the Rings, New Line Cinema, neither image is subject to the Creative Commons license applicable to the rest of the work.,Distributed problems,Indexing the web (Google),Simulating an Internet-sized network for networking experiments (PlanetLab),Speeding up content delivery (Akamai),What is the key attribute that all these examples have in common?,Parallel vs. Distributed,Parallel computing can mean:,Vector processing of data,Multiple CPUs in a single computer,Distributed computing is multiple CPUs across many computers over the network,A Brief History1975-85,Parallel computing was favored in the early years,Primarily vector-based at first,Gradually more thread-based parallelism was introduced,Image: Computer Pictures Database and Cray Research Corp and is not subject to the Creative Commons license applicable to the rest of this work.,“,Massively parallel architectures” start rising in prominence,Message Passing Interface (MPI) and other libraries developed,Bandwidth was a big problem,A Brief History1985-95,A Brief History1995-Today,Cluster/grid architecture increasingly dominant,Special node machines eschewed in favor of COTS technologies,Web-wide cluster software,Companies like Google take this to the extreme,Parallelization & Synchronization,Parallelization Idea,Parallelization is “easy” if processing can be cleanly split into n units:,Parallelization Idea (2),In a parallel computation, we would like to have as many threads as we have processors. e.g., a four-processor computer would be able to run four threads at the same time.,Parallelization Idea (3),Parallelization Idea (4),Parallelization Pitfalls,But this model is too simple!,How do we assign work units to worker threads?,What if we have more work units than threads?,How do we aggregate the results at the end?,How do we know all the workers have finished?,What if the work cannot be divided into completely separate tasks?,What is the common theme of all of these problems?,Parallelization Pitfalls (2),Each of these problems represents a point at which multiple threads must communicate with one another, or access a shared resource.,Golden rule: Any memory that can be used by multiple threads must have an associated,synchronization system,!,What is Wrong With This?,Thread 1:,void foo() ,x+;,y = x;,Thread 2:,void bar() ,y+;,x+=3;,If the initial state is y = 0, x = 6, what happens after these threads finish running?,Multithreaded = Unpredictability,When we run a multithreaded program, we dont know what order threads run in, nor do we know when they will interrupt one another.,Thread 1:,void foo() ,eax = memx;,inc eax;,memx = eax;,ebx = memx;,memy = ebx;,Thread 2:,void bar() ,eax = memy;,inc eax;,memy = eax;,eax = memx;,add eax, 3;,memx = eax;,Many things that look like “one step” operations actually take several steps under the hood:,Multithreaded = Unpredictability,This applies to more than just integers:,Pulling work units from a queue,Reporting work back to master unit,Telling another thread that it can begin the “next phase” of processing,All require synchronization!,Synchronization Primitives,A,synchronization primitive,is a special shared variable that guarantees that it can only be accessed,atomically,.,Hardware support guarantees that operations on synchronization primitives only ever take one step,Semaphores,A semaphore is a flag that can be raised or lowered in one step,Semaphores were flags that railroad engineers would use when entering a shared track,Only one side of the semaphore can ever be red! (Can both be green?),Semaphores,set() and reset() can be thought of as lock() and unlock(),Calls to lock() when the semaphore is already locked cause the thread to,block,.,Pitfalls: Must “bind” semaphores to particular objects; must remember to unlock correctly,The “corrected” example,Thread 1:,void foo() ,sem.lock();,x+;,y = x;,sem.unlock();,Thread 2:,void bar() ,sem.lock();,y+;,x+=3;,sem.unlock();,Global var “Semaphore sem = new Semaphore();” guards access to x & y,Condition Variables,A condition variable notifies threads that a particular condition has been met,Inform another thread that a queue now contains elements to pull from (or that its empty request more elements!),Pitfall: What if nobodys listening?,The final example,Thread 1:,void foo() ,sem.lock();,x+;,y = x;,fooDone = true;,sem.unlock();,fooFinishedCV.notify();,Thread 2:,void bar() ,sem.lock();,if(!fooDone) fooFinishedCV.wait(sem);,y+;,x+=3;,sem.unlock();,Global vars: Semaphore sem = new Semaphore(); ConditionVar fooFinishedCV = new ConditionVar(); boolean fooDone = false;,Too Much Synchronization? Deadlock,Synchronization becomes even more complicated when multiple locks can be used,Can cause entire system to “get stuck”,Thread A:,semaphore1.lock();,semaphore2.lock();,/* use data guarded by,semaphores */,semaphore1.unlock();,semaphore2.unlock();,Thread B:,semaphore2.lock();,semaphore1.lock();,/* use data guarded by,semaphores */,semaphore1.unlock();,semaphore2.unlock();,(Image: RPI CSCI.4210 Operating Systems notes),The Moral: Be Careful!,Synchronization is hard,Need to consider all possible shared state,Must keep locks organized and use them consistently and correctly,Knowing there are bugs may be tricky; fixing them can be even worse!,Keeping shared state to a minimum reduces total system complexity,Fundamentals of Networking,Sockets: The Internet = tubes?,A socket is the basic network interface,Provides a two-way “pipe” abstraction between two applications,Client creates a socket, and connects to the server, who receives a socket representing the other side,Ports,Within an IP address, a,port,is a sub-address identifying a listening program,Allows multiple clients to connect to a server at once,What makes this work?,Underneath the socket layer are several more protocols,Most important are TCP and IP (which are used hand-in-hand so often, theyre often spoken of as one protocol: TCP/IP),Even more low-level protocols handle how data is sent over Ethernet wires, or how bits are sent through the air using 802.11 wireless,Why is This Necessary?,Not actually tube-like “underneath the hood”,Unlike phone system (circuit switched), the,packet switched,Internet uses many routes at once,Networking Issues,If a party to a socket disconnects, how much data did they receive?,Did they crash? Or did a machine in the middle?,Can someone in the middle intercept/modify our data?,Traffic congestion makes switch/router topology important for efficient throughput,Conclusions,Processing more data means using more machines at the same time,Cooperation between processes requires synchronization,Designing real distributed systems requires consideration of networking topology,Next time: How MapReduce works,
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