2 research outputs found
The RapidMind Development Platform for Cell, GP-GPU, and Multicore CPUs
Massively multi-core processors such as GPUs and the Cell BE have the
potential to deliver high performance computation to many applications.
However, these processors require parallel programming on several
levels, use some novel programming models, and native code written for
one will not execute on the other. The
RapidMind
development platform
enables portable access to the power of these processors. It provides a
uniform, simple, safe, data-parallel programming model and takes care of
most of the low-level details of mapping programs to each hardware
target. It combines a dynamic compiler with a runtime streaming
execution manager, and provides a single system image for computations
running on any number of cores.
The interface to this system is embedded inside ISO standard C++, where
it can capture arbitrary computations specified at runtime. The use of
a dynamic compiler means that high-level C++ abstractions can be used
without sacrificing performance, while maintaining portability among
current--and future--processors.
This seminar will introduce and demonstrate the use of the RapidMind
platform on GPUs (for both visualization and general-purpose
computation), the Cell BE (where it runs on both IBM blades and the PS3)
and on multicore CPU (x86 processor targets are under development).
Comparative performance
results will be presented for the GPU, the Cell BE, and multicore CPUs.
On
GPUs up to a 30x speedup over tuned CPU code has been achieved. On the
Cell, we can match or exceed the performance of vendor tools in tuned
applications, but with much less developer effort. On CPUs, we have
doubled performance over native tools (i.e. 8x speedup on a 4-core
relative to icc on one).
About the speaker
Michael McCool is an Associate Professor in the School of Computer
Science at the University of Waterloo and co-founder and Chief Scientist
of RapidMind Inc.
Prof. McCool's current research efforts are targeted at enabling
high-performance parallel applications by the development of advanced
programming technologies. Research interests include interval analysis,
Monte Carlo and quasi-Monte Carlo numerical methods, optimization,
simulation, sampling, cellular automata, real-time computer graphics,
vision, image processing, hardware design, and programming languages and
development platforms.
He has degrees in both Computer Engineering (B.A.Sc. with Math option,
Waterloo, 1989, Sir Sandford Medal) and Computer Science (M.Sc. in 1991
and Ph.D. in 1995, Toronto).
Organiser(s):
Miguel Angel Marquina
Computing Seminars /
IT Department
</address