Search Results - (("parallel computation") OR ("parallel computational"))

Cloud data centers and cost modeling : a complete guide to planning, designing and building a cloud data center by Wu, Caesar, Buyya, Rajkumar

Table of Contents: “…Cloud Computing Foundations and Business Requirements; 1 Cloud Computing; 1.1 Introduction; 1.1.1 Operation Cost Rationalization; 1.1.2 Revenue Estimation for Emerging Products; 1.2 Cloud Computing at a Glance; 1.3 Right Approach to Definition; 1.4 A Brief History of Cloud Computing Definitions; 1.5 Parallel Computing; 1.5.1 Hardware Parallelism; 1.5.1.1 Processor parallelism; 1.5.1.2 Memory parallelism; 1.5.2 Software Parallelism; 1.5.2.1 Algorithm parallelism.…”
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Discrete Algorithms and Complexity : Proceedings of the Japan-US Joint Seminar, June 4-6, 1986, Kyoto, Japan.

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Multi-Objective Optimization in Theory and Practice II : metaheuristic algorithms. by Keller, André A.

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The multilevel fast multipole algorithm (MLFMA) for solving large-scale computational electromagnetics problems by Ergül, Özgür

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Machine learning in image steganalysis by Schaathun, Hans Georg

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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…<i>3</i>Big Data Analytics. 31</p> <p>1.7.4 Visualizing Big Data. 32</p> <p>1.8 Big Data Technology. 32</p> <p>1.8.1 Challenges faced by Big Data technology. 34</p> <p>1.8.1 Heterogeneity and incompleteness. 34</p> <p>1.8.2 Volume and velocity of the Data. 35</p> <p>1.8.3 Data Storage. 35</p> <p>1.8.4 Data Privacy. 36</p> <p>1.9 Big Data Applications. 36</p> <p>1.10 Big Data Use Cases. 37</p> <p>1.9. 1 Healthcare. 37</p> <p>1.9.2 Telecom.. 38</p> <p>1.9.3 Financial Services. 39</p> <p>Chapter 1 refresher: 40</p> <p>Conceptual short Questions with answers. 43</p> <p>Frequently asked Interview questions. 45</p> <p>Chapter Objective. 46</p> <p>Big Data Storage Concepts. 46</p> <p>2.1 Cluster computing. 47</p> <p>2.1.1 Types of cluster. 49</p> <p>2.1.1.1 High availability cluster. 50</p> <p>2.1.1.2 Load balancing cluster. 50</p> <p>2.1.2 Cluster structure. 51</p> <p>2.3 Distribution Models. 53</p> <p>2.3.1 Sharding. 54</p> <p>2.3.2 Data Replication. 56</p> <p>2.3.2.1 Master-Slave model 57</p> <p>2.3.2.2 Peer-to-Peer model 58</p> <p>2.3.3 Sharding and Replication. 59</p> <p>2.4 Distributed file system.. 60</p> <p>2.5 Relational and Non Relational Databases. 61</p> <p>CoursesOffered. 62</p> <p>Figure 2.12 Data divided across multiple related tables. 62</p> <p>2.4.2 RDBMS Databases. 63</p> <p>2.4.3 NoSQL Databases. 63</p> <p>2.4.4 NewSQL Databases. 64</p> <p>2.5 Scaling Up and Scaling Out Storage. 65</p> <p>Chapter 2 refresher. 67</p> <p>Conceptual short questions with answers. 69</p> <p>Chapter Objective. 72</p> <p>3.1 Introduction to NoSQL. 72</p> <p>3.2 Why NoSQL. 72</p> <p>3.3 CAP theorem.. 73</p> <p>3.4 ACID.. 75</p> <p>3.5 BASE. 76</p> <p>3.6 Schemaless Database. 77</p> <p>3.7 NoSQL (Not Only SQL) 77</p> <p>3.7.1 NoSQL Vs RDBMS. 78</p> <p>3.7.2Features of NoSQL database. 79</p> <p>3.7.3Types of NoSQL Technologies. 80</p> <p>3.7.3.1 Key-Value store database. 81</p> <p>3.7.3.2 Column-store database. 82</p> <p>3.7.3.3 Document Oriented Database. 84</p> <p>3.7.3.4 Graph-oriented Database. 86</p> <p>3.7.4 NoSQL Operations. 93</p> <p>3.9 Migrating from RDBMS to NoSQL. 98</p> <p>Chapter 3 refresher. 99</p> <p>Conceptual short questions with answers. 102</p> <p>Chapter Objective. 104</p> <p>4.1 Data Processing. 104</p> <p>4.2 Shared Everything Architecture. 106</p> <p>4.2.1 Symmetric multiprocessing architecture. 107</p> <p>4.2.2 Distributed Shared memory. 108</p> <p>4.3 Shared nothing architecture. 109</p> <p>4.4 Batch Processing. 110</p> <p>4.5 Real-Time Data Processing. 111</p> <p>4.6 Parallel Computing. 112</p> <p>4.7 Distributed Computing. 113</p> <p>4.8 Big Data Virtualization. 113</p> <p>4.8.1 Attributes of Virtualization. 114</p> <p>4.8.1.1 Encapsulation. 115</p> <p>4.8.1.2 Partitioning. 115</p> <p>4.8.1.3 Isolation. 115</p> <p>4.8.2Big Data Server Virtualization. 116</p> <p>4.9 Introduction. 116</p> <p>4.10 Cloud computing types. 118</p> <p>4.11Cloud Services. 120</p> <p>4.12 Cloud Storage. 121</p> <p>4.12.1 Architecture of GFS. 121</p> <p>4.12.1.1 Master. 123</p> <p>4.12.1.2 Client. 123</p> <p>4.13 Cloud Architecture. 127</p> <p>Cloud Challenges. 129</p> <p>Chapter 4 Refresher. 130</p> <p>Conceptual short questions with answers. 133</p> <p>Chapter Objective. 139</p> <p>5.1 Apache Hadoop. 139</p> <p>5.1.1 Architecture of Apache Hadoop. 140</p> <p>5.1.2Hadoop Ecosystem Components Overview.. 140</p> <p>5.2 Hadoop Storage. 142</p> <p>5.2.1HDFS (Hadoop Distributed File System). 142</p> <p>5.2.2Why HDFS?. …”
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Professional CUDA C programming by Cheng, John

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Topics in parallel and distributed computing : introducing concurrency in undergraduate courses

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Insect immunology

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Structured parallel programming : patterns for efficient computation by McCool, Michael D.

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The future of computing performance : game over or next level?

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Multicore Hardware-Software Design and Verification Techniques. by Hsiung, Pao-Ann

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Performance-Oriented Application Development for Distributed Architectures.

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Using OpenMP : portable shared memory parallel programming by Chapman, Barbara, 1954-

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Computational Methods for Fluid Dynamics by Ferziger, Joel H.

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Multicore computing : algorithms, architectures, and applications

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CUDA Fortran for Scientists and Engineers : Best Practices for Efficient CUDA Fortran Programming by Ruetsch, Gregory, Fatica, Massimiliano

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Parallel science and engineering applications : the Charm++ approach

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Apache Spark Machine Learning Blueprints. by Liu, Alex

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Numerical methods for linear control systems : design and analysis by Datta, Biswa Nath

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