A CUDA backend for Torch7

Low-level streams functions (dont use this as a user, easy to shoot yourself in the foot):

• cutorch.reserveStreams(n): creates n user streams for use on every device.
• n = cutorch.getNumStreams(): returns the number of user streams available on every device. By default, this is 0, meaning only the default stream (stream 0) is available.
• cutorch.setStream(n): specifies that the current stream active for the current device (or any other device) is n. This is preserved across device switches. 1-N are user streams, 0 is the default stream.
• n = cutorch.getStream(): returns the current stream active. By default, returns 0.
• cutorch.setDefaultStream(): an alias for cutorch.setStream(0)
• cutorch.streamWaitFor(streamWaiting, {streamsToWaitOn...}): A 1-to-N-way barrier. streamWaiting will wait for the list of streams specified to finish executing all kernels/events/barriers. Does not block any of the streamsToWaitOn. Current device only.
• cutorch.streamWaitForMultiDevice(deviceWaiting, streamWaiting, {[device]={streamsToWaitOn...}...}): (deviceWaiting, streamWaiting) will wait on the list of (device, streams...) pairs; handles single or multiple device. cutorch.streamWaitForMultiDevice, a, b, {[a]={streams...}}) is equivalent to cutorch.setDevice(a); cutorch.streamWaitFor(b, {streams...}).
• cutorch.streamBarrier({streams...}): an N-to-N-way barrier between all the streams; all streams will wait for the completion of all other streams on the current device only. More efficient than creating the same N-to-N-way dependency via streamWaitFor.
• cutorch.streamBarrierMultiDevice({[device]={streamsToWaitOn...}...}): As with streamBarrier but allows barriers between streams on arbitrary devices. Creates a cross-device N-to-N-way barrier between all (device, stream) values listed.
• cutorch.streamSynchronize(stream): equivalent to cudaStreamSynchronize(stream) for the current device. Blocks the CPU until stream completes its queued kernels/events.