Skip to main content

Finally, a way to speed up Android emulator

The Android emulator's speed is killer. Literally. Android developers know this; irrespective of platform you are using, Windows, Linux or Macintosh, and your hardware specification, the emulator provided with the Android's SDK crawls like a snail.

 Thankfully, the developers at ‎Intel® have come up with a hardware-assisted virtualization engine that uses the power of hardware virtualization to boost the performance of Android emulators. Here is how you can configure it:

 1. First, you need to enable Hardware Virtualization (VTx) technology from the BIOS settings on your computer. Since different vendors have different settings, you may have to search on the web on how to do so.

 2. Next, you need to download and install Android HAX Manager from Intel. During installation, you will be asked to reserve the amount of memory for HAX, keep it default (1024MB).

 3. Now go to your Android SDK directory and launch "SDK Manager.exe".

 4. Under Extras, check "Intel x86 Emulator Accelerator (HAXM installer)" and also select "Intel x86 Atom System Image" for your SDK version.


5. Launch "AVD Manager.exe" from your Android SDK directory and create a new Virtual Device (AVD). Select Intel Atom (x86) for the CPU. You may just copy the settings from the image below.


That's all. Launch your AVD and feel the speed. :-)

 Note: Unfortunately, this may not work if your computer does not support hardware virtualization, or you are on AMD platform. Here is a list from Intel of the CPUs that support hardware virtualization (I hope your CPU is listed here).

 Feel free to comment for suggestions and corrections...
Labels:

Comments

  1. AnonymousMay 13, 2014 at 9:32 AM

    Very good post keep up the good work Owais !

    ReplyDelete

Post a Comment

Popular posts from this blog

Executing MapReduce Applications on Hadoop (Single-node Cluster) - Part 1

Okay. You just set up Hadoop on a single node on a VM and now wondering what comes next. Of course, you’ll run something on it, and what could be better than your own piece of code? But before we move to that, let’s first try to run an existing program to make sure things are well set on our Hadoop cluster. Power up your Ubuntu with Hadoop on it and on Terminal (Ctrl+Alt+T) run the following command: $ start-all.sh Provide the password whenever asked and when all the jobs have started, execute the following command to make sure all the jobs are running: $ jps Note: The “jps” utility is available only in Oracle JDK, not Open JDK. See, there are reasons it was recommended in the first place. You should be able to see the following services: NameNode SecondaryNameNode DataNode JobTracker TaskTracker Jps We'll take a minute to very briefly define these services first. NameNode : a component of HDFS (Hadoop File System) that manages all the f...
3 comments
Read more

5 things to do when you cannot trace a bug

Programming today, is more about fixing existing code than writing new. In most of the cases, bugs are easy to trace, especially when you are using a modern IDEs like Eclipse or Visual Studio. However, it is very likely that you get trapped in situations like a specific button not doing anything, application crashing randomly,  or a record not updating for a specific ID. Here are some tips you may find lifesavers if you get jammed too often when debugging your code: Catching random errors Remember, there are no random errors unless you are calling a random function. The code is always consistent, if a function calculates compound interest of an amount over a certain period of time -- within an allowed range -- correctly, it will never do it wrong as long as the parameter values are in range. So, the code is consistent. Data, however, may not be. Here is an example: public boolean saveRecordInDB (int id, String name, float height, float weight) { // Do something } Test: ...
1 comment
Read more

A step-by-step guide to query data on Hadoop using Hive

Hadoop empowers us to solve problems that require intense processing and storage on commodity hardware harnessing the power of distributed computing, while ensuring reliability. When it comes to applicability beyond experimental purposes, the industry welcomes Hadoop with warm heart, as it can query their databases in realistic time regardless of the volume of data. In this post, we will try to run some experiments to see how this can be done. Before you start, make sure you have set up a Hadoop cluster . We will use Hive , a data warehouse to query large data sets and a adequate-sized sample data set, along with an imaginary database of a travelling agency on MySQL; the DB  consisting of details about their clients, including Flight bookings, details of bookings and hotel reservations. Their data model is as below: The number of records in the database tables are as: - booking: 2.1M - booking_detail: 2.1M - booking_hotel: 1.48M - city: 2.2K We will write a query t...
1 comment
Read more