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	<title>性能 - 四号程序员</title>
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		<title>RK系列芯片在主流AI模型上的性能对比</title>
		<link>https://www.coder4.com/archives/8152</link>
					<comments>https://www.coder4.com/archives/8152#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Fri, 10 May 2024 07:30:25 +0000</pubDate>
				<category><![CDATA[心情随笔]]></category>
		<category><![CDATA[ai模型]]></category>
		<category><![CDATA[rk]]></category>
		<category><![CDATA[rknn]]></category>
		<category><![CDATA[性能]]></category>
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					<description><![CDATA[来源：https://www.scensmart.com/news/comparison-of-ai-model-performance-of-rockchip-mainstream-socs-such-as-rk3588-rk3576-rk3568-rv1126-etc/ [......] 继续阅读]]></description>
		
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		<title>puppeteer如何做性能分析</title>
		<link>https://www.coder4.com/archives/7888</link>
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		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Thu, 17 Aug 2023 01:18:44 +0000</pubDate>
				<category><![CDATA[前端技术]]></category>
		<category><![CDATA[计算机技术]]></category>
		<category><![CDATA[puppeteer]]></category>
		<category><![CDATA[tracing]]></category>
		<category><![CDATA[性能]]></category>
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					<description><![CDATA[毕竟也是Chrome goto前：awaitpage.tracing.start({ path:'trace.json' }); waitxx后：awaitpage.tracing.stop(); 生成的trace.json文件，用以下3种方式分析： Chrome DevTools timeline viewer trace cafe &#160;[......] 继续阅读]]></description>
		
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		<title>性能压测时通过docker突破nofile / 端口数量的限制</title>
		<link>https://www.coder4.com/archives/7223</link>
					<comments>https://www.coder4.com/archives/7223#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Wed, 24 Feb 2021 12:32:33 +0000</pubDate>
				<category><![CDATA[Linux]]></category>
		<category><![CDATA[docker]]></category>
		<category><![CDATA[压测]]></category>
		<category><![CDATA[性能]]></category>
		<category><![CDATA[限制]]></category>
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					<description><![CDATA[服务端性能压测时，在客户端侧经常遇到两个问题： nofile超限额，对于Linux尚可调整，但是对于Mac系统调整非常难。 tcp端口数限制(3w左右，放开限制也只能到6w），这个没法调整 可以通过docker的方式，突破这两个限制 #!/bin/bash docker run -v $(pwd):/benchmark-client.bin -it ubuntu:20.04 /app/benchmark-client.bin 如上，我们启用若干个个docker，每个都[......] 继续阅读]]></description>
		
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		<title>RocketMQ性能测试结果</title>
		<link>https://www.coder4.com/archives/6428</link>
					<comments>https://www.coder4.com/archives/6428#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Fri, 29 Mar 2019 11:51:45 +0000</pubDate>
				<category><![CDATA[Java]]></category>
		<category><![CDATA[Linux]]></category>
		<category><![CDATA[ecs]]></category>
		<category><![CDATA[RocketMQ]]></category>
		<category><![CDATA[压力]]></category>
		<category><![CDATA[性能]]></category>
		<category><![CDATA[打压]]></category>
		<category><![CDATA[测试]]></category>
		<category><![CDATA[阿里云]]></category>
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					<description><![CDATA[0. 测试环境 阿里云，内存型R5，2核16G内存，5台机器。 RocketMq部署采用Docker，自己定制了镜像，参见：docker-rocketmq 1. 单机测试 单机: NameServer、Broker、Test程序都部署在一台机器上。 1.1 单机 发送线程与TPS 此时默认msgLen=100，主要看线程数的增加，对于同步发消息性能的影响。 可以看到12个线程后，TPS ~= 12K/s，之后线程数再增加，也不会有很大增长了。 我选用的R5机器，只有2[......] 继续阅读]]></description>
		
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		<title>Python Essential Reference 4th – 第11章 – 读书笔记</title>
		<link>https://www.coder4.com/archives/1573</link>
					<comments>https://www.coder4.com/archives/1573#comments</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Sun, 22 May 2011 14:54:22 +0000</pubDate>
				<category><![CDATA[Python]]></category>
		<category><![CDATA[Python Essential Reference]]></category>
		<category><![CDATA[性能]]></category>
		<category><![CDATA[测试]]></category>
		<category><![CDATA[第11章]]></category>
		<category><![CDATA[读书笔记]]></category>
		<category><![CDATA[调优]]></category>
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					<description><![CDATA[本章主要介绍测试、调试和性能调优 1、C、Java等语言，都是预编译类型，编译器会阻止大部分的错误。而对于Python来说，仅当运行时才能知道错误。因此，发现错误的过程更麻烦一些。 2、函数、类等第一行常用三个引号的字符串来写注释docstring，如下： def split(line,...): """ Split.... >>>split(...) >>>[...] """ 如上所示，doc中经常包含python交互shell的[......] 继续阅读]]></description>
		
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		<title>KVM安装Guest(CentOS+virtio)</title>
		<link>https://www.coder4.com/archives/1125</link>
					<comments>https://www.coder4.com/archives/1125#comments</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Sun, 07 Nov 2010 23:11:49 +0000</pubDate>
				<category><![CDATA[Linux]]></category>
		<category><![CDATA[网络&网络模拟]]></category>
		<category><![CDATA[CentOS]]></category>
		<category><![CDATA[Guest]]></category>
		<category><![CDATA[KVM]]></category>
		<category><![CDATA[sda]]></category>
		<category><![CDATA[Ubuntu]]></category>
		<category><![CDATA[virtio]]></category>
		<category><![CDATA[内核虚拟机]]></category>
		<category><![CDATA[安装呢]]></category>
		<category><![CDATA[性能]]></category>
		<category><![CDATA[网卡]]></category>
		<category><![CDATA[虚拟化]]></category>
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					<description><![CDATA[1、创建虚拟镜像，raw格式 [shell] #据说qow2会有问题，用virtio驱动 /usr/local/kvm/bin/qemu-img create -f raw test.img 10G [/shell] 2、安装虚拟机的Guest OS [shell] #注意，一开始我们就启用了if=virtio #因此，安装过程中会很顺利的识别出vda sudo /usr/local/kvm/bin/qemu-system-x86_64 -m 512 -boot d -driv[......] 继续阅读]]></description>
		
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		<title>Tokyo Cabinet中Bucket（桶）的设置与性能关系</title>
		<link>https://www.coder4.com/archives/815</link>
					<comments>https://www.coder4.com/archives/815#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Wed, 28 Jul 2010 10:02:40 +0000</pubDate>
				<category><![CDATA[数据库技术]]></category>
		<category><![CDATA[Bucket]]></category>
		<category><![CDATA[Tokyo Cabinet]]></category>
		<category><![CDATA[大小]]></category>
		<category><![CDATA[性能]]></category>
		<category><![CDATA[桶]]></category>
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					<description><![CDATA[关于TC中的Bucket的大小设置。 作者原文如下： Tokyo Cabinet attains improvement in retrieval by loading RAM with the whole of a bucket array. If a bucket array is on RAM, it is possible to access a region of a target record by about one path of file operations. A[......] 继续阅读]]></description>
		
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