Parallel pipelines

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  • This Book Is For Why This Book Is Pertinent Now What You Need to Use the Code How to Use This Book Introduction Parallelism with Control Dependencies Only Parallelism with Control and Data Dependencies Dynamic Task Parallelism and Pipelines Supporting Material What Is Not Covered Goals

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  • Parallel Processing & Distributed Systems: Lecture 4 - Parallel Computer Architectures provides about Flynn’s Taxonomy, Classification of Parallel Computers Based on Architectures, Classification based on Architecture.

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  • SOA Patterns provides architectural guidance through patterns and antipatterns. It shows you how to build real SOA services that feature flexibility, availability, and scalability. Through an extensive set of patterns, this book identifies the major SOA pressure points and provides reusable techniques to address them. Each pattern pairs the classic problem/solution format with a unique technology map, showing where specific solutions fit into the general pattern.

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  • (BQ) This new emphasis on parallelism is supported by updates reflecting the newest technologies, with examples highlighting the latest processor designs and benchmarking standards. As with previous editions, a MIPS processor is the core used to present the fundamentals of hardware technologies, assembly language, computer arithmetic, pipelining, memory hierarchies and I/O.

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  • Code Optimization • • • • • Optimization techniques for code efficiency Intrinsic C functions Parallel instructions Word-wide data access Software pipelining In this chapter we illustrate several schemes that can be used to optimize and drastically reduce the execution time of your code. These techniques include the use of instructions in parallel, word-wide data, intrinsic functions, and software pipelining. 8.1 INTRODUCTION Begin at a workstation level; for example, use C code on a PC.

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  • Greenplum’s SG Streaming™ technology ensures parallelism by “scattering” data from all source systems across hundreds or thousands of parallel streams that simultaneously flow to all Greenplum Database nodes (Figure 11). Performance scales with the number of Greenplum Database nodes, and the technology supports both large batch and continuous near-real-time loading patterns with negligible impact on concurrent database operations.

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  • A NEW CHALLENGE FOR APPLICATION DEVELOPERS Scientific and engineering applications have driven the development of high-performance computing (HPC) for several decades. Many new techniques have been developed over the years to study increasingly complex phenomena using larger and more demanding jobs with greater throughput, fidelity, and sophistication than ever before.

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  • The pipeline of most Phrase-Based Statistical Machine Translation (PB-SMT) systems starts from automatically word aligned parallel corpus. But word appears to be too fine-grained in some cases such as non-compositional phrasal equivalences, where no clear word alignments exist. Using words as inputs to PBSMT pipeline has inborn deficiency. This paper proposes pseudo-word as a new start point for PB-SMT pipeline.

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