<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multi-Core CPU on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/multi-core-cpu/</link><description>Recent content in Multi-Core CPU on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://arshadhs.github.io/tags/multi-core-cpu/index.xml" rel="self" type="application/rss+xml"/><item><title>Parallel Programming Models</title><link>https://arshadhs.github.io/docs/ai/038-ml-system-optimisation/030-parallel-programming-models/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/038-ml-system-optimisation/030-parallel-programming-models/</guid><description>&lt;h1 id="parallel-programming-models">
 Parallel Programming Models
 
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&lt;p>Parallel algorithms need hardware that can execute independent work efficiently. Modern systems therefore combine multiple CPU cores, memory hierarchies, threads, instruction pipelines, GPUs, clusters, and specialised matrix processors.&lt;/p>
&lt;p>This page covers:&lt;/p>
&lt;ul>
&lt;li>multi-core CPU organisation&lt;/li>
&lt;li>cache and memory hierarchy&lt;/li>
&lt;li>processes, threads, scheduling, and synchronisation&lt;/li>
&lt;li>instruction pipelining and clock-cycle time&lt;/li>
&lt;li>SIMD, MIMD, and SIMT execution&lt;/li>
&lt;li>GPGPU architecture and GPU memory behaviour&lt;/li>
&lt;li>CPU-only and GPU-accelerated clusters&lt;/li>
&lt;li>Tensor Processing Units and systolic arrays&lt;/li>
&lt;/ul>
&lt;h2 id="learning-objectives">
 Learning Objectives
 
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&lt;p>By the end of this page, you should be able to:&lt;/p></description></item></channel></rss>