I think they're complementary.
Hadoop's MapReduce lets you run computations on up to thousands of
computers potentially processing petabytes of data. It gets data from
the grid to your computation, reliably stores output back to the grid,
and supports grid-global computations (e.g., sorting).
CUDA can make computations on a single computer run faster by using its
GPU. It does not handle co-ordination of multiple computers, e.g., the
flow of data in and out of a distributed filesystem, distributed
reliability, global computations, etc.
So you might use CUDA within mapreduce to more efficiently run
compute-intensive tasks over petabytes of data.
Doug
Mark Kerzner wrote:
Hi, this from Dr. Dobbs caught my attention, 240 CPU for $1,700
http://www.ddj.com/focal/NVIDIA-CUDA
What are your thoughts?
Thank you,
Mark
