[Numpy-discussion] Adopt Mersenne Twister 64bit?
Hi all, I am redirecting a discussion on github issue tracker here. My original post (https://github.com/numpy/numpy/issues/3137): The current implementation of the RNG seems to be MT19937-32. Since 64-bit machines are common nowadays, I am suggesting adding or upgrading to MT19937-64. Thoughts? Let me start by answering to njsmith's comments on the issue tracker: Would it be faster? Although I have not benchmarked the 64-bit implementation, it is likely that it will be faster on a 64-bit machine since the number of iteration (controlled by NN and MM in the reference implementation http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/VERSIONS/C-LANG/mt19937-64.c) is reduced by half. In addition, each generation in the 64-bit implementation produces a 64-bit random int which can be used to generate double precision random number. Unlike the 32-bit implementation which requires generating a pair of 32-bit random int. But, on a 32-bit machine, a 64-bit instruction is translated into 4 32-bit instructions; thus, it is likely to be slower. (1) Use less memory? The amount of memory use will remain the same. The size of the RNG state is the same. Provide higher quality randomness? My naive answer is that 32-bit and 64-bit implementation have the same 2^19937-1 period. Need to do some research and experiments. Would it change the output of this program: import numpy numpy.random.seed(0) print numpy.random.random() ? Unfortunately, yes. The 64-bit implementation generates a different random number sequence with the same seed. (2) My suggestion to overcome (1) and (2) is to allow the user to select between the two implementations (and possibly different algorithms in the future). If user does not provide a choice, we use the MT19937-32 by default. numpy.random.set_state(MT19937_64, …) # choose the 64-bit implementation Thoughts? Best, Siu ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Numpy 1.7.0 with Intel MKL 11.0.2.146
Dear all, I'm at my wits end. I've followed Intel's own instructionshttp://software.intel.com/en-us/articles/numpyscipy-with-intel-mklon how to compile Numpy with Intel MKL. Everything compiled and linked fine and I've installed it locally in my user folder...There is one nasty problem. When one calls the numpy library to do some computation, it does not use all of the available threads. I have 8 cores on my machine and it only uses 4 of them. The MKL_NUM_THREADS environmental variable can be set to tune the number of threads but setting it to 8 does not change anything. Indeed, setting it to 3 does limit the threads to 3What is going on? As a comparison, the numpy (version 1.4.1, installed from yum, which uses BLAS+ATLAS) uses all 8 threads. I do not get this. You can run this test program python -mtimeit -s'import numpy as np; a = np.random.randn(1e3,1e3)' 'np.dot(a, a)' There is one saving grace, the local numpy built with MKL is much faster than the system's numpy. I hope someone can help me. Searching the internet has been fruitless. Best, Quyen My site.cfg for numpy (1.7.0) [mkl] library_dirs = /opt/intel/mkl/lib/intel64 include_dirs = /opt/intel/mkl/include mkl_libs = mkl_rt lapack_libs = I've edited line 37 of numpy/distutils/intelcompiler.py self.cc_exe = 'icc -O3 -fPIC -fp-model strict -fomit-frame-pointer -openmp -parallel -DMKL_ILP64' Also line 54 of numpy/distutils/fcompiler/intel.py return ['-i8 -xhost -openmp -fp-model strict'] My .bash_profile also contains the lines: source /opt/intel/bin/compilervars.sh intel64 source /opt/intel/mkl/bin/mklvars.sh intel64 The above is needed to set the LD_LIBRARY_PATH so that Python can source the intel dynamic library when numpy is called. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.7.0 with Intel MKL 11.0.2.146
On 3/10/13, QT rdirect...@gmail.com wrote: Dear all, I'm at my wits end. I've followed Intel's own instructionshttp://software.intel.com/en-us/articles/numpyscipy-with-intel-mklon how to compile Numpy with Intel MKL. Everything compiled and linked fine and I've installed it locally in my user folder...There is one nasty problem. When one calls the numpy library to do some computation, it does not use all of the available threads. I have 8 cores on my machine and it only uses 4 of them. The MKL_NUM_THREADS environmental variable can be set to tune the number of threads but setting it to 8 does not change anything. Indeed, setting it to 3 does limit the threads to 3What is going on? Does your computer have 8 physical cores, or 4 cores that look like 8 because of hyperthreading? Warren As a comparison, the numpy (version 1.4.1, installed from yum, which uses BLAS+ATLAS) uses all 8 threads. I do not get this. You can run this test program python -mtimeit -s'import numpy as np; a = np.random.randn(1e3,1e3)' 'np.dot(a, a)' There is one saving grace, the local numpy built with MKL is much faster than the system's numpy. I hope someone can help me. Searching the internet has been fruitless. Best, Quyen My site.cfg for numpy (1.7.0) [mkl] library_dirs = /opt/intel/mkl/lib/intel64 include_dirs = /opt/intel/mkl/include mkl_libs = mkl_rt lapack_libs = I've edited line 37 of numpy/distutils/intelcompiler.py self.cc_exe = 'icc -O3 -fPIC -fp-model strict -fomit-frame-pointer -openmp -parallel -DMKL_ILP64' Also line 54 of numpy/distutils/fcompiler/intel.py return ['-i8 -xhost -openmp -fp-model strict'] My .bash_profile also contains the lines: source /opt/intel/bin/compilervars.sh intel64 source /opt/intel/mkl/bin/mklvars.sh intel64 The above is needed to set the LD_LIBRARY_PATH so that Python can source the intel dynamic library when numpy is called. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.7.0 with Intel MKL 11.0.2.146
On 3/10/13, Warren Weckesser warren.weckes...@gmail.com wrote: On 3/10/13, QT rdirect...@gmail.com wrote: Dear all, I'm at my wits end. I've followed Intel's own instructionshttp://software.intel.com/en-us/articles/numpyscipy-with-intel-mklon how to compile Numpy with Intel MKL. Everything compiled and linked fine and I've installed it locally in my user folder...There is one nasty problem. When one calls the numpy library to do some computation, it does not use all of the available threads. I have 8 cores on my machine and it only uses 4 of them. The MKL_NUM_THREADS environmental variable can be set to tune the number of threads but setting it to 8 does not change anything. Indeed, setting it to 3 does limit the threads to 3What is going on? Does your computer have 8 physical cores, or 4 cores that look like 8 because of hyperthreading? Here's why I ask this: http://software.intel.com/en-us/forums/topic/294954 Warren As a comparison, the numpy (version 1.4.1, installed from yum, which uses BLAS+ATLAS) uses all 8 threads. I do not get this. You can run this test program python -mtimeit -s'import numpy as np; a = np.random.randn(1e3,1e3)' 'np.dot(a, a)' There is one saving grace, the local numpy built with MKL is much faster than the system's numpy. I hope someone can help me. Searching the internet has been fruitless. Best, Quyen My site.cfg for numpy (1.7.0) [mkl] library_dirs = /opt/intel/mkl/lib/intel64 include_dirs = /opt/intel/mkl/include mkl_libs = mkl_rt lapack_libs = I've edited line 37 of numpy/distutils/intelcompiler.py self.cc_exe = 'icc -O3 -fPIC -fp-model strict -fomit-frame-pointer -openmp -parallel -DMKL_ILP64' Also line 54 of numpy/distutils/fcompiler/intel.py return ['-i8 -xhost -openmp -fp-model strict'] My .bash_profile also contains the lines: source /opt/intel/bin/compilervars.sh intel64 source /opt/intel/mkl/bin/mklvars.sh intel64 The above is needed to set the LD_LIBRARY_PATH so that Python can source the intel dynamic library when numpy is called. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.7.0 with Intel MKL 11.0.2.146
Dear Warren, It's an Intel i7 950, 4 cores, 8 with hyper-threading. I used MKL 11.0.2.146, but I will read your link. It seems spot on. Best, Quyen On Sun, Mar 10, 2013 at 10:31 PM, Warren Weckesser warren.weckes...@gmail.com wrote: On 3/10/13, Warren Weckesser warren.weckes...@gmail.com wrote: On 3/10/13, QT rdirect...@gmail.com wrote: Dear all, I'm at my wits end. I've followed Intel's own instructions http://software.intel.com/en-us/articles/numpyscipy-with-intel-mklon how to compile Numpy with Intel MKL. Everything compiled and linked fine and I've installed it locally in my user folder...There is one nasty problem. When one calls the numpy library to do some computation, it does not use all of the available threads. I have 8 cores on my machine and it only uses 4 of them. The MKL_NUM_THREADS environmental variable can be set to tune the number of threads but setting it to 8 does not change anything. Indeed, setting it to 3 does limit the threads to 3What is going on? Does your computer have 8 physical cores, or 4 cores that look like 8 because of hyperthreading? Here's why I ask this: http://software.intel.com/en-us/forums/topic/294954 Warren As a comparison, the numpy (version 1.4.1, installed from yum, which uses BLAS+ATLAS) uses all 8 threads. I do not get this. You can run this test program python -mtimeit -s'import numpy as np; a = np.random.randn(1e3,1e3)' 'np.dot(a, a)' There is one saving grace, the local numpy built with MKL is much faster than the system's numpy. I hope someone can help me. Searching the internet has been fruitless. Best, Quyen My site.cfg for numpy (1.7.0) [mkl] library_dirs = /opt/intel/mkl/lib/intel64 include_dirs = /opt/intel/mkl/include mkl_libs = mkl_rt lapack_libs = I've edited line 37 of numpy/distutils/intelcompiler.py self.cc_exe = 'icc -O3 -fPIC -fp-model strict -fomit-frame-pointer -openmp -parallel -DMKL_ILP64' Also line 54 of numpy/distutils/fcompiler/intel.py return ['-i8 -xhost -openmp -fp-model strict'] My .bash_profile also contains the lines: source /opt/intel/bin/compilervars.sh intel64 source /opt/intel/mkl/bin/mklvars.sh intel64 The above is needed to set the LD_LIBRARY_PATH so that Python can source the intel dynamic library when numpy is called. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
