Fork/Join in Java

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441 Fork/Join
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Fork/join decomposition relies on parallel versions of divide-and-conquer techniques familiar in sequential algorithm design Solutions take the form:
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pseudoclass Solver { // Pseudocode // Result solve(Param problem) { if (problemsize <= BASE_CASE_SIZE) return directlySolve(problem); else { Result l, r; IN-PARALLEL { l = solve(lefthalf(problem)); r = solve(rightHalf(problem)); } return combine(l, r);
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It takes some hard work and inspiration to invent a divide-and-conquer algorithm But many common computationally intensive problems have known solutions of approximately this form Of course, there may be more than two recursive calls, multiple base cases, and arbitrary pre- and post-processing surrounding any of the cases Familiar sequential examples include quicksort, mergesort, and many data structure, matrix, and image processing algorithms Sequential recursive divide-and-conquer designs are easy to parallelize when the recursive tasks are completely independent; that is, when they operate on different parts of a data set (for example different sections of an array) or solve different sub-problems, and need not otherwise communicate or coordinate actions This often holds in recursive algorithms, even those not originally intended for parallel implementation Additionally, there are recursive versions of algorithms (for example, matrix multiplication) that are not used much in sequential contexts, but are more widely used on multiprocessors because of their readily parallelizable form And other parallel algorithms perform extensive transformations and preprocessing to convert problems into a form that can be solved using fork/join techniques (See Further Readings in 444) The IN-PARALLEL pseudocode is implemented by forking and later joining tasks performing the recursive calls However, before discussing how to do this, we first examine issues and frameworks that permit efficient parallel execution of recursively generated tasks 4411 Task granularity and structure Many of the design forces encountered when implementing fork/join designs surround task granularity: Maximizing parallelism In general, the smaller the tasks, the more opportunities for parallelism All other things being equal, using many fine-grained tasks rather than only a few coarse-grained tasks keeps more CPUs busy, improves load balancing, locality and scalability, decreases the percentage of time that CPUs must idly wait for one another, and leads to greater throughput Minimizing overhead Constructing and managing an object to process a task in parallel, rather than just invoking a method to process it serially, is the main unavoidable overhead associated with taskbased programming compared with sequential solutions It is intrinsically more costly to create and use task objects than to create and use stack-frames Additionally, the use of task objects can add to the amount of argument and result data that must be transmitted and can impact garbage collection All other things being equal, total overhead is minimized when there are only a few coarse-grained tasks Minimizing contention A parallel decomposition is not going to lead to much speed-up if each task frequently communicates with others or must block waiting for resources held by others Tasks should be of a size and structure that maintain as much independence as possible They should minimize (in most cases, eliminate) use of shared resources, global (static) variables, locks, and other dependencies Ideally, each task would contain simple straight-line code that runs to completion and then terminates However, fork/join designs require at least some minimal synchronization The main object that commences processing normally waits for all subtasks to finish before proceeding
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Maximizing locality Each subtask should be the only one operating on some small piece of a problem, not only conceptually but also at the level of lower-level resources and memory access patterns Refactorings that achieve good locality of reference can significantly improve performance on modern heavily cached processors When dealing with large data sets, it is not uncommon to partition computations into subtasks with good locality even when parallelism is not the main goal Recursive decomposition is often a productive way to achieve this Parallelism accentuates the effects of locality When parallel tasks all access different parts of a data set (for example, different regions of a common matrix), partitioning strategies that reduce the need to transmit updates across caches often achieve much better performance 4412 Frameworks There is no general optimal solution to granularity and related task structuring issues Any choice represents a compromise that best resolves the competing forces for the problem at hand However, it is possible to build lightweight execution frameworks that support a wide range of choices along the continuum
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Thread objects are unnecessarily heavy vehicles for supporting purely computational fork/join tasks For example, these tasks never need to block on IO, and never need to sleep They require only an operation to synchronize across subtasks Worker thread techniques discussed in 414 can be extended to construct frameworks efficiently supporting only the necessary constructs While there are several approaches, for concreteness we'll limit discussion to a framework in utilconcurrent that restricts all tasks to be subclasses of class FJTask Here is a brief sketch of principal methods More details are discussed along with examples in 4414 through 4417 abstract class FJTask implements Runnable { boolean isDone(); // True after task is run void cancel(); // Prematurely set as done void fork(); // Start a dependent task void start(); // Start an arbitrary task static void yield(); // Allow another task to run void join(); // Yield caller until done static void invoke(FJTask t); // Directly run t static void coInvoke(FJTask t, FJTask u); // Fork and join t and u static void coInvoke(FJTask[] tasks); // coInvoke all void reset(); // Clear to allow reuse }
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An associated FJTaskRunnerGroup class provides control and entry points into this framework A FJTaskRunnerGroup is constructed with a given number of worker threads that should ordinarily be equal to the number of CPUs on a system The class supports method invoke that starts up a main task, which will in turn normally create many others
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FJTasks must employ only these task control methods, not arbitrary Thread or monitor methods While the names of these operations are the same or similar to those in class Thread, their implementations are very different In particular, there are no general suspension facilities For example, the join operation is implemented simply by having the underlying worker thread run
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other tasks to completion until the target task is noticed to have completed (via isDone) This wouldn't work at all with ordinary threads, but is effective and efficient when all tasks are structured as fork/join methods These kinds of trade-offs make FJTask construction and invocation substantially cheaper than would be possible for any class supporting the full Thread interface As of this writing, on at least some platforms, the overhead of creating, running, and otherwise managing a FJTask for the kinds of examples illustrated below is only between four and ten times that of performing equivalent sequential method calls The main effect is to lessen the impact of overhead factors when making choices about task partitioning and granularity The granularity threshold for using tasks can be fairly small on the order of a few thousand instructions even in the most conservative cases without noticeably degrading performance on uniprocessors Programs can exploit as many CPUs as are available on even the largest platforms without the need for special tools to extract or manage parallelism However, success also depends on construction of task classes and methods that themselves minimize overhead, avoid contention, and preserve locality 4413 Defining tasks Sequential divide-and-conquer algorithms can be expressed as fork/join-based classes via the following steps: 1 Create a task class with: o Fields to hold arguments and results Most should be strictly local to a task, never accessed from any other task This eliminates the need for synchronization surrounding their use However, in the typical case where result variables are accessed by other tasks, they should either be declared as volatile or be accessed only via synchronized methods o A constructor that initializes argument variables o A run method that executes the reworked method code 2 Replace the original recursive case with code that: o Creates subtask objects o Forks each one to run in parallel o Joins each of them o Combines results by accessing result variables in the subtask objects 3 Replace (or extend) the original base case check with a threshold check Problem sizes less than the threshold should use the original sequential code This generalization of base case checks maintains efficiency when problem sizes are so small that task overhead overshadows potential gains from parallel execution Tune performance by determining a good threshold size for the problem at hand 4 Replace the original method with one that creates the associated task, waits it out, and returns any results (In the FJTask framework, the outermost call is performed via FJTaskRunnerGroupinvoke) 4414 Fibonacci
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We'll illustrate the basic steps with a very boring and unrealistic, but very simple classic example: recursively computing fib, the Fibonacci function This function can be programmed sequentially as:
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int seqFib(int n) { if (n <= 1) return n; else return seqFib(n-1) + seqFib(n-2); }
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This example is unrealistic because there is a much faster non-recursive solution for this particular problem, but it is a favorite for demonstrating both recursion and parallelism Because it does so little other computation, it makes the basic structure of fork/join designs easier to see, yet it generates many recursive calls at least fib(n) calls to compute fib(n) The first few values of the sequence are 0, 1, 1, 2, 3, 5, 8; fib(10) is 55; fib(20) is 6,765; fib(30) is 832,040; fib(40) is 102,334,155 Function seqFib can be transformed into a task class such as the following:
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class Fib extends FJTask { static final int sequentialThreshold = 13; // for tuning volatile int number; // argument/result Fib(int n) { number = n; } int getAnswer() { if (!isDone()) throw new IllegalStateException("Not yet computed"); return number; } public void run() { int n = number;
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