Hello all! I’ve been working with Storm here lately. In case you don’t know what Storm is you can check out the project on Github here: Storm, the short version is that it’s a real time distributed data processing engine. After spending considerable amounts of time getting everything to run properly on an Ubuntu 12.04 box I decided I’d post my recipe for success. So here we go:
You’ll need to ensure that a few packages are installed first: build-essential, uuid-dev, libtool, git, autoconf, openjdk-6-jdk
Create a JAVA_HOME variable that point the the jdk you just installed. Should be in the /usr/lib/jvm directory
Run the following commands for installing Zero MQ:
tar -xzf zeromq-2.1.7.tar.gz
sudo make install
Download JZMQ and navigate to the the src directory
git clone https://github.com/nathanmarz/jzmq.git
Once in the src directory run the touch command to create a file and then redefine the classpath.
Every so often I run out of original content to write or show this is one of those times so I’d like to direct you to some projects or people who I think are doing really amazing work.
For the techie:
First I’d like to show off DuoLingo the brain child of Luis Von Ahn from Carnegie Mellon University. DuoLingo is a free services that seeks to help you learn a new language from scratch instead of buying software such as Rosetta Stone. The way they do this is they give you sentences to translate to and from one language to another. The really amazing part is that you’re doing actual work: the material that you translating while learning is actual content from somewhere on the web that is getting translated by you and several other users. Check out his presentation at TedX CMU.
Looks like BlackBerry has finally met it’s match hackers xpvqus, neuralgic, and cmwdotme have gained root access to the RIM’s Playbook. This is particularly interesting to me, RIM’s hardware is pretty nice but their operating system seems clunky and unwieldy. Maybe just maybe someone will take it upon themselves to port a newer Android OS to this device.
I’m not sure why I’m recommending this link. On one hand it’s really interesting to see what people are doing with the Siri proxy, but on the other hand it’s terribly entertaining. This gentleman appears to have linked Siri with his X10 home automation system. This allows him to give Siri voice commands to control the things that he has linked to his X10; in this case his fireplace and his lights.
For the artist:
The 45 most powerful photos of 2011:
Enough said. The two that are the most moving to me are #25 and #30 they will always be stuck in my mind.
When I first started programming I remember seeing a table with the amount of memory that each variable would take up for instance a byte would take up a byte, a short would take up two bytes, and an int would take up four bytes. Now any enthusiastic programmer (I did this so it may just be me) would immediately think I’m going to make my program take up as few bytes of memory as possible which is a noble idea. They will go through their program and determine the range of each variable and then figure out at most how many bytes they will need. However, this approach may be quite flawed for the following reason: once your compiler converts your high level language that you’re using into machine code you end up using more processor cycles to achieve the result. This is due to the nature of numbers inside of memory and the processor. Inside a processor you cannot add two variables if they differ in size so you must convert one to the size of the other by loading it into the A(al,ax,eax for 1byte,2byte, and 4byte respectively) register of the appropriate size and converting it (2 cycles where it should take one cycle)
Take the following example for instance: [Written in Java]
byte x = 6;
short y = 32;
int z= 128;
int v = x+y+z;
the same process in x86 assembly would look like this:
x byte 6
y word 32
z dword 128
v dword ?
Now lets take a look at the processor optimized solution: [Java]
int x = 6;
int y = 32;
int z= 128;
int v = x+y+z;
And in x86 assembly:
x dword 6
y dword 32
z dword 128
v dword ?
You see in the processor optimized solution there are only four total operations to carry out in the memory optimized solution though there are six total operations. It might not seem like much but imagine you have to do this operation ten thousand times. The processor optimized solution takes only 60% of the operations that the memory optimized solution takes and it only uses five more bytes of memory. That’s a huge time savings no matter how you look at it.
Now I’m not saying the processor optimized solution is always the best solution. Instead I’m saying that in a system where you don’t have to worry too much about memory management that a processor optimized solution my make your program run faster. If you’re in a system with limited memory though it’s probably faster to just go ahead and optimize it for memory since the conversion operations are pretty fast anyways. It’s worth it to note I’m just a student so if I’ve misunderstood some concept please correct me I’m just trying to learn all I can.