Octave [1] is a great platform to perform Machine Learning tasks. Octave run time performance can be improved by adding OpenBlas [2] and by increasing the number of threads Octave can run using. This can be further improved using NVIDIA's cuBLAS [3]. The following steps would work in Ubuntu OS.
For the purposes of this test, the matrix multiplication Octave code from [3] was used. To increase the number of threads and using OpenBlas, run the octave code in a command line in a terminal as:
The performance is best when OMP_NUM_THREADS=#coresOnTheMachine (Obviously.).
[2] OpenBlas: http://www.openblas.net/
[3] Drop-in Acceleration of GNU Octave. https://devblogs.nvidia.com/parallelforall/drop-in-acceleration-gnu-octave/
For the purposes of this test, the matrix multiplication Octave code from [3] was used. To increase the number of threads and using OpenBlas, run the octave code in a command line in a terminal as:
- export LD_LIBRARY_PATH=/opt/OpenBLAS
- OMP_NUM_THREADS=<NumThreads> LD_PRELOAD=/opt/OpenBLAS/lib/libopenblas.so octave code.m
Results
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| Matrix Multiplication without OpenBlas. Note: Y-axis is in log scale. |
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| Improved Performance Using OpenBlas. Note: Y-axis is in log scale. |
The performance is best when OMP_NUM_THREADS=#coresOnTheMachine (Obviously.).
References
[1] GNU Octave: https://www.gnu.org/software/octave/[2] OpenBlas: http://www.openblas.net/
[3] Drop-in Acceleration of GNU Octave. https://devblogs.nvidia.com/parallelforall/drop-in-acceleration-gnu-octave/
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