Scientific Computing with Python and CUDA
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1 Scientific Computing with Python and CUDA Stefan Reiterer High Performance Computing Seminar, January Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 1 / 55
2 Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 2 / 55
3 A short Introduction to Python Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 3 / 55
4 What is Python? A short Introduction to Python Python is Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 4 / 55
5 What is Python? A short Introduction to Python Python is a high level programming language Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 4 / 55
6 What is Python? A short Introduction to Python Python is a high level programming language interpreted, Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 4 / 55
7 What is Python? A short Introduction to Python Python is a high level programming language interpreted, object oriented, Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 4 / 55
8 What is Python? A short Introduction to Python Python is a high level programming language interpreted, object oriented, named after the "Monty Pythons". Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 4 / 55
9 What is Python? A short Introduction to Python Python is a high level programming language interpreted, object oriented, named after the "Monty Pythons". It was invented by Guido van Rossum in the early 90s. Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 4 / 55
10 A short Introduction to Python Why Python? Object Oriented programming Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 5 / 55
11 A short Introduction to Python Why Python? Object Oriented programming reusable code Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 5 / 55
12 A short Introduction to Python Why Python? Object Oriented programming reusable code True strength of Python: Rapid prototyping Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 5 / 55
13 A short Introduction to Python Why Python? Object Oriented programming reusable code True strength of Python: Rapid prototyping accelerate research process, saves time and nerves. Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 5 / 55
14 A short Introduction to Python Classical prototyping process Prototype Easy SL Working Code Fast, but complex PL e.g. Matlab e.g. C, C++... Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 6 / 55
15 A short Introduction to Python Prototyping with Python Tests (Python) GUI (Python) Algorithms (Python) Work Flow (Python) Critical calculations (Python) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 7 / 55
16 A short Introduction to Python Prototyping with Python Tests (Python) GUI (Python) Algorithms (Python) (Compile to C) Work Flow (Python) Critical calculations (Python) (link C/C++) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 8 / 55
17 Don t fear Python! A short Introduction to Python Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 9 / 55
18 Don t fear Python! A short Introduction to Python Intuitive Syntax No Allocation Garbage Collection Easy to Learn! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 9 / 55
19 A short Introduction to Python The Goodbye World Program print ( "GoodbyeWorld! " ) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 10 / 55
20 A short Introduction to Python Definition of functions def function_name ( arg1, arg2, arg3 = default ) : r e s u l t = arg1 + arg2*arg3 #do something return result Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 11 / 55
21 A short Introduction to Python Definition of functions def function_name ( arg1, arg2, arg3 = default ) : r e s u l t = arg1 + arg2*arg3 #do something return result Python recognizes code blocks with intends! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 11 / 55
22 A short Introduction to Python Definition of functions def function_name ( arg1, arg2, arg3 = default ) : r e s u l t = arg1 + arg2*arg3 #do something return result Python recognizes code blocks with intends! Always remember: Don t mix tabs with spaces! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 11 / 55
23 A short Introduction to Python If statments i f cond1 i s True : #normal i f do this e l i f cond2 i s True : #else i f do that else : #else do something_else Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 12 / 55
24 A short Introduction to Python while loops while condition i s True : do something Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 13 / 55
25 A short Introduction to Python while loops while condition i s True : do something While loops know else too: while condition i s True : do something else : do something_else Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 13 / 55
26 A short Introduction to Python for loops for x in range(n ) : do something with x Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 14 / 55
27 A short Introduction to Python for loops for x in range(n ) : do something with x Instead of range one can use any list! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 14 / 55
28 A short Introduction to Python for loops for x in range(n ) : do something with x Instead of range one can use any list! For better performance use xrange. Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 14 / 55
29 A short Introduction to Python for loops for x in range(n ) : do something with x Instead of range one can use any list! For better performance use xrange. for loops also have an else statement. Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 14 / 55
30 A short Introduction to Python An example... for n in range(2, 10): for x in range(2, n ) : i f n % x == 0: print n, equals, x, *, n/ x break else : # loop f e l l through without finding a factor print n, i s a prime number Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 15 / 55
31 A short Introduction to Python Output 2 is a prime number 3 is a prime number 4 equals 2 * 2 5 is a prime number 6 equals 2 * 3 7 is a prime number 8 equals 2 * 4 9 equals 3 * 3 Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 16 / 55
32 A short Introduction to Python Classes In Python everything is a class! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 17 / 55
33 A short Introduction to Python Classes In Python everything is a class! No elementary data types! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 17 / 55
34 A short Introduction to Python Definition of a class Classes are defined in the same way as functions class my_class : statement1 statement2... Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 18 / 55
35 A short Introduction to Python Definition of a class Classes are defined in the same way as functions class my_class : statement1 statement2... init serves as constructor Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 18 / 55
36 A short Introduction to Python Definition of a class Classes are defined in the same way as functions class my_class : statement1 statement2... init serves as constructor del serves as destructor Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 18 / 55
37 A short Introduction to Python Definition of a class Classes are defined in the same way as functions class my_class : statement1 statement2... init serves as constructor del serves as destructor Class methods take the object itself as first argument (as convention one writes self) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 18 / 55
38 A short Introduction to Python Derivative of classes Classes are derived simply with class my_class ( inheritence ) : statement1 statement2... Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 19 / 55
39 A short Introduction to Python Derivative of classes Classes are derived simply with class my_class ( inheritence ) : statement1 statement2... They hold the same methods and variables as their parents Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 19 / 55
40 A short Introduction to Python Derivative of classes Classes are derived simply with class my_class ( inheritence ) : statement1 statement2... They hold the same methods and variables as their parents To overload methods simply add the same method agein. Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 19 / 55
41 A short Introduction to Python Operator overloading To overload operators like +,-,*,/ etc simply add the methods add, sub, mul, div... Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 20 / 55
42 A short Introduction to Python Another example: class band_matrix : def i n i t ( self,ab ) : s e l f. shape = (ab. shape[1],ab. shape[1]) s e l f. data = ab s e l f. band_width = ab. shape[0] s e l f. dtype = ab. dtype def matvec( self,u ) : i f s e l f. shape[0]!= u. shape [ 0 ] : raise ValueError ( "Dimension Missmatch! " ) return band_matvec( s e l f. data,u) def add ( self, other ) : return add_band_mat( self, other ) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 21 / 55
43 Scientific Computing tools in Python Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 22 / 55
44 NumPy and SciPy Scientific Computing tools in Python NumPy is for Numerical Python SciPy is for Scientific Python Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 23 / 55
45 NumPy and SciPy Scientific Computing tools in Python NumPy is for Numerical Python SciPy is for Scientific Python Allow Numerical Linear Algebra like in Matlab Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 23 / 55
46 NumPy and SciPy Scientific Computing tools in Python NumPy is for Numerical Python SciPy is for Scientific Python Allow Numerical Linear Algebra like in Matlab Speed: NumPy + SciPy Matlab Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 23 / 55
47 Some examples... Scientific Computing tools in Python Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 24 / 55
48 Easy ways to make Python faster Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 25 / 55
49 Easy ways to make Python faster Band Matrix vector Multiplication # * coding : utf 8 * def band_matvec(a, u ) : result = zeros (u. shape[0], dtype=u. dtype ) for i in xrange(a. shape [ 1 ] ) : result [ i ] = A[0, i ]*u[ i ] for j in xrange(1,a. shape [ 0 ] ) : for i in xrange(a. shape[1] j ) : result [ i ] += A[ j, i ]*u[ i+j ] result [ i+j ] += A[ j, i ]*u[ i ] return result Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 26 / 55
50 Easy ways to make Python faster Write the C-Code inline in Python with Blitz # * coding : utf 8 * from numpy import array, zeros from scipy.weave import converters from scipy import weave def band_matvec_inline (A, u ) : result = zeros (u. shape[0], dtype=u. dtype ) N = A. shape[1] B = A. shape[0] Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 27 / 55
51 Easy ways to make Python faster Write the C-Code inline in Python with Blitz code = """ for ( int i =0; i < N; i++) { result ( i ) = A(0, i )*u( i ) ; } for ( int j =1; j < B; j++) { for ( int i =0; i < (N j ) ; i++) { i f ( ( i+j < N) ) { result ( i ) += A( j, i )*u( j+i ) ; result ( i+j ) += A( j, i )*u( i ) ; } } } """ Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 28 / 55
52 Easy ways to make Python faster Write the C-Code inline in Python with Blitz weave. i n l i n e (code, [ u, A, result, N, B ], type_converters=converters. b l i t z ) return result Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 29 / 55
53 Easy ways to make Python faster Write the C-Code inline in Python with Blitz weave. i n l i n e (code, [ u, A, result, N, B ], type_converters=converters. b l i t z ) return result 290x speedup to python Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 29 / 55
54 Easy ways to make Python faster Cython Python produces a lot of overhead with data types Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 30 / 55
55 Easy ways to make Python faster Cython Python produces a lot of overhead with data types Solution: Declare which data types to use and compile it with Cython! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 30 / 55
56 Easy ways to make Python faster Python again # * coding : utf 8 * def band_matvec(a, u ) : result = zeros (u. shape[0], dtype=u. dtype ) for i in xrange(a. shape [ 1 ] ) : result [ i ] = A[0, i ]*u[ i ] for j in xrange(1,a. shape [ 0 ] ) : for i in xrange(a. shape[1] j ) : result [ i ] += A[ j, i ]*u[ i+j ] result [ i+j ] += A[ j, i ]*u[ i ] return result Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 31 / 55
57 Easy ways to make Python faster Get more with Cython import numpy, scipy cimport numpy as cnumpy ctypedef cnumpy. float64_t reals def matvec_help(cnumpy. ndarray [ reals, ndim=2] A, cnumpy. ndarray [ reals,ndim=1] u ) : cdef Py_ssize_t i, j cdef cnumpy. ndarray [ reals,ndim=1] result = \ numpy. zeros (A. shape[1], dtype=a. dtype ) for i in xrange(a. shape [ 1 ] ) : result [ i ] = A[0, i ]*u[ i ] for j in xrange(1,a. shape [ 0 ] ) : for i in xrange(a. shape[1] j ) : result [ i ] = result [ i ] + A[ j, i ]*u[ i+j ] result [ i+j ] = result [ i+j ]+A[ j, i ]*u[ i ] return result Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 32 / 55
58 Easy ways to make Python faster...and even more import numpy, scipy cimport numpy as cnumpy, boundscheck( False ) ctypedef cnumpy. float64_t reals def matvec_help(cnumpy. ndarray [ reals, ndim=2] A, cnumpy. ndarray [ reals,ndim=1] u ) : cdef Py_ssize_t i, j cdef cnumpy. ndarray [ reals,ndim=1] result = \ numpy. zeros (A. shape[1], dtype=a. dtype ) for i in xrange(a. shape [ 1 ] ) : result [ i ] = A[0, i ]*u[ i ] for j in xrange(1,a. shape [ 0 ] ) : for i in xrange(a. shape[1] j ) : result [ i ] = result [ i ] + A[ j, i ]*u[ i+j ] result [ i+j ] = result [ i+j ]+A[ j, i ]*u[ i ] return result Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 33 / 55
59 Easy ways to make Python faster Speedup to python Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 34 / 55
60 Easy ways to make Python faster Speedup to python Cython 220 speedup Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 34 / 55
61 Easy ways to make Python faster Speedup to python Cython 220 speedup...with boundscheck: 440 speedup Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 34 / 55
62 Easy ways to make Python faster Overview Language time speedup Python 2.96 s 1 Matlab 90 ms 30 Pure C 50 ms 60 Cython 12 ms 220 Inline C++ 10 ms 290 Cython w. boundscheck 6 ms 440 Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 35 / 55
63 Easy ways to make Python faster Overview Language time speedup Python 2.96 s 1 Matlab 90 ms 30 Pure C 50 ms 60 Cython 12 ms 220 Inline C++ 10 ms 290 Cython w. boundscheck 6 ms 440 With linking libraries like Lapack even more is possible! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 35 / 55
64 Python + CUDA = PyCUDA Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 36 / 55
65 What is PyCUDA Python + CUDA = PyCUDA PyCUDA is a Wrapper in Python to CUDA Inline CUDA Garbage Collection A NumPy like vector class. Developed by Andreas Klöckner Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 37 / 55
66 Python + CUDA = PyCUDA Step 1: Write your Code in CUDA source = """ global void doublify ( f loat *a) { int idx = threadidx. x + threadidx. y*4; a[ idx ] *= 2; } """ Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 38 / 55
67 Python + CUDA = PyCUDA Step 2: Get it into Python import pycuda. driver as cuda import pycuda. autoinit from pycuda. compiler import SourceModule mod = SourceModule( source ) func = mod. get_function ( " doublify " ) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 39 / 55
68 Python + CUDA = PyCUDA Step3: Call it import numpy #create vector x = numpy.random. randn(4,4) x = x. astype (numpy. float32 ) #copy i t on card x_gpu = cuda. mem_alloc(x. nbytes ) cuda.memcpy_htod( x_gpu, x) #c a l l function func (x_gpu, block =(4,4,1)) #get data back x_doubled = numpy. empty_like ( x) cuda.memcpy_dtoh( x_doubled, x_gpu) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 40 / 55
69 Python + CUDA = PyCUDA Benefits You can compile CUDA code on runtime Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 41 / 55
70 Python + CUDA = PyCUDA Benefits You can compile CUDA code on runtime You can call it in Python Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 41 / 55
71 Python + CUDA = PyCUDA Benefits You can compile CUDA code on runtime You can call it in Python I was able to implement CG algorithm with the different variants without rewriting code! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 41 / 55
72 Python + CUDA = PyCUDA Overview (Band-matrix vector multiplication) Language time speedup Python 2.87 s 1 Cython 9.87 ms 270 Cython w. boundscheck 5.56 ms 500 PyCUDA (on GTX 280) 585 µ s 5000 Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 42 / 55
73 Python + CUDA = PyCUDA Overview (Band-matrix vector multiplication) Language time speedup Python 2.87 s 1 Cython 9.87 ms 270 Cython w. boundscheck 5.56 ms 500 PyCUDA (on GTX 280) 585 µ s 5000 Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 42 / 55
74 Python + MPI = mpi4py Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 43 / 55
75 Python + MPI = mpi4py Now something completly different... mpi4py from mpi4py import MPI comm = MPI.COMM_WORLD print ( " hello world" ) print ( "my rank i s : %d"%comm. rank ) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 44 / 55
76 Python + MPI = mpi4py Now something completly different... mpi4py from mpi4py import MPI comm = MPI.COMM_WORLD print ( " hello world" ) print ( "my rank i s : %d"%comm. rank ) Simply call Python with MPI: mpirun -n <# processes> python filename.py Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 44 / 55
77 Python + MPI = mpi4py PyCUDA+mpi4py from mpi4py import MPI import pycuda. driver as cuda from pycuda import gpuarray from numpy import float32, array from numpy. random import randn as rand Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 45 / 55
78 Python + MPI = mpi4py PyCUDA+mpi4py comm = MPI.COMM_WORLD rank = comm. rank root = 0 nr_gpus = 4 sendbuf = [ ] N = 2**20*nr_gpus K = 1000 i f rank == 0: x = rand (N). astype ( float32 )*10**16 print "x : ", x cuda. i n i t ( ) #i n i t pycudadriver sendbuf = x. reshape(nr_gpus,n/ nr_gpus ) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 46 / 55
79 Python + MPI = mpi4py PyCUDA+mpi4py i f rank > nr_gpus 1: raise ValueError ( "To few gpus! " ) current_dev = cuda. Device ( rank ) #device we are working on ctx = current_dev. make_context ( ) #make a working context ctx. push ( ) #l e t context make the lead #recieve data and port i t to gpu: x_gpu_part = gpuarray. to_gpu (comm. scatter ( sendbuf, root ) ) Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 47 / 55
80 Python + MPI = mpi4py PyCUDA+mpi4py #do something... for k in xrange(k ) : x_gpu_part = 0.9* x_gpu_part Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 48 / 55
81 Python + MPI = mpi4py PyCUDA+mpi4py #get data back : x_part = ( x_gpu_part ). get ( ) ctx. pop( ) #deactivate again ctx. detach ( ) #delete i t Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 49 / 55
82 Python + MPI = mpi4py PyCUDA+mpi4py recvbuf=comm. gather ( x_part, root ) #recieve data i f rank == 0: x_altered= array ( recvbuf ). reshape(n) print " altered x : ", x_altered Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 50 / 55
83 Python + MPI = mpi4py Overview N K NumPy MPI+Numpy MPI+PyCUDA ms 4.6 ms 3 s ms 212 ms 3.7 s s 781 ms 3.84 s s 6.11 s 7.6 s s 24.2 s 11.6 s min 93.0 s 19.3 s ? 382 s 24.1 s ? 25 min 48.0 s Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 51 / 55
84 ...and the Rest Inhalt 1 A short Introduction to Python 2 Scientific Computing tools in Python 3 Easy ways to make Python faster 4 Python + CUDA = PyCUDA 5 Python + MPI = mpi4py 6...and the Rest Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 52 / 55
85 ...and the Rest Where can I get Python? Everything is Opensource Links: Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 53 / 55
86 ...and the Rest Where can I get Python? Everything is Opensource Links: If you need the latest mpi4py and and PyCUDA packages for Sage/Femhub ask me! Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 53 / 55
87 ...and the Rest Other interesting things for Python f2py Fortran Code inline petsc4py PETSC interface for Python PyOpenCL like PyCUDA but for OpenCl matplotlib, Mayavi powerful plotting libraries... and many many more Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 54 / 55
88 ...and the Rest Questions? Stefan Reiterer () Scientific Computing with Python and CUDA HPC Seminar 55 / 55
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