• Credit to Zhennan for this proposal 

Problem

Although data parallel is used in MXNet, its performance is not good enough for the less computational intensive operators in the inference stage, especially for the small batchsize. This phenomena widely exists in many popular models, such as googlenet, wide deep and inception v3. For example in the wide deep model, 26 embedding OPs are executed in sequence and each one only consumes very little computing resource. So, the model level performance is sub-optimal since the long execution path in the low-parallelism operators.

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The key bock in Fig.2 is Filter which is used check whether the finding parallel structure meet the metrics. It must guarantee that the operator is thread safe; otherwise, it may fails during simultaneous execution by multiple threads.  From MKL-DNN 1.0 all MKLDNN operators will be thread safe and can be executed in parallel. But now, we need to maintain a whitelist for thread safe operators. There are some other conditions which used to fine tune the performance such as paralleled Node number >= threshold or OPs will  will cause performance drop. Environment variable may be add by user to add/remove whitelists in future release.

The main body of parallel op forward function is accelerate by OMP multithread as Figure3. When do the inference, several op runs operators run parallel. In our Such as in the wide deep model, 26 embedding forward function are called simultaneously. By this parallel in OP level, performance is improved a lot.

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