QueensGambit edited a comment on issue #15640: Performance regression for MXNet 1.5.0 URL: https://github.com/apache/incubator-mxnet/issues/15640#issuecomment-514365409 > Also if the cp nodes depths values are different, does it mean the time will be different? It's basically the other way around. Usually, the movetime is a fixed given time and the `nodes` defines how many nodes have been created in this time within search tree. This makes the engine applicable to different hardware and time-controls. Higher `nodes` and `depths` are preferable. `cp` is a dynamically changing value evaluation which converges in theory to the true value for a particular position given infinite samples, here nodes. The nodes are reused for future board position if possible, therefore even though for cases when a network with a slightly slower nps predicted the same `bestmove`, a higher nps version has a better calibrated evaluation for possible future positions. In a simplified view, the `.asnumpy()` method is called and the prediction results of the neural network are assigned to a newly created node. https://github.com/QueensGambit/CrazyAra/blob/master/DeepCrazyhouse/src/domain/agent/player/util/net_pred_service.py#L80 Thus this speed measurement should be fairly independent from profiler update changes. Engines with higher nps are able to explore the position more deeply in the same time and have in result a higher playing strength. > In addition, if you are using mkl version, you can try these env var mentioned here:#15429 (comment) Thank you, I haven't tried optimizing all mkl hyperparameters so far, but only activated `MXNET_SUBGRAPH_BACKEND=MKLDNN` which gives a speed-up of ~16% for this particular model. I'm also planning to support low precision inference `float16`, `int8` and `Tensorrt` in the future. A C++ version of the engine will also be released soon. Here the tree-traversal and tree-management on CPU is significantly faster and the inference time of the neural network plays a much bigger factor.
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