the
Mahout
and GPU capabilities. I just wanted to know if people involve in
Mahout
have thought about it or is it at all possible or not.for example
speed
up
the Map and Reduce phases by parallelise computations on nodes. Of
course I
am not aware of communication cost
.
but I am more interested to get faster computation by combining
the
Mahout
and GPU capabilities. I just wanted to know if people involve in
Mahout
have thought about it or is it at all possible or not.for example
speed
up
the Map and Reduce phases by parallelise
mohsen.jad...@gmail.com
wrote:
yes it makes sense .
but I am more interested to get faster computation by combining the
Mahout
and GPU capabilities. I just wanted to know if people involve in Mahout
have thought about it or is it at all possible or not.for example speed
up
the Map and Reduce
in
this project.
On Mon, Jul 9, 2012 at 6:07 PM, mohsen jadidi mohsen.jad...@gmail.com
wrote:
yes it makes sense .
but I am more interested to get faster computation by combining the
Mahout
and GPU capabilities. I just wanted to know if people involve in
Mahout
have thought about
I don't think this result holds in general -- they chose a very CPU
intensive problem, without much data movement. This won't work for,
say, Mahout jobs. I don't really see the point in porting Hadoop to a
GPU. If you're in a GPU you don't need most of what Hadoop does! That
is I imagine this is
:
yes it makes sense .
but I am more interested to get faster computation by combining the
Mahout
and GPU capabilities. I just wanted to know if people involve in
Mahout
have thought about it or is it at all possible or not.for example
speed
up
the Map and Reduce phases
Hi Mohsen, hello Sean,
there is already a lot of researching going on for doing recommendations
especially matrix factorization on GPUs:
e.g.
http://www.slideshare.net/NVIDIA/1034-gtc09
20x - 300x faster
or
http://www.multicoreinfo.com/research/papers/2009/ipdps09-lahabar.pdf
60x faster over
(I agree, it's quite a useful approach -- was answering the question
about whether there was any such thing in Mahout. This all assumes you
can fit the data in memory in the GPU but that is true for moderately
large data sets.)
On Mon, Jul 9, 2012 at 9:04 AM, Manuel Blechschmidt
Just a quick and possible innumerate thought re WebGL (which is OpenGL
exposed as Web browser content via Javascript).
Perhaps the big heavy number-crunching can be done on server-side
Mahout / Hadoop, but with a role for *delivery* of computed matrices
in the browser? The memory concerns are
The factorization is the heavy number crunching. The client of a
recommender needs to do very little computation in comparison, like a
vector-matrix product. While a GPU might make this happen faster, it's
already on the order of microseconds. Compare with the cost of
downloading the whole
Thanks for clarifications and comments.
On Mon, Jul 9, 2012 at 10:18 AM, Sean Owen sro...@gmail.com wrote:
The factorization is the heavy number crunching. The client of a
recommender needs to do very little computation in comparison, like a
vector-matrix product. While a GPU might make this
Dot products are an example of something that gpu can't help with. The problem
is that there the same number of flops as memory operations and memory is slow.
To get acceleration you need lots of flops per memory fetch. Usually you need
at least matrix by matrix multiply with both dense.
yes it makes sense .
but I am more interested to get faster computation by combining the Mahout
and GPU capabilities. I just wanted to know if people involve in Mahout
have thought about it or is it at all possible or not.for example speed up
the Map and Reduce phases by parallelise computations
interested to get faster computation by combining the Mahout
and GPU capabilities. I just wanted to know if people involve in Mahout
have thought about it or is it at all possible or not.for example speed up
the Map and Reduce phases by parallelise computations on nodes. Of course I
am
Hello ,
This is my first post here and I just started reading about Hadoop, Mahout
and all. I was wondering if there is any solution to use Mahout on parallel
computing on GPU (mainly CUDA) ? I know it's a bit wired question to ask
because cud a is C base and Mahout is Java base , but I just ask
More than that, Mahout is mostly Hadoop-based, which is well up the
stack from Java. No there is nothing CUDA-related in the project. The
closest thing are the pure Java non-Hadoop-based recommender pieces.
But it is still far from CUDA.
I think CUDA is intriguing since a lot of ML is a bunch of
In general, large scale machine learning is I/O bound already. There are
some things that would not be, but to really feed a GPU reasonably, data
almost has to be memory resident.
For more information on CUDA from Java, see (among others)
http://www.jcuda.de/
On Sun, Jul 8, 2012 at 4:04 PM,
To put it a little differently: the GPU architecture has been
developed around video games. In a video game architecture, you have a
fairly small amount of data (models and textures) going into the GPU
memory via the bus, and then a lot of data coming out of the GPU
hardware substrate to the video
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