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250022716
Optimization of SVD over GPGPU using OpenCL (Paperback)
Item #:
250022716
UGX 334644
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This book proposes parallelization of SVD modules in LAPACK over GPGPU using OpenCL, which is platform independent and focuses on routines beyond BLAS.
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What Stands Out
GPGPU Acceleration
Optimizes singular value decomposition (SVD) processes using General-Purpose computing on Graphics Processing Units, significantly enhancing performance for large data sets compared to traditional CPU methods.
OpenCL Compatibility
Utilizes OpenCL framework, ensuring compatibility across various devices and promoting easy integration with different hardware platforms, maximizing accessibility for developers.
Performance Boost
Achieves remarkable speed improvements in computational tasks, enabling faster analysis and real-time processing, appealing to researchers and professionals in data analytics and heavy computational fields.
Product Details
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Publication date | June, 2013 |
| Pages | 92 |
| Edition | Standard Edition |
| Publisher | KS Omniscriptum Publishing |
| Original languages | English |
| Language | English |
| Edu focus | Engineering |
| Assembled product height | 9 in |
| Assembled product weight | 0.32 lb (150 grams) |
| Bisac subject heading | Computers |
Product Description
Optimization of SVD over GPGPU using OpenCL (Paperback)
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Nadeem Akhtar; Shazeb Nawaz Khan All Books Editorial Review
Optimization of SVD over GPGPU using OpenCL (Paperback) is a valuable non-fiction resource published in June 2013 by KS Omniscriptum Publishing. This book spans 92 pages and is written in English, focusing on engineering applications, particularly in computational algorithms. It provides in-depth insights into optimizing Singular Value Decomposition (SVD) leveraging GPGPU capabilities through OpenCL, catering to engineering professionals and students alike. Readers appreciate the structured approach and clarity of concepts, making it easier to grasp complex topics. The book's paperback format adds convenience for on-the-go reading and reference.
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Pros
- Focused on engineering applications of SVD
- Clear and structured explanations
- Suitable for both students and professionals
- Compact size for easy handling
- Published by a reputable publisher
Cons
- Might not have extensive coverage of advanced topics
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Features & Benefits
- Unlock powerful linear algebra with Singular Value Decomposition.
- Optimize your computations through parallelization over GPGPU.
- Utilize OpenCL for platform-independent performance enhancements.
- Improve efficiency with robust SVD modules in LAPACK.
- Focus on advanced routines that go beyond traditional BLAS.
- Transform engineering applications with cutting-edge computing techniques.
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