<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Latency on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/latency/</link><description>Recent content in Latency on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://arshadhs.github.io/tags/latency/index.xml" rel="self" type="application/rss+xml"/><item><title>ML and DL System Performance</title><link>https://arshadhs.github.io/docs/ai/038-ml-system-optimisation/010-ml-and-dl-system-performance/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/038-ml-system-optimisation/010-ml-and-dl-system-performance/</guid><description>&lt;h1 id="ml-and-dl-system-performance">
 ML and DL System Performance
 
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&lt;p>Machine learning system optimisation begins with measurement. Before changing an algorithm, adding processors, or moving work to a GPU, we need to understand &lt;strong>what is slow&lt;/strong>, &lt;strong>which resource is limiting performance&lt;/strong>, and &lt;strong>how performance changes as the workload grows&lt;/strong>.&lt;/p>
&lt;p>This page covers:&lt;/p>
&lt;ul>
&lt;li>time and space complexity&lt;/li>
&lt;li>throughput and latency&lt;/li>
&lt;li>the relationship between workload, throughput, and latency&lt;/li>
&lt;li>the main measurements used to describe system performance&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>