Difference between revisions of "GPU621/Group 3"
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| + | == '''Intel's Data Analytics Library for Parallel computing and Vectorization''' == | ||
| − | Introduction: | + | |
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| + | '''Introduction:''' | ||
In this project I will be using Intel's Data Analytics Library for Parallel computing and Vectorization tasks. | In this project I will be using Intel's Data Analytics Library for Parallel computing and Vectorization tasks. | ||
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| − | Data Analytics Library Overview: | + | Data Analytics Library Overview:''' |
Intel's Data Analytics Library offers a robust collection of tools and algorithms that can assist programmers in building high-performance applications tailored for Intel chips. These tools are designed to interact with various data sources, such as data stored in memory, hard disc, or distributed systems. These functions available in Intel's Data Analytics Library are usable by a broad range of developers because it supports various programming languages, such as C++, Python, and Java. | Intel's Data Analytics Library offers a robust collection of tools and algorithms that can assist programmers in building high-performance applications tailored for Intel chips. These tools are designed to interact with various data sources, such as data stored in memory, hard disc, or distributed systems. These functions available in Intel's Data Analytics Library are usable by a broad range of developers because it supports various programming languages, such as C++, Python, and Java. | ||
Data Analytics Library offers functionalities for: | Data Analytics Library offers functionalities for: | ||
Revision as of 10:59, 8 March 2023
Intel's Data Analytics Library for Parallel computing and Vectorization
Introduction: In this project I will be using Intel's Data Analytics Library for Parallel computing and Vectorization tasks.
Data Analytics Library Overview:
Intel's Data Analytics Library offers a robust collection of tools and algorithms that can assist programmers in building high-performance applications tailored for Intel chips. These tools are designed to interact with various data sources, such as data stored in memory, hard disc, or distributed systems. These functions available in Intel's Data Analytics Library are usable by a broad range of developers because it supports various programming languages, such as C++, Python, and Java.
Data Analytics Library offers functionalities for:
• Parallel computing.
• Vectorization.
• Machine learning.
• Graph analytics.
• Statistical analysis.
• Data visualization.
