THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Figure 1. Example for parallel embedding
Take our the wide deep model for example, after split, data flow is divided to 26 and each of them will be handled by single embedding OP. In ordinary process, these 26 embedding OPs will be executed one by one when running inference, and data parallel will be used in its kernel function. Now we replace the 26 OPS using one parallel OP which can handle inference in OP level parallel.
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We implement paralle_op based on subgraph API. The main body of parallel op forward function is accelerate by OMP multithread as Figure3. This means origin OP forward function should be thread safe. As mentioned in step 4, OP whitelist is used to check if OP support thread safe. And whitelist can be add/remove in future by setting environment variables.
Figure 3. Main body of parallel OP forward.
To get the best performance, we need to support nested OMP and fine tune the parameters. In current version, we just simplify it by disable nested OMP. Environment variable may be added to support fine tune the performance in future release.
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