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pytensor
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d02cfad2
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d02cfad2
authored
8月 20, 2014
作者:
Pascal Lamblin
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presentation.tex
doc/omlw2014/presentation.tex
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doc/omlw2014/presentation.tex
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d02cfad2
...
...
@@ -42,7 +42,7 @@ Montréal, Canada \newline
\begin{itemize}
\item
Python: OO coding language
\item
Numpy: n-dimension array object and scientific computing toolbox
\item
SciPy: sparse matrix object and more scientfic computing functionality
\item
SciPy: sparse matrix object and more scient
i
fic computing functionality
\item
libgpuarray: gpu n-dimentional array object in C for CUDA and OpenCL
\item
Theano: compiler/symbolic graph manipulation
\item
Pylearn2: machine learning framework
...
...
@@ -71,22 +71,22 @@ Montréal, Canada \newline
\item
NumPy provides a N-dimensional numeric array in Python
\begin{itemize}
\item
Perfect for high-performance computing.
\item
Slice
are return view
(no copy)
\item
Slice
s of arrays are views
(no copy)
\end{itemize}
\item
NumPy provides
\begin{itemize}
\item
e
lementwise computations
\item
l
inear algebra, Fourier transforms
\item
p
seudorandom numbers from many distributions
\item
E
lementwise computations
\item
L
inear algebra, Fourier transforms
\item
P
seudorandom numbers from many distributions
\end{itemize}
\item
SciPy provides lots more, including
\begin{itemize}
\item
s
parse matrix
\item
m
ore linear algebra
\item
s
olvers and optimization algorithms
\item
m
atlab-compatible I/O
\item
S
parse matrix
\item
M
ore linear algebra
\item
S
olvers and optimization algorithms
\item
M
atlab-compatible I/O
\item
I/O and signal processing for images and audio
\end{itemize}
\end{itemize}
...
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@@ -104,9 +104,8 @@ Montréal, Canada \newline
\begin{frame}
{
Why scripting for GPUs?
}
\begin{bf}
They Complement each other
\end{bf}
\begin{itemize}
\item
GPUs are everything that scripting/high level languages are not
GPUs are everything that scripting/high level languages are not
\begin{itemize}
\item
Highly parallel
...
...
@@ -117,13 +116,18 @@ Montréal, Canada \newline
\end{itemize}
\begin{bf}
Best of both:
\end{bf}
easy scripted development invokes GPU kernel.
Combined with Theano c code generation...
\begin{bf}
Theano c code generation remove overhead
\end{bf}
from function call between Python and C.
\begin{bf}
Theano C code generation removes overhead
\end{bf}
from
function call between Python and C, by launching many C functions at once.
\end{frame}
\begin{frame}
{
Theano
}
\end{frame}
\begin{frame}
{
Pylearn2
}
\end{frame}
\begin{frame}
{
libgpuarray
}
...
...
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