As planed in the previous discussion, we are stabilizing the APIs of each module. One problem I am encountered is about the communication APIs to support GPUs.
We can use some libraries like cudamat (https://code.google.com/p/cudamat/) for linear algebra computation. Hence, the APIs on computation would almost the same as those for CPU. But I have poor knowledge on the communication between GPU and CPU, and the communication between GPUs. I am asking you for your suggestions. Wangyuan, Wuwei and Haibo: Since you are working on deep learning using GPUs, it would be appreciated if you can give some feedback. As far as I know that traditionally messages are transferred from GPU memory to CPU memory and then transferred through TCP/IP to other nodes and then transferred from CPU memory to GPU memory. We can easily support such communication using the current APIs for CPU. But the transferring between GPU and CPU would bring extra cost. NVDIA has provided a technique called GPUDirect, which enables directly message passing from GPU memory to network (e.g., infiniband) card. Some MPI variants now use this technique. But we have switched from MPI to ZeroMQ, we need to make sure that ZeroMQ supports GPUDirect and Infiniband. Do you have any investigations on this? Or how do you implement the message transferring in your implementation? Thanks. regards, Wei
