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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Update of "CUDA On Hadoop" by ChenHe
Date Mon, 14 Mar 2011 16:00:26 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Hadoop Wiki" for change notification.

The "CUDA On Hadoop" page has been changed by ChenHe.
http://wiki.apache.org/hadoop/CUDA%20On%20Hadoop?action=diff&rev1=3&rev2=4

--------------------------------------------------

  From the parallel programming point of view, CUDA can hlep us to parallelize program in
the second level if we regard the MapReduce framework as the first level parallelization.
In our study, we provide Hadoop+CUDA solution for programming languages: Java and C/C++. The
scheduling of GPU threads among grids and blocks is not concerned in our study.
  
  === For Java programmers ===
- If your MapReduce program is written in Java, you may need [[http://download.oracle.com/javase/6/docs/technotes/guides/jni/spec/jniTOC.html|JNI]]
to make use of CUDA. However, [[http://www.jcuda.org|JCuda]] provides a easier solution for
us. We introduce CUDA to our Map stage. The CUDA code is called by map() method within Map
class. It is easy to extend to Reduce stage if necessary. There two ways to compile your CUDA
code.
+ If your MapReduce program is written in Java, you may need [[http://download.oracle.com/javase/6/docs/technotes/guides/jni/spec/jniTOC.html|JNI]]
to make use of CUDA. However, [[http://www.jcuda.org|JCuda]] provides a easier solution for
us. We introduce CUDA to our Map stage. The CUDA code is called by map() method within Map
class. It is easy to extend to Reduce stage if necessary. There are two ways to compile your
CUDA code.
  
  One is to write CUDA code as a String variable in your Java code. JCuda will automatically
compile it for you. The compiled binary file is located in tasktrackers working directory
that you can configure in mapred-site.xml file.  
  
- The other is little bit tricky. you can compile the CUDA code into binary files in advance
and move them to tasktrackers working directory. And then every tasktracker can access those
compiled binary files.    
+ The other is little bit tricky. you can manually compile the CUDA code into binary files
in advance and move them to tasktrackers working directory. And then every tasktracker can
access those compiled binary files.    
+ 
+ 
  
  === For C/C++ programmers ===
  
  
- == Hardware ==
- == Test cases ==
  
- == Results ==
  

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