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hive编译部分的源码结构 -
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TPC-H on Hive
http://www.cloudera.com/blog/2009/12/7-tips-for-improving-mapreduce-performance/
7 Tips for Improving MapReduce Performance
- by Todd Lipcon
- December 17, 2009
- no comments
One service that Cloudera provides for our customers is help with tuning and optimizing MapReduce jobs. Since MapReduce and HDFS are complex distributed systems that run arbitrary user code, there’s no hard and fast set of rules to achieve optimal performance; instead, I tend to think of tuning a cluster or job much like a doctor would treat a sick human being. There are a number of key symptoms to look for, and each set of symptoms leads to a different diagnosis and course of treatment.
In medicine, there’s no automatic process that can replace the experience of a well seasoned doctor. The same is true with complex distributed systems — experienced users and operators often develop a “sixth sense” for common issues. Having worked with Cloudera customers in a number of different industries, each with a different workload, dataset, and cluster hardware, I’ve accumulated a bit of this experience, and would like to share some with you today.
In this blog post, I’ll highlight a few tips for improving MapReduce performance. The first few tips are cluster-wide, and will be useful for operators and developers alike. The latter tips are for developers writing custom MapReduce jobs in Java. For each tip, I’ll also note a few of the “symptoms” or “diagnostic tests” that indicate a particular remedy might bring you some good improvements.
Please note, also, that these tips contain lots of rules of thumb based on my experience across a variety of situations. They may not apply to your particular workload, dataset, or cluster, and you should always benchmark your jobs before and after any changes. For these tips, I’ll show some comparative numbers for a 40GB wordcount job on a small 4-node cluster. Tuned optimally, each of the map tasks in this job runs in about 33 seconds, and the total job runtime is about 8m30s.
Tip 1) Configure your cluster correctly
Diagnostics/symptoms:
-
top
shows slave nodes fairly idle even when all map and reduce task slots are filled up running jobs. -
top
shows kernel processes like RAID (mdX_raid*
) orpdflush
taking most of the CPU time. - Linux load averages are often seen more than twice the number of CPUs on the system.
- Linux load averages stay less than half the number of CPUs on the system, even when running jobs.
- Any swap usage on nodes beyond a few MB.
The first step to optimizing your MapReduce performance is to make sure your cluster configuration has been tuned. For starters, check out our earlier blog post on configuration parameters . In addition to those knobs in the Hadoop configuration, here are a few more checklist items you should go through before beginning to tune the performance of an individual job:
- Make sure the mounts you’re using for DFS and MapReduce storage have been mounted with the
noatime
option. This disables access time tracking and can improve IO performance. - Avoid RAID and LVM on TaskTracker and DataNode machines – it generally reduces performance.
- Make sure you’ve configured
mapred.local.dir
anddfs.data.dir
to point to one directory on each of your disks to ensure that all of your IO capacity is used. Runiostat -dx 5
from thesysstat
package while the cluster is loaded to make sure each disk shows utilization. - Ensure that you have SMART monitoring for the health status of your disk drives. MapReduce jobs are fault tolerant, but dying disks can cause performance to degrade as tasks must be re-executed. If you find that a particular TaskTracker becomes blacklisted on many job invocations, it may have a failing drive.
- Monitor and graph swap usage and network usage with software like Ganglia
. Monitoring Hadoop metrics
in Ganglia is also a good idea. If you see swap being used, reduce the amount of RAM allocated to each task in
mapred.child.java.opts
.
Benchmarks:
Unfortunately I was not able to perform benchmarks for this tip, as it would involve re-imaging the cluster. If you have had relevant experience, feel free to leave a note in the Comments section below.
Tip 2) Use LZO Compression
Diagnostics/symptoms:
- This is almost always a good idea for intermediate data! In the doctor analogy, consider LZO compression your vitamins.
- Output data size of MapReduce job is nontrivial.
- Slave nodes show high
iowait
utilization intop
andiostat
when jobs are running.
Almost every Hadoop job that generates an non-negligible amount of map output will benefit from intermediate data compression with LZO. Although LZO adds a little bit of CPU overhead, the reduced amount of disk IO during the shuffle will usually save time overall.
Whenever a job needs to output a significant amount of data, LZO
compression can also increase performance on the output side. Since
writes are replicated 3x by default, each GB of output data you save
will save 3GB of disk writes.
In order to enable LZO compression, check out our
recent guest blog from Twitter
. Be sure to set
mapred.compress.map.output
to
true
.
Benchmarks:
Disabling LZO compression on the wordcount example increased the job runtime only slightly on our cluster. The FILE_BYTES_WRITTEN
counter increased from 3.5GB to 9.2GB, showing that the compression
yielded a 62% decrease in disk IO. Since this job was not sharing the
cluster, and each node has a high ratio of number of disks to number of
tasks, IO is not the bottleneck here, and thus the improvement was not
substantial. On clusters where disks are pegged due to a lot of
concurrent activity, a 60% reduction in IO can yield a substantial
improvement in job completion speed.
Tip 3) Tune the number of map and reduce tasks appropriately
Diagnostics/symptoms:
- Each map or reduce task finishes in less than 30-40 seconds.
- A large job does not utilize all available slots in the cluster.
- After most mappers or reducers are scheduled, one or two remains pending and then runs all alone.
Tuning the number of map and reduce tasks for a job is important and easy to overlook. Here are some rules of thumb I use to set these parameters:
- If each task takes less than 30-40 seconds, reduce the number of tasks. The task setup and scheduling overhead is a few seconds, so if tasks finish very quickly, you’re wasting time while not doing work. JVM reuse can also be enabled to solve this problem.
-
If a job has more than 1TB of input, consider increasing the block
size of the input dataset to 256M or even 512M
so that the number of
tasks will be smaller. You can change the block size of existing files
with a command like
hadoop distcp -Ddfs.block.size=$[256*1024*1024] /path/to/inputdata /path/to/inputdata-with-largeblocks
. After this command completes, you can remove the original data. - So long as each task runs for at least 30-40 seconds, increase the number of mapper tasks to some multiple of the number of mapper slots in the cluster. If you have 100 map slots in your cluster, try to avoid having a job with 101 mappers – the first 100 will finish at the same time, and then the 101st will have to run alone before the reducers can run. This is more important on small clusters and small jobs.
- Don’t schedule too many reduce tasks – for most jobs, we recommend a number of reduce tasks equal to or a bit less than the number of reduce slots in the cluster.
Benchmarks:
To make the wordcount job run with too many tasks, I ran it with the argument -Dmapred.max.split.size=$[16*1024*1024]
.
This yielded 2640 tasks instead of the 360 that the framework chose by
default. When running with this setting, each task took about 9 seconds,
and watching the Cluster Summary view on the JobTracker showed the
number of running maps fluctuating between 0 and 24 continuously
throughout the job. The entire job finished in 17m52s, more than twice
as slow as the original job.
Tip 4) Write a Combiner
Diagnostics/symptoms:
- A job performs aggregation of some sort, and the Reduce input groups counter is significantly smaller than the Reduce input records counter.
- The job performs a large shuffle (e.g. map output bytes is multiple GB per node)
- The number of spilled records is many times larger than the number of map output records as seen in the Job counters.
If your algorithm involves computing aggregates of any sort, chances are you can use a Combiner in order to perform some kind of initial aggregation before the data hits the reducer. The MapReduce framework runs combiners intelligently in order to reduce the amount of data that has to be written to disk and transfered over the network in between the Map and Reduce stages of computation.
Benchmarks:
I modified the word count example to remove the call to setCombinerClass
,
and otherwise left it the same. This changed the average map task run
time from 33s to 48s, and increased the amount of shuffled data from 1GB
to 1.4GB. The total job runtime increased from 8m30s to 15m42s, nearly a
factor of two. Note that this benchmark was run with map output
compression enabled – without map output compression, the effect of the
combiner would have been even more important.
Tip 5) Use the most appropriate and compact Writable type for your data
Symptoms/diagnostics:
-
Text
objects are used for working with non-textual or complex data -
IntWritable
orLongWritable
objects are used when most output values tend to be significantly smaller than the maximum value.
When users are new to programming in MapReduce, or are switching from Hadoop Streaming to Java MapReduce, they often use the Text
writable type unnecessarily. Although Text
can be convenient, converting numeric data to and from UTF8 strings is
inefficient and can actually make up a significant portion of CPU time.
Whenever dealing with non-textual data, consider using the binary
Writables like IntWritable
, FloatWritable
, etc.
In addition to avoiding the text parsing overhead, the binary
Writable types will take up less space as intermediate data. Since disk
IO and network transfer will become a bottleneck in large jobs, reducing
the sheer number of bytes taken up by the intermediate data can provide
a substantial performance gain. When dealing with integers, it can also
sometimes be faster to use VIntWritable
or VLongWritable
— these implement variable-length integer encoding which saves space
when serializing small integers. For example, the value 4 will be
serialized in a single byte, whereas the value 10000 will be serialized
in two. These variable length numbers can be very effective for data
like counts, where you expect that the majority of records will have a
small number that fits in one or two bytes.
If the Writable types that ship with Hadoop don’t fit the bill,
consider writing your own. It’s pretty simple, and will be significantly
faster than parsing text. If you do so, make sure to provide a RawComparator
— see the source code for the built in Writables for an example.
Along the same vein, if your MapReduce job is part of a multistage workflow, use a binary format like SequenceFile
for the intermediate steps, even if the last stage needs to output
text. This will reduce the amount of data that needs to be materialized
along the way.
Benchmarks:
For the example word count job, I modified the intermediate count values to be Text
type rather than IntWritable
. In the reducer, I used Integer.parseString(value.toString())
when accumulating the sum. The performance of the suboptimal version of
the WordCount was about 10% slower than the original. The full job ran
in a bit over 9 minutes, and each map task took 36 seconds instead of
the original 33. Since integer parsing is itself rather fast, this did
not represent a large improvement; in the general case, I have seen
using more efficient Writables to make as much as a 2-3x difference in
performance.
Tip 6) Reuse Writables
Symptoms/diagnostics:
- Add
-verbose:gc -XX:+PrintGCDetails
tomapred.child.java.opts
. Then inspect the logs for some tasks. If garbage collection is frequent and represents a lot of time, you may be allocating unnecessary objects. - grep for “
new Text
” or “new IntWritable
” in your code base. If you find this in an inner loop, or inside themap
orreduce
functions this tip may help. - This tip is especially helpful when your tasks are constrained in RAM.
One of the first mistakes that many MapReduce users make is to allocate a new Writable
object for every output from a mapper or reducer. For example, one might implement a word-count mapper like this:
public void map(...) { ... for (String word : words) { output.collect(new Text(word), new IntWritable(1)); } }
This implementation causes thousands of very short-lived objects to be allocated. While the Java garbage collector does a reasonable job at dealing with this, it is more efficient to write:
class MyMapper ... { Text wordText = new Text(); IntWritable one = new IntWritable(1); public void map(...) { ... for (String word : words) { wordText.set(word); output.collect(word, one); } } }
Benchmarks:
When I modified the word count example as described above, I initially found it made no difference in the run time of the job. This is because this cluster’s default settings include a 1GB heap size for each task, so garbage collection never ran. However, running it with each task allocated only 200mb of heap size showed a drastic slowdown in the version that did not reuse Writables — the total job runtime increased from around 8m30s to over 17 minutes. The original version, which does reuse Writables, stayed the same speed even with the smaller heap. Since reusing Writables is an easy fix, I recommend always doing so – it may not bring you a gain for every job, but if you’re low on memory it can make a huge difference.
Tip 7) Use “Poor Man’s Profiling” to see what your tasks are doing
This is a trick I almost always use when first looking at the performance of a MapReduce job. Profiling purists will disagree and say that this won’t work, but you can’t argue with results!
In order to do what I call “poor man’s profiling”, ssh
into one of your slave nodes while some tasks from a slow job are running. Then simply run sudo killall -QUIT java
5-10 times in a row, each a few seconds apart. Don’t worry — this
doesn’t cause anything to quit, despite the name. Then, use the
JobTracker interface to navigate to the stdout
logs for one of the tasks that’s running on this node, or look in /var/log/hadoop/userlogs/
for a stdout
file of a task that is currently running. You’ll see stack trace output from each time you sent the SIGQUIT
signal to the JVM.
It takes a bit of experience to parse this output, but here’s the method I usually use:
- For each thread in the trace, quickly scan for the name of your Java package (e.g. com.mycompany.mrjobs). If you don’t see any lines in the trace that are part of your code, skip over this thread.
- When you find a stack trace that has some of your code in it, make a quick mental note what it’s doing. For example, “something NumberFormat-related” is all you need at this point. Don’t worry about specific line numbers yet.
- Go down to the next dump you took a few seconds later in the logs. Perform the same process here and make a note.
- After you’ve gone through 4-5 of the traces, you might notice that
the same vague thing shows up in every one of them. If that thing is
something that you expect to be fast, you probably found your culprit.
If you take 10 traces, and 5 of them show
NumberFormat
in the dump, it means that you’re spending somewhere around 50% of your CPU time formatting numbers, and you might consider doing something differently.
Sure, this method isn’t as scientific as using a real profiler on your tasks, but I’ve found that it’s a surefire way to notice any glaring CPU bottlenecks very quickly and with no setup involved. It’s also a technique that you’ll get better at with practice as you learn what a normal dump looks like and when something jumps out as odd.
Here are a few performance mistakes I often find through this technique:
-
NumberFormat
is slow – avoid it where possible. -
String.split
, as well as encoding or decoding UTF8 are slower than you think – see above tips about using the appropriate Writables - Concatenating
String
s rather than usingStringBuffer.append
发表评论
-
Hadoop的Secondary NameNode方案
2012-11-13 10:39 1307http://book.51cto.com/art/20120 ... -
hadoop
2011-10-08 12:20 1138hadoop job解决 ... -
hadoop作业调优参数整理及原理
2011-04-15 14:02 13401 Map side tuning 参数 ... -
Job运行流程分析
2011-03-31 11:04 1710http://www.cnblogs.com/forfutur ... -
hadoop作业运行部分源码
2011-03-31 10:51 1459一、客户端 Map-Reduce的过程首先是由客户端提交 ... -
eclipse中编译hadoop(hive)源码
2011-03-24 13:20 3448本人按照下面编译Hadoop 所说的方法在eclipse中编 ... -
Configuration Parameters: What can you just ignore?
2011-03-11 15:16 900http://www.cloudera.com/blog/20 ... -
hadoop 源码分析一
2011-02-22 15:29 1245InputFormat : 将输入的 ... -
hadoop参数配置(mapreduce数据流)
2011-01-14 11:08 2932Hadoop配置文件设定了H ... -
混洗和排序
2011-01-05 19:33 3284在mapreduce过程中,map ... -
hadoop中每个节点map和reduce个数的设置调优
2011-01-05 19:28 8489map red.tasktracker.map.tasks. ... -
hadoop profiling
2010-12-20 20:52 2666和debug task一样,profiling一个运行在分布 ... -
关于JVM内存设置
2010-12-20 20:49 1381运行map、reduce任务的JVM内存调整:(我当时是在jo ... -
HADOOP报错Incompatible namespaceIDs
2010-12-14 12:56 1045HADOOP报错Incomp ... -
node1-node6搭建hadoop
2010-12-13 18:42 1159环境: node1-node6 node1为主节点 ... -
hadoop启动耗时
2010-12-07 17:28 1354http://blog.csdn.net/AE86_FC/ar ... -
namenode 内部关键数据结构简介
2010-12-07 16:35 1306http://www.tbdata.org/archiv ... -
HDFS常用命令
2010-12-04 14:59 1344文件系统检查 bin/hadoop fsck [pa ... -
HDFS添加和删除节点
2010-12-04 14:45 2059From http://developer.yahoo.co ... -
hadoop 0.20 程式開發
2010-11-30 17:15 1317hadoop 0.20 程式開發 ecl ...
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数据集一个高质量的医学图像数据集,专门用于脑肿瘤的检测和分类研究以下是关于这个数据集的详细介绍:该数据集包含5249张脑部MRI图像,分为训练集和验证集。每张图像都标注了边界框(Bounding Boxes),并按照脑肿瘤的类型分为四个类别:胶质瘤(Glioma)、脑膜瘤(Meningioma)、无肿瘤(No Tumor)和垂体瘤(Pituitary)。这些图像涵盖了不同的MRI扫描角度,包括矢状面、轴面和冠状面,能够全面覆盖脑部解剖结构,为模型训练提供了丰富多样的数据基础。高质量标注:边界框是通过LabelImg工具手动标注的,标注过程严谨,确保了标注的准确性和可靠性。多角度覆盖:图像从不同的MRI扫描角度拍摄,包括矢状面、轴面和冠状面,能够全面覆盖脑部解剖结构。数据清洗与筛选:数据集在创建过程中经过了彻底的清洗,去除了噪声、错误标注和质量不佳的图像,保证了数据的高质量。该数据集非常适合用于训练和验证深度学习模型,以实现脑肿瘤的检测和分类。它为开发医学图像处理中的计算机视觉应用提供了坚实的基础,能够帮助研究人员和开发人员构建更准确、更可靠的脑肿瘤诊断系统。这个数据集为脑肿瘤检测和分类的研究提供了宝贵的资源,能够帮助研究人员开发出更准确、更高效的诊断工具,从而为脑肿瘤患者的早期诊断和治疗规划提供支持。
内容概要:本文详细介绍了STM32F103的CAN通讯和IAP升级Bootloader的源码实现及其硬件设计。首先,针对CAN通讯部分,文章深入探讨了CAN外设的初始化配置,包括波特率、位时间、过滤器等重要参数的设置方法,并提供了一段完整的初始化代码示例。接着,对于IAP升级Bootloader,文中讲解了通过CAN总线接收HEX文件并写入Flash的具体实现步骤,以及如何安全地从Bootloader跳转到应用程序。此外,文章还附上了原理图和PCB文件,有助于理解和优化硬件设计。最后,作者分享了一些实用的调试技巧和注意事项,如终端电阻的正确使用、CRC校验的应用等。 适合人群:嵌入式系统开发者、硬件工程师、从事STM32开发的技术人员。 使用场景及目标:适用于正在开发STM32相关项目的工程师,尤其是那些需要实现CAN通讯和固件在线升级功能的人群。通过学习本文提供的源码和技术要点,可以帮助他们快速掌握相关技能,提高开发效率。 其他说明:本文不仅提供了详细的代码示例,还包含了丰富的实践经验分享,能够帮助读者更好地理解和解决实际开发中遇到的问题。
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内容概要:本文详细介绍了西门子S7-1500 PLC在制药厂洁净空调建筑管理系统(BMS)中的应用案例。重点讨论了硬件配置(1500 CPU + ET200SP分布式IO)、温湿度控制策略(串级PID、分程调节)、以及具体的编程实现(SCL语言)。文中分享了多个技术细节,如PT100温度采集、PID控制算法优化、报警管理和HMI界面设计等。此外,作者还提到了一些调试过程中遇到的问题及其解决方案,如PID_Compact块的手动模式设定值跳变问题、博图V15.1的兼容性问题等。 适合人群:从事工业自动化领域的工程师和技术人员,特别是那些对PLC编程、温湿度控制和洁净空调系统感兴趣的读者。 使用场景及目标:适用于制药厂或其他对温湿度控制要求严格的行业。主要目标是确保洁净空调系统的高效运行,将温湿度波动控制在极小范围内,保障生产环境的安全性和稳定性。 其他说明:本文不仅提供了详细的编程代码和硬件配置指南,还分享了许多实践经验,帮助读者更好地理解和应用相关技术。同时,强调了在实际项目中需要注意的关键点和潜在问题。
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内容概要:本文详细介绍了一种用于解决车间调度问题的遗传算法(Matlab实现),即JSPGA。文章首先介绍了遗传算法的基本概念及其在车间调度问题中的应用场景。接着,作者展示了完整的Matlab源码,包括参数设置、种群初始化、选择、交叉、变异、适应度计算以及结果输出等模块。文中还特别强调了适应度计算方法的选择,采用了最大完工时间的倒数作为适应度值,并通过三维甘特图和迭代曲线直观展示算法性能。此外,文章提供了多个调参技巧和改进方向,帮助读者更好地理解和应用该算法。 适合人群:对遗传算法感兴趣的研究人员、工程师以及希望深入理解车间调度问题求解方法的技术爱好者。 使用场景及目标:适用于需要优化多台机器、多个工件加工顺序与分配的实际工业生产环境。主要目标是通过遗传算法找到最优或近似最优的调度方案,从而减少最大完工时间,提高生产效率。 其他说明:文章不仅提供了详细的理论解释和技术细节,还包括了大量实用的代码片段和图表,使读者能够轻松复现实验结果。同时,作者还分享了一些个人经验和建议,为后续研究提供了有价值的参考。
内容概要:本文深入探讨了永磁同步电机(PMSM)的最大转矩电流比(MTPA)控制算法,并详细介绍了基于Simulink的仿真模型设计。首先,文章阐述了PMSM的数学模型,包括电压方程和磁链方程,这是理解控制算法的基础。接着,解释了矢量控制原理,通过将定子电流分解为励磁电流和转矩电流分量,实现对电机的有效控制。随后,重点讨论了MTPA控制的目标和方法,即在限定电流条件下最大化转矩输出。此外,文章还涉及了前馈补偿、弱磁控制和SVPWM调制等关键技术,提供了具体的实现代码和仿真思路。最后,通过一系列实验验证了各控制策略的效果。 适合人群:从事电机控制系统设计的研究人员和技术人员,尤其是对永磁同步电机和Simulink仿真感兴趣的工程师。 使用场景及目标:适用于希望深入了解PMSM控制算法并在Simulink环境中进行仿真的技术人员。主要目标是掌握MTPA控制的核心原理,学会构建高效的仿真模型,优化电机性能。 其他说明:文中不仅提供了详细的理论推导,还有丰富的代码示例和实践经验,有助于读者快速理解和应用相关技术。同时,强调了实际工程中常见的问题及解决方案,如负载扰动、弱磁控制和SVPWM调制等。
内容概要:本文详细介绍了三机并联的风光储混合系统在Matlab中的仿真方法及其关键技术。首先,针对光伏阵列模型,讨论了其核心二极管方程以及MPPT(最大功率点跟踪)算法的应用,强调了环境参数对输出特性的影响。接着,探讨了永磁同步风机的矢量控制,尤其是转速追踪和MPPT控制策略。对于混合储能系统,则深入讲解了超级电容和蓄电池的充放电策略,以及它们之间的协调机制。此外,还涉及了PQ控制的具体实现,包括双闭环结构的设计和锁相环的优化。最后,提供了仿真过程中常见的问题及解决方案,如求解器选择、参数敏感性和系统稳定性等。 适合人群:从事电力电子、新能源系统设计与仿真的工程师和技术人员,以及相关专业的研究生。 使用场景及目标:适用于希望深入了解风光储混合系统工作原理的研究人员,旨在帮助他们掌握Matlab仿真技巧,提高系统设计和优化的能力。 其他说明:文中不仅提供了详细的理论推导和代码示例,还分享了许多实践经验,有助于读者更好地理解和应用所学知识。
本书由国际发展研究中心(IDRC)和东南亚研究院(ISEAS)联合出版,旨在探讨亚洲背景下电子商务的发展与实践。IDRC自1970年起,致力于通过科学技术解决发展中国家的社会、经济和环境问题。书中详细介绍了IDRC的ICT4D项目,以及如何通过项目如Acacia、泛亚网络和泛美项目,在非洲、亚洲和拉丁美洲推动信息通信技术(ICTs)的影响力。特别强调了IDRC在弥合数字鸿沟方面所作出的贡献,如美洲连通性研究所和非洲连通性项目。ISEAS作为东南亚区域研究中心,专注于研究该地区的发展趋势,其出版物广泛传播东南亚的研究成果。本书还收录了电子商务在亚洲不同国家的具体案例研究,包括小型工匠和开发组织的电子商务行动研究、通过互联网直接营销手工艺品、电子营销人员的创新方法以及越南电子商务发展的政策影响。
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内容概要:本文档《Java经典面试笔试题及答案.docx》涵盖了广泛的Java基础知识和技术要点,通过一系列面试题的形式,深入浅出地讲解了Java的核心概念。文档内容包括但不限于:变量的声明与定义、对象序列化、值传递与引用传递、接口与抽象类的区别、继承的意义、方法重载的优势、集合框架的结构、异常处理机制、线程同步、泛型的应用、多态的概念、输入输出流的使用、JVM的工作原理等。此外,还涉及了诸如线程、GUI事件处理、类与接口的设计原则等高级主题。文档不仅解释了各个知识点的基本概念,还提供了实际应用场景中的注意事项和最佳实践。 适合人群:具备一定Java编程基础的学习者或开发者,特别是准备参加Java相关岗位面试的求职者。 使用场景及目标:①帮助读者巩固Java基础知识,提升对Java核心技术的理解;②为面试做准备,提供常见面试题及其详细解答;③指导开发者在实际项目中应用Java的最佳实践,优化代码质量和性能。 其他说明:文档内容详实,涵盖了Java开发中的多个方面,从基础语法到高级特性均有涉及。建议读者在学习过程中结合实际编程练习,加深对各个知识点的理解和掌握。同时,对于复杂的概念和技术,可以通过查阅官方文档或参考书籍进一步学习。
内容概要:本文详细介绍了如何利用MATLAB将预训练的深度学习模型(如ResNet50、YOLOv2和LaneNet)转化为高效的C++代码,并部署到嵌入式系统中。首先,通过ResNet50展示了图像分类任务的代码生成流程,强调了输入图像的预处理和归一化步骤。接着,YOLOv2用于车辆检测,讨论了anchor box的可视化及其优化方法,特别是在Jetson Nano平台上实现了显著的速度提升。最后,LaneNet应用于车道线识别,探讨了实例分割和聚类算法的实现细节,以及如何通过OpenMP和CUDA进行性能优化。文中还提供了多个实用技巧,如选择合适的编译器版本、处理自定义层和支持动态输入等。 适合人群:具有一定MATLAB和深度学习基础的研发人员,尤其是关注嵌入式系统和高性能计算的应用开发者。 使用场景及目标:适用于希望将深度学习模型高效部署到嵌入式设备的研究人员和工程师。主要目标是提高模型推理速度、降低内存占用,并确保代码的可移植性和易维护性。 其他说明:文中不仅提供了详细的代码示例和技术细节,还分享了许多实践经验,帮助读者避免常见的陷阱。此外,还提到了一些高级优化技巧,如SIMD指令集应用和内存管理策略,进一步提升了生成代码的性能。