On Tue, Jan 1, 2013 at 5:59 AM, Rohmatul Fajriyah <rfajriyah@yahoo.com>wrote:

>
>
> Dear All,
>
> I have a simulation program in R, which I run it in my laptop.
> To produce 1 result, it took about 7 hours and I need about 50, 100, and
> 500 results.
> (I use Mac OS, Macbook Pro. Hardware specs are 2.9 GHz Dual-Core Intel
> Core i7 Processor and RAM: 8GB.)
>
> Then I tried to learn and use Cloud Computing, which was ended up with the
> Bioconductor Cloud AMI.
>
> I took the High-Memory Quadruple Extra Large Instance, API name:
> m2.4xlarge.
> With the hope that it will make it faster in producing the results.
>
>
The definition of the m2.4large instance is

68.4 GiB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
EBS-Optimized Available: 1000 Mbps
API name: m2.4xlarge

You need to write the simulation code to take advantage of the multiplicity
of nodes/cores, the quantity of memory, and
the quantity of disk.  If your tasks are not explicitly matched to the
capabilities of this system, resources will sit idle at your expense, and
the program can be expected to run in about the same time as it takes on
your laptop.

I would suggest that you try to identify a piece of your simulation that
completes in a modest period of time, say two minutes.  Then find some
elements of the computations that can be executed simultaneously.  Rewrite
these using functions in the parallel package (or look at the BiocParallel
package, now available only in the devel branch of Bioconductor) and
demonstrate using your multicore laptop that you have achieved some speedup
by doing this.  Once you have accomplished this, you can consider whether
you can benefit from further redesign and then deployment on the AMI
instance type that provides resources that will actually contribute to
faster completion of your task.

The vignette "Using the foreach package" in the foreach package from CRAN
has nice examples of approaches to reformulation of iterative programs to
use concurrent computing facilities.  The CRAN task view has many relevant
items:
http://cran.r-project.org/web/views/HighPerformanceComputing.html

http://en.wikipedia.org/wiki/Amdahl's_law  is very relevant


> What happened was the amazon cloud computing produced the result longer
> than my laptop.
> It took about 8 hours for 1 result.
>
> I am really new to the cloud computing.
> Could you help me to find out, what my mistake was in using/setting the
> cloud computing, therefore, the result was produce longer than my laptop?
> What should I do to make it faster, please?
>
> For your attention and kindness, thank you very much in advance.
> Happy New Year 2013.
>
> With kind regards,
> R Fajriyah
>         [[alternative HTML version deleted]]
>
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>

	[[alternative HTML version deleted]]

