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Fast Kernel SVM on TensorCore enabled GPU
if the #features if very large (>10k) the dense matrix representation is not feasible both in terms of memory space and computation.
Need sparse matrix representation.
fixed in commit a6700a1
The reason is that 400k dataset triggers chunking (NN=100k), but the C=Q'*W did not accumulate (in SGEMM(), the beta was 0, should have been 1).
Hardware/Software requirement
Short description
How to train
How to predict
iter 8, mu=1.311e-06, normdx=3.811e-03, normdy=4.306e-03 max/min(D)=1.925e+16 pobj=-5.396244462e+08 dobj=-5.396244536e+08
MPC elapsed time = 362 seconds
Writemodel #BSV 2500092, #SV 4608000
Segmentation fault
When running on SUSY with rank 512, here is a segmentation fault when writing the model
Using the whole dataset, the Chol(C) fails very easily with
make it compatible with LIBSVM/ThunderSVM.
How do we choose good hyperparameters such as gamma, C, and rank k?
right now, the size of data (n) must be divisble by the number of chunks (nb).
This only applies to large datasets, but nonetheless should be fixed.
Enable TensorCore in LRA.
Test the stability.
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