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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. vb.net barcode reader usb Visual Studio .NET Barcode Scanner Library API for .NET Barcode ...
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Read & scan Linear & 2D barcode images from Visual Basic .NET? VB.NET Barcode Reader Integration Tutorial. 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 vb.net barcode reader tutorial VB.NET Barcode Reader - How to Scan & Read Barcode in VB.NET ...
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Touchless API library manager (to use it you should have TouchlessLib.dll referenced and WebCamLib.dll copied to the output directory). 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 } vb.net barcode scanner webcam How to read input from a barcode scanner in vb.net without using a ...
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