Slide 1

Graphics Processing Units (GPUs) for
Powerful, Simple and Cheap
parallel computing
by High Performance Consulting
[email protected]
Swedish Multicore Initiative:
“… nearly all commercially
available microprocessors are
now organized as multicore
processors where performance
improvements are obtained
through parallelism.”
Parallel programming is necessary
“… only 1% of software
developers are proficient
in parallel programming”
We make parallel programming simple
“parallel software
development is 2-3 times more
than conventional software
We make parallel programming cheap
GPUs have 35 times the performance of CPUs
a simple parallel programming model
GPUs are cheap. They use a simple programming model yet provide
enormous levels of performance in an energy-efficient manner. Moreover, the
simplicity of their programming model means that code written today will
scale linearly with future hardware performance.
GPUs are used in:
•Image analysis
•Signal processing
•Computational Fluid Dynamics
•Medical imaging
•Simulations and real-time
•Plus much more!
“If we think about how long it would take to handle this much complexity with
traditional methods, we’re probably close to 100 times faster.”
- Weta Digital, Visual effects Vendor of Avatar
“With parallel processing on GPUs, pricing a large portfolio of exotic contracts
can be accomplished in minutes instead of hours.”
- Curt Randall, Executive Vice President of SciComp
“GPU computing technology has given us a 100-fold increase in some of our
programs, and this is on desktop machines where previously we would have had
to run these calculations to a cluster.”
- John Stone, senior research programmer at the University of Illinois
“By using … GPUs, these applications can now be run 10-20 times faster, which
means even a PC with Tesla GPUs can outperform a supercomputer."
- HPCwire
High Performance Consulting
Consultants in GPU programming and
[email protected]

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