kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-593952925
The quantization operator is now parallelized with OpenMP and supports an
arbitrary number of arguments. It
kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-590363395
While I agree with the principle that all operators should be parallel (and
intend to parallelize mine),
kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-590265641
@ciyongch Here is a usage example.
```python
import mxnet as mx
#This is done offline.
weight =
kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-590256391
@pengzhao-intel Which OMP do you recommend? I've been getting bad OMP
results with a stock install of
kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-587073135
Also, OMP performance is very bad. (NB: intgemm is running single-threaded
here, partly because OMP is bad
kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-587066870
The current MXNet quantizer is 3-10x slower than intgemm's on a wide variety
of matrix sizes.
kpuatamazon commented on issue #17559: [MXNET-1446] Quantization: intgemm
matrix multiply wrappers
URL: https://github.com/apache/incubator-mxnet/pull/17559#issuecomment-587020032
I'm the same person as @kpu but work part time as @kpuatamazon. Typically
you'll hear from my Amazon hat on
