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Concurrency The need for speed. Why concurrency? Moore’s law: 1. The number of components on a chip doubles about every 18 months 2. The speed of computation.

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Presentation on theme: "Concurrency The need for speed. Why concurrency? Moore’s law: 1. The number of components on a chip doubles about every 18 months 2. The speed of computation."— Presentation transcript:

1 Concurrency The need for speed

2 Why concurrency? Moore’s law: 1. The number of components on a chip doubles about every 18 months 2. The speed of computation doubles about every 18 months Moore’s law (version 1) continues to hold Moore’s law (version 2) ended in about 2003 But we’ve gotten to depend on faster and faster machines! In addition, Some problems are more easily and naturally solved with concurrency There are qualitative, as well as quantitative, benefits to faster programs 2

3 Terminology The terms thread and process each refer to a single sequential flow of execution Separate threads run in the same memory space, and interact by reading from and writing to shared memory locations Separate processes each have their own memory, and interact by sending messages Parallel processing: Threads running simultaneously, on different cores (processors), in the same computer “Parallelism”is essentially a hardware term. Concurrent processing: Threads running asynchronously, on one or more cores, usually in the same computer “Concurrency” is essentially a software term. 3

4 Synchronicity Synchronous processes run in parallel, such that each process “knows” how far along in the computation the other threads are Example: A vector machine, which does the same operation at the same time to every element of an array Asynchronous means that you cannot tell whether operation A in thread #1 happens before, during, or after operation B in thread #2 Asynchronous processes may be running simultaneously on different cores, or they may be sharing time on the same core Asynchronous processes may be running on distributed computers An asynchronous function call starts another function executing but does not wait for it to return The calling thread just keeps on going Some other mechanism is used for retrieving the function result later 4

5 Concurrency with shared state Shared state: Threads run in the same memory space Modifying a piece of data while another thread is attempting to read or modify it will result in inconsistent state Synchronization must be used to temporarily lock data to give one thread exclusive access to it Immutable data need not be locked Used by the C family of languages, including Java and Scala An operation, or block of code, is atomic if it happens “all at once” No other thread can access the same data while the operation is being performed In a higher-level language, there are essentially no atomic actions Synchronization is used to make actions atomic. Check-then-act is a common error pattern if(check(x)) {act(x)} where check(x) and act(x) are each synchronized Another process may modify x in the interval between checking and acting 5

6 Problems Race conditions: If two or more processes try to write to the same data space, or one tries to write and one tries to read, it is indeterminate which happens first The processes may even interleave, to produce inconsistent data Deadlock: Two or more processes are each waiting for data from the other, or are waiting for a lock held by the other. Livelock: Two or more processes each repeatedly change state in an attempt to avoid deadlock, but in so doing continue to block one another Starvation: A process never gets an opportunity to run, possibly because other processes have higher priority 6

7 Software Transactional Memory Software Transactional Memory (STM) uses shared state, but avoids locks Approach: Make a snapshot of all relevant data Perform the operation Check if the relevant data has changed since the snapshot If not, atomically replace the data If so, throw away the result and perform the computation again STM is optimistic, while locking is pessimistic We lock because the data might change as we work STM works well when the data is not likely to change STM is built in to Clojure, but available in other languages 7

8 Concurrency without shared state Actors, with message passing Processes run in disjoint memory spaces, and all communication is by sending messages from one process to another Each process has a queue of received messages This approach avoids many of the problems of the shared state model, but requires processes to be very lightweight (cheap) This is the approach used by Erlang, and subsequently by Clojure and Scala 8

9 Does concurrency help? Concurrency does not always make things faster Overhead is work to establish the concurrency that a sequential program does not need to do Threads may need to wait for a lock, processes may need to wait for a message Unless all processors get exactly the same amount of work, some will be idle Processes may have to contend for resources Shared state requires synchronization, which is expensive Some things can only be done sequentially 9

10 10 Overhead Overhead is any cost incurred by the parallel algorithm but not by the corresponding sequential algorithm Communication among threads and processes (a single thread has no other threads with which to communicate) Synchronization is when one thread or process has to wait for results or events from another thread or process Contention for a shared resource, such as memory Java’s synchronized is used to wait for a lock to become free Extra computation to combine the results of the various threads or processes Extra memory may be needed to give each thread or process the memory required to do its job Messages must be marshalled (packaged) for transmission and unmarshalled when received

11 11 Idle time Idle time results when There is a load imbalance--one process may have much less work to do than another A process must wait for access to memory or some other shared resource Data is registers is most quickly accessed Data in a cache is next most quickly accessed A level 1 cache is the fastest, but also the smallest A level 2 cache is larger, but slower Memory--RAM--is much slower Disk access is very much slower

12 12 Dependencies A dependency is when one thread or process requires the result of another thread or process Example: (a + b) * (c + d) The additions can be done in parallel The multiplication must wait for the results of the additions Of course, at this level, the hardware itself handles the parallelism Threads or processors that depend on results from other threads or processors must wait for those results

13 13 Amdahl’s law Some proportion P of a program can be made to run in parallel, while the remaining (1 - P) must remain sequential If there are N processors, then the computation can be done in (1 - P) + P/N time The maximum speedup is then 1. (1 - P) + P/N As N goes to infinity, the maximum speedup is 1/(1 - P) For example, if P = 0.75, the maximum speedup is (1/0.25), or four times

14 14 Consequences of Amdahl’s law If 75% of a process can be parallelized, and there are four processors, then the possible speedup is 1 / ((1 - 0.75) + 0.75/4) = 2.286 But with 40 processors--ten times as many--the speedup is only 1 / ((1 - 0.75) + 0.75/40) = 3.721 This has led many people (including Amdahl) to conclude that having lots of processors won’t help very much However.... For many problems, as the data set gets larger, The inherently sequential part of the program remains (fairly) constant Thus, the sequential proportion P becomes smaller So: The greater the volume of data, the more speedup we can get

15 Why functional programming? Here are the three most important reasons that functional programming is better for concurrency than imperative programming: Immutable values are automatically thread safe Why not functional programming? Functional languages—Lisp, Haskell, ML, OCaml—have long been regarded as only for ivory-tower academics Functional languages are “weird” (meaning: unfamiliar) 15

16 What’s happening now? Moore’s law has ended Instead of getting faster processors, we’re now getting more of them Consequently, parallelism, and concurrency, have become much more important Python has gotten more functional Other languages are getting more functional Microsoft is starting to promote F# (based on ML) Java 8 will have some functional features Scala is a hybrid object/functional language Scala is designed to be a “better Java” Like many new languages, Scala runs on the JVM 16

17 17 The End

18 I’m thinking of something. A. Animal B. Vegetable C. Mineral 18

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