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
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.
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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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.
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.
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.
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.