Cloud Computing Strategies - Dimitris N. Chorafas

Cloud Computing Strategies - Dimitris N. Chorafas

With an abundance of books available that cover the technical aspects of cloud computing, the time is right for a book that helps managers and CIOs understand exactly how cloud computing affects operations in their companies and departments. A guide to managing a cloud project, Cloud Computing Strategies provides the understanding required to effectively evaluate the technology and determine how it can be best applied to improve business and enhance the overall corporate strategy.

Based on extensive research, the book examines the opportunities and challenges that loom in the clouds. It explains exactly what cloud computing is, what it has to offer, and calls attention to the important issues management needs to consider before passing the point of no return regarding financial commitments. Illustrated with numerous examples and case studies, the text examines security, privacy, data ownership, and data protection in the clouds.
It also:

Explains how and why your company can benefit from Open Software and onDemand Services Identifies various cloud providers, their services, and their content as a basis for evaluating cost effectiveness Provides authoritative guidance on how to transition from legacy systems to the clouds Includes helpful tips for managing cloud vendor relationships and avoiding vendor lock-in

Whether you’re already in the clouds or just considering it, this book provides the unbiased understanding of the strengths and weaknesses of cloud computing needed to make informed decisions regarding its future in your organization. The book’s strength is that it supplies authoritative insight on everything needed to decide if you should make a transition to the clouds, and if you decide to do so, how to effectively manage relationships with your cloud providers.

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Vector Models for Data-Parallel Computing

Vector Models for Data-Parallel Computing

 

1 Introduction
1.1 Parallel Vector Models
1.2 Vector Instructions
1.3 Implementation
1.4 Summary and Roadmap
I Models

2 Parallel Vector Models
2.1 The Vector Random Access Machine
2.2 Comparison to P-RAM Models
2.3 Comparison to Circuit and Network Models
2.4 ComparisontoBit-VectorModels
2.5 Selecting Primitives
2.6 Other Issues
2.6.1 Serially Optimal Algorithms
2.6.2 SpaceComplexity
2.6.3 Equal Time Assumption
2.6.4 Do We Need the Scalar Memory?
2.7 Conclusion

3 The Scan Primitives
3.1 Why Scan Primitives?
3.2 Notation
3.3 Example: Line-of-Sight
3.4 Simple Operations
3.4.1 Example: Split Radix Sort
3.5 Segments and Segmented Scans
3.6 Allocating Elements
3.6.1 Example: Line Drawing
3.6.2 Notes on Allocating
3.7 Long Vectors and Load Balancing
3.7.1 Load Balancing
3.7.2 Example: Halving Merge
3.7.3 Notes on Simulating Long Vectors

4 The Scan Vector Model
4.1 The Scan Vector Instruction Set
4.1.1 Scalar Instructions
4.1.2 Elementwise Instructions
4.1.3 Permute Instructions
4.1.4 Scan Instructions
4.1.5 Vector-Scalar Instructions
4.2 Simple Operations
4.3 Segments and Segmented Instructions
4.4 Segmented Operations
4.5 Additional Instructions
4.5.1 Merge Instruction
4.5.2 Combine Instructions
4.5.3 Multi-Extract Instruction
4.5.4 Keyed-Scan Instructions

II Algorithms
5 Data Structures
5.1 Graphs
5.1.1 Vector Graph Representations
5.1.2 Neighbor Reducing
5.1.3 Distributing an Excess Across Edges
5.2 Trees
5.2.1 Vector Tree Representation
5.2.2 Leaffix and Rootfix Operations
5.2.3 Tree Manipulations
5.3 Multidimensional Arrays

6 Computational-Geometry Algorithms
6.1 Generalized Binary Search
6.2 Building a k-DTree
6.3 ClosestPair
6.4 Quickhull
6.5 n MergeHull
6.6 Line of Sight

7 Graph Algorithms
7.1 Minimum Spanning Tree and Connectivity
7.2 Maximum Flow
7.3 Maximal Independent Set and Biconnectivity

8 Numerical Algorithms
8.1 Matrix-Vector Multiplication
8.2 Linear-SystemsSolver
8.3 Simplex
8.4 Outer Product
8.5 Sparse-Matrix Multiplication

III Languages and Compilers
9 Collection-Oriented Languages
9.1 Collections
9.2 Collection Operations
9.3 Mapping Collections onto Vectors

10 Flattening Nested Parallelism
10.1 Nested Parallelism and Replicating
10.2 The Replicating Theorem
10.3 Access-Restricted Code
10.4 Access-Fixed Replicating Theorem
10.5 Indirect Addressing
10.6 Conditional Control
10.6.1 Branch-Packing
10.6.2 Contained Programs
10.6.3 Containment of Functions in Book
10.6.4 Round-Robin Simulation

11 A Compiler for Paralation Lisp
11.1 Source Code: Paralation Lisp
11.2 TargetCode:Scan-VectorLisp
11.3 Translation

IV Architecture
12 Implementing Parallel Vector Models
12.1 Implementation on the Connection Machine
12.1.1 The Vector Memory
12.1.2 The Instructions
12.1.3 Optimizations
12.1.4 Running Times
12.2 Simulating on P-RAM

13 Implementing the Scan Primitives
13.1 Unsigned +-Scan and Max-Scan
13.1.1 Tree Scan
13.1.2 Hardware Implementation of Tree Scan
13.1.3 An Example System
13.2 Directly Implementing Other Scans
13.2.1 Backward and Segmented Scans
13.2.2 Multidimensional Grid Scans
13.3 Floating-Point +-Scan

14 Conclusion

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