triplekings edited a comment on issue #14492: use mkl sparse matrix to improve performance URL: https://github.com/apache/incubator-mxnet/pull/14492#issuecomment-477504457 > Great! Thanks for your contribution! > Is there any test case for the sparse matrix? > Could you please provide the comparison on performance? Thanks for your comments. testcase: tests/python/unittest/test_sparse_ndarray.py tests/python/unittest/test_sparse_operator.py using mkl , sparse dot will speedup 1.3X With mkl sparse INFO:logger:Run [7812] Batchs Speed: 941008.89 samples/sec (mxnet_venv) [root@VNNI-SDP86 scripts_zhenlin]# python myProfileParser.py Time of each OP: _contrib_quantized_fully_connected, 4162.701, ms, 0.1776199436763953 , ms/call, 23436, calls, 54.45 % Concat , 870.905 , ms, 0.11148297491039426 , ms/call, 7812 , calls, 11.39 % dot , 554.544 , ms, 0.07098617511520737 , ms/call, 7812 , calls, 7.25 % elemwise_add , 394.928 , ms, 0.05055401945724526 , ms/call, 7812 , calls, 5.17 % _contrib_quantize , 321.782 , ms, 0.04119073220686124 , ms/call, 7812 , calls, 4.21 % SoftmaxOutput , 309.333 , ms, 0.03959715821812596 , ms/call, 7812 , calls, 4.05 % ParallelEmbedding , 266.09 , ms, 0.03406169994879672 , ms/call, 7812 , calls, 3.48 % CopyCPU2CPU , 244.173 , ms, 0.010418714797747057 , ms/call, 23436, calls, 3.19 % broadcast_add , 177.62 , ms, 0.022736815156169994 , ms/call, 7812 , calls, 2.32 % SliceChannel , 151.811 , ms, 0.01943305171530978 , ms/call, 7812 , calls, 1.99 % slice , 124.944 , ms, 0.007996927803379416 , ms/call, 15624, calls, 1.63 % DeleteVariable , 66.723 , ms, 0.0021352726574500767, ms/call, 31248, calls, 0.87 % With mxnet sparse INFO:logger:Run [7812] Batchs Speed: 915862.99 samples/sec (mxnet_venv) [root@VNNI-SDP86 scripts_zhenlin]# python myProfileParser.py Time of each OP: _contrib_quantized_fully_connected, 4235.11, ms, 0.1807095920805598 , ms/call, 23436, calls, 53.72 % Concat , 879.878, ms, 0.112631592421915 , ms/call, 7812 , calls, 11.16 % dot , 724.61 , ms, 0.09275601638504864 , ms/call, 7812 , calls, 9.19 % elemwise_add , 404.946, ms, 0.05183640552995392 , ms/call, 7812 , calls, 5.14 % _contrib_quantize , 332.088, ms, 0.0425099846390169 , ms/call, 7812 , calls, 4.21 % SoftmaxOutput , 280.663, ms, 0.03592716333845366 , ms/call, 7812 , calls, 3.56 % ParallelEmbedding , 271.335, ms, 0.034733102918586785 , ms/call, 7812 , calls, 3.44 % CopyCPU2CPU , 244.757, ms, 0.01044363372589179 , ms/call, 23436, calls, 3.10 % broadcast_add , 181.36 , ms, 0.02321556579621096 , ms/call, 7812 , calls, 2.30 % SliceChannel , 153.897, ms, 0.019700076804915513 , ms/call, 7812 , calls, 1.95 % slice , 124.163, ms, 0.007946940604198668 , ms/call, 15624, calls, 1.57 % DeleteVariable , 50.809 , ms, 0.0021679894179894178, ms/call, 23436, calls, 0.64 % Total OP Time: 7883.61600000 ms
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