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March 29, 2012 21:16
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output of check_blas.py using various BLAS configurations
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| (x86_64-apple-darwin10.8.0)dhcp0-176:Theano gwtaylor$ THEANO_FLAGS=blas.ldflags=-lblas python theano/misc/check_blas.py | |
| Some results that you can compare against. They were 10 executions | |
| of gemm in float64 with matrices of shape 2000x2000 (M=N=K=2000). | |
| All memory layout was in C order. | |
| CPU tested: Xeon E5345(2.33Ghz, 8M L2 cache, 1333Mhz FSB), | |
| Xeon E5430(2.66Ghz, 12M L2 cache, 1333Mhz FSB), | |
| Xeon E5450(3Ghz, 12M L2 cache, 1333Mhz FSB), | |
| Xeon X5560(2.8Ghz, 12M L2 cache, hyper-threads?) | |
| Core 2 E8500, Core i7 930(2.8Ghz, hyper-threads enabled), | |
| Core i7 950(3.07GHz, hyper-threads enabled) | |
| Xeon X5550(2.67GHz, 8M l2 cache?, hyper-threads enabled) | |
| Libraries tested: | |
| * numpy with ATLAS from distribution (FC9) package (1 thread) | |
| * manually compiled numpy and ATLAS with 2 threads | |
| * goto 1.26 with 1, 2, 4 and 8 threads | |
| * goto2 1.13 compiled with multiple threads enabled | |
| Xeon Xeon Xeon Core2 i7 i7 Xeon Xeon | |
| lib/nb threads E5345 E5430 E5450 E8500 930 950 X5560 X5550 | |
| numpy 1.3.0 blas 775.92s | |
| numpy_FC9_atlas/1 39.2s 35.0s 30.7s 29.6s 21.5s 19.60s | |
| goto/1 18.7s 16.1s 14.2s 13.7s 16.1s 14.67s | |
| numpy_MAN_atlas/2 12.0s 11.6s 10.2s 9.2s 9.0s | |
| goto/2 9.5s 8.1s 7.1s 7.3s 8.1s 7.4s | |
| goto/4 4.9s 4.4s 3.7s - 4.1s 3.8s | |
| goto/8 2.7s 2.4s 2.0s - 4.1s 3.8s | |
| openblas/1 14.04s | |
| openblas/2 7.16s | |
| openblas/4 3.71s | |
| openblas/8 3.70s | |
| mkl 11.0.083/1 7.97s | |
| mkl 10.2.2.025/1 13.7s | |
| mkl 10.2.2.025/2 7.6s | |
| mkl 10.2.2.025/4 4.0s | |
| mkl 10.2.2.025/8 2.0s | |
| goto2 1.13/1 14.37s | |
| goto2 1.13/2 7.26s | |
| goto2 1.13/4 3.70s | |
| goto2 1.13/8 1.94s | |
| goto2 1.13/16 3.16s | |
| Test time in float32 with cuda 3.0.14 | |
| (cuda version 3.2RC and up have a faster gemm on the Fermi/GTX[45]??) | |
| gpu/cuda version | |
| GTX580/3.2 0.20s | |
| GTX480/3.2 0.24s | |
| GTX480/3.0 0.27s | |
| GTX470/3.2 0.29s | |
| M2070/3.2 0.32s | |
| GTX470/3.0 0.34s | |
| GTX285/3.0 0.40s | |
| C1060/3.2 0.46s | |
| GTX550Ti/4.0 0.57s | |
| GT220/3.2RC 3.80s | |
| GT210/4.0 6.35s | |
| 8500GT/3.0 10.68s | |
| Some Theano flags: | |
| blas.ldflags= -lblas | |
| compiledir= /Users/gwtaylor/.theano/compiledir_Darwin-10.8.0-i386-64bit-i386-2.7.2 | |
| floatX= float64 | |
| Some environment variables: | |
| MKL_NUM_THREADS= 2 | |
| OMP_NUM_THREADS= None | |
| GOTO_NUM_THREADS= None | |
| Numpy config: (used when the Theano flag "blas.ldflags" is empty) | |
| lapack_opt_info: | |
| libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| blas_opt_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| lapack_mkl_info: | |
| libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| blas_mkl_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| mkl_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| Numpy dot module: numpy.core._dotblas | |
| Numpy location: /Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/numpy/__init__.py | |
| Numpy version: 1.6.1 | |
| We executed 10 calls to gemm with a and b matrices of shapes (2000, 2000) and (2000, 2000). | |
| Total execution time: 10.81s on CPU (with direct Theano binding to blas). | |
| Try to run this script a few times. Experience shows that the first time is not as fast as followings calls. The difference is not big, but consistent. |
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| (x86_64-apple-darwin10.8.0)dhcp0-176:Theano gwtaylor$ THEANO_FLAGS=blas.ldflags= python theano/misc/check_blas.py | |
| Some results that you can compare against. They were 10 executions | |
| of gemm in float64 with matrices of shape 2000x2000 (M=N=K=2000). | |
| All memory layout was in C order. | |
| CPU tested: Xeon E5345(2.33Ghz, 8M L2 cache, 1333Mhz FSB), | |
| Xeon E5430(2.66Ghz, 12M L2 cache, 1333Mhz FSB), | |
| Xeon E5450(3Ghz, 12M L2 cache, 1333Mhz FSB), | |
| Xeon X5560(2.8Ghz, 12M L2 cache, hyper-threads?) | |
| Core 2 E8500, Core i7 930(2.8Ghz, hyper-threads enabled), | |
| Core i7 950(3.07GHz, hyper-threads enabled) | |
| Xeon X5550(2.67GHz, 8M l2 cache?, hyper-threads enabled) | |
| Libraries tested: | |
| * numpy with ATLAS from distribution (FC9) package (1 thread) | |
| * manually compiled numpy and ATLAS with 2 threads | |
| * goto 1.26 with 1, 2, 4 and 8 threads | |
| * goto2 1.13 compiled with multiple threads enabled | |
| Xeon Xeon Xeon Core2 i7 i7 Xeon Xeon | |
| lib/nb threads E5345 E5430 E5450 E8500 930 950 X5560 X5550 | |
| numpy 1.3.0 blas 775.92s | |
| numpy_FC9_atlas/1 39.2s 35.0s 30.7s 29.6s 21.5s 19.60s | |
| goto/1 18.7s 16.1s 14.2s 13.7s 16.1s 14.67s | |
| numpy_MAN_atlas/2 12.0s 11.6s 10.2s 9.2s 9.0s | |
| goto/2 9.5s 8.1s 7.1s 7.3s 8.1s 7.4s | |
| goto/4 4.9s 4.4s 3.7s - 4.1s 3.8s | |
| goto/8 2.7s 2.4s 2.0s - 4.1s 3.8s | |
| openblas/1 14.04s | |
| openblas/2 7.16s | |
| openblas/4 3.71s | |
| openblas/8 3.70s | |
| mkl 11.0.083/1 7.97s | |
| mkl 10.2.2.025/1 13.7s | |
| mkl 10.2.2.025/2 7.6s | |
| mkl 10.2.2.025/4 4.0s | |
| mkl 10.2.2.025/8 2.0s | |
| goto2 1.13/1 14.37s | |
| goto2 1.13/2 7.26s | |
| goto2 1.13/4 3.70s | |
| goto2 1.13/8 1.94s | |
| goto2 1.13/16 3.16s | |
| Test time in float32 with cuda 3.0.14 | |
| (cuda version 3.2RC and up have a faster gemm on the Fermi/GTX[45]??) | |
| gpu/cuda version | |
| GTX580/3.2 0.20s | |
| GTX480/3.2 0.24s | |
| GTX480/3.0 0.27s | |
| GTX470/3.2 0.29s | |
| M2070/3.2 0.32s | |
| GTX470/3.0 0.34s | |
| GTX285/3.0 0.40s | |
| C1060/3.2 0.46s | |
| GTX550Ti/4.0 0.57s | |
| GT220/3.2RC 3.80s | |
| GT210/4.0 6.35s | |
| 8500GT/3.0 10.68s | |
| Some Theano flags: | |
| blas.ldflags= | |
| compiledir= /Users/gwtaylor/.theano/compiledir_Darwin-10.8.0-i386-64bit-i386-2.7.2 | |
| floatX= float64 | |
| Some environment variables: | |
| MKL_NUM_THREADS= 2 | |
| OMP_NUM_THREADS= None | |
| GOTO_NUM_THREADS= None | |
| Numpy config: (used when the Theano flag "blas.ldflags" is empty) | |
| lapack_opt_info: | |
| libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| blas_opt_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| lapack_mkl_info: | |
| libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| blas_mkl_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| mkl_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| Numpy dot module: numpy.core._dotblas | |
| Numpy location: /Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/numpy/__init__.py | |
| Numpy version: 1.6.1 | |
| We executed 10 calls to gemm with a and b matrices of shapes (2000, 2000) and (2000, 2000). | |
| Total execution time: 8.10s on CPU (without direct Theano binding to blas but with numpy/scipy binding to blas). | |
| Try to run this script a few times. Experience shows that the first time is not as fast as followings calls. The difference is not big, but consistent. |
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| (x86_64-apple-darwin10.8.0)dhcp0-176:Theano gwtaylor$ DYLD_FALLBACK_LIBRARY_PATH=/Library/Frameworks/EPD64.framework/Versions/Current/lib python theano/misc/check_blas.py | |
| Some results that you can compare against. They were 10 executions | |
| of gemm in float64 with matrices of shape 2000x2000 (M=N=K=2000). | |
| All memory layout was in C order. | |
| CPU tested: Xeon E5345(2.33Ghz, 8M L2 cache, 1333Mhz FSB), | |
| Xeon E5430(2.66Ghz, 12M L2 cache, 1333Mhz FSB), | |
| Xeon E5450(3Ghz, 12M L2 cache, 1333Mhz FSB), | |
| Xeon X5560(2.8Ghz, 12M L2 cache, hyper-threads?) | |
| Core 2 E8500, Core i7 930(2.8Ghz, hyper-threads enabled), | |
| Core i7 950(3.07GHz, hyper-threads enabled) | |
| Xeon X5550(2.67GHz, 8M l2 cache?, hyper-threads enabled) | |
| Libraries tested: | |
| * numpy with ATLAS from distribution (FC9) package (1 thread) | |
| * manually compiled numpy and ATLAS with 2 threads | |
| * goto 1.26 with 1, 2, 4 and 8 threads | |
| * goto2 1.13 compiled with multiple threads enabled | |
| Xeon Xeon Xeon Core2 i7 i7 Xeon Xeon | |
| lib/nb threads E5345 E5430 E5450 E8500 930 950 X5560 X5550 | |
| numpy 1.3.0 blas 775.92s | |
| numpy_FC9_atlas/1 39.2s 35.0s 30.7s 29.6s 21.5s 19.60s | |
| goto/1 18.7s 16.1s 14.2s 13.7s 16.1s 14.67s | |
| numpy_MAN_atlas/2 12.0s 11.6s 10.2s 9.2s 9.0s | |
| goto/2 9.5s 8.1s 7.1s 7.3s 8.1s 7.4s | |
| goto/4 4.9s 4.4s 3.7s - 4.1s 3.8s | |
| goto/8 2.7s 2.4s 2.0s - 4.1s 3.8s | |
| openblas/1 14.04s | |
| openblas/2 7.16s | |
| openblas/4 3.71s | |
| openblas/8 3.70s | |
| mkl 11.0.083/1 7.97s | |
| mkl 10.2.2.025/1 13.7s | |
| mkl 10.2.2.025/2 7.6s | |
| mkl 10.2.2.025/4 4.0s | |
| mkl 10.2.2.025/8 2.0s | |
| goto2 1.13/1 14.37s | |
| goto2 1.13/2 7.26s | |
| goto2 1.13/4 3.70s | |
| goto2 1.13/8 1.94s | |
| goto2 1.13/16 3.16s | |
| Test time in float32 with cuda 3.0.14 | |
| (cuda version 3.2RC and up have a faster gemm on the Fermi/GTX[45]??) | |
| gpu/cuda version | |
| GTX580/3.2 0.20s | |
| GTX480/3.2 0.24s | |
| GTX480/3.0 0.27s | |
| GTX470/3.2 0.29s | |
| M2070/3.2 0.32s | |
| GTX470/3.0 0.34s | |
| GTX285/3.0 0.40s | |
| C1060/3.2 0.46s | |
| GTX550Ti/4.0 0.57s | |
| GT220/3.2RC 3.80s | |
| GT210/4.0 6.35s | |
| 8500GT/3.0 10.68s | |
| Some Theano flags: | |
| blas.ldflags= -L/Users/gwtaylor/virtualenvs/x86_64-apple-darwin10.8.0/bin/../lib -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -lmkl_mc -lmkl_mc3 -lpthread | |
| compiledir= /Users/gwtaylor/.theano/compiledir_Darwin-10.8.0-i386-64bit-i386-2.7.2 | |
| floatX= float64 | |
| Some environment variables: | |
| MKL_NUM_THREADS= 2 | |
| OMP_NUM_THREADS= None | |
| GOTO_NUM_THREADS= None | |
| Numpy config: (used when the Theano flag "blas.ldflags" is empty) | |
| lapack_opt_info: | |
| libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| blas_opt_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| lapack_mkl_info: | |
| libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| blas_mkl_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| mkl_info: | |
| libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread'] | |
| library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib'] | |
| define_macros = [('SCIPY_MKL_H', None)] | |
| include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include'] | |
| Numpy dot module: numpy.core._dotblas | |
| Numpy location: /Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/numpy/__init__.py | |
| Numpy version: 1.6.1 | |
| We executed 10 calls to gemm with a and b matrices of shapes (2000, 2000) and (2000, 2000). | |
| Total execution time: 7.50s on CPU (with direct Theano binding to blas). | |
| Try to run this script a few times. Experience shows that the first time is not as fast as followings calls. The difference is not big, but consistent. |
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