wangwei created SINGA-131:
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Summary: Implement and optimize hybrid training using both CPU and
GPU
Key: SINGA-131
URL: https://issues.apache.org/jira/browse/SINGA-131
Project: Singa
Issue Type: Improvement
Reporter: wangwei
We discussed with researchers from Stanford on implementing hybrid training
before
http://mail-archives.apache.org/mod_mbox/singa-dev/201507.mbox/%3CCAJz0iLsd5iSCqqVU4QHLKzMO2o%2BFt-40kN8RgWkYhDn%3D6Qqqbw%40mail.gmail.com%3E.
Now with the GPU training supported, we can move on to this feature.
The distributed training framework is natural for hybrid training with CPU and
GPU. The first n workers would be assigned with GPU cards (n is the number of
cards configured by users), and the rest workers would run on CPU.
Some code may need updates and optimization to consider the memory transferring
between GPU workers and CPU workers. Most of them is in worker.cc, param.cc and
stub.cc.
Automatically tune the workload among GPU and CPU could be designed and
implemented in this ticket or a new ticket.
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