Elements of Parallel Computing (Chapman & Hall/CRC Computational Science)
An introduction to the fundamental concepts of parallel computing, emphasizing abstract algorithmic structures rather than specific hardware. It highlights how the multicore revolution and the rise of Big Data have made parallel programming essential for modern software development. The author introduces Flynn’s Taxonomy and compares SIMD, shared memory, and distributed memory machine models to illustrate how different architectures handle data and instructions. A central focus is placed on task graph models, which allow developers to visualize and analyze data dependencies and execution flow. By exploring design patterns like MapReduce, divide-and-conquer, and pipelining, the source guides readers toward a mindset of thinking in parallel to optimize performance and ensure program correctness.
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