Cutorch provides a CUDA backend for torch7.
Cutorch provides the following:
- a new tensor type:
torch.CudaTensorthat acts liketorch.FloatTensor, but all it's operations are on the GPU. Most of the tensor operations are supported by cutorch. There are a few missing ones, which are being implemented. The missing list can be found here: torch#70 cutorch.*- Functions to set/get GPU, get device properties, memory usage, set/get low-level streams, set/get random number generator's seed, synchronization etc. They are described in more detail below.
This new tensor type behaves exactly like a torch.FloatTensor, but has a couple of extra functions of note:
t:getDevice()- Given a CudaTensort, you can call :getDevice on it to find out the GPU ID on which the tensor memory is allocated.
###cutorch.* API
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cutorch.synchronize(): All of the CUDA API is asynchronous (barring a few functions), which means that you can queue up operations. To wait for the operations to finish, you can issuecutorch.synchronize()in your code, when the code waits for all GPU operations on the current GPU to finish. -
cutorch.setDevice(i): If one has multiple-GPUs, you can switch the default GPU (to allocate CUDA tensors and do operations). The GPU IDs are 1-indexed, so having 4 GPUs means, you can setDevice(1), setDevice(2), setDevice(3), setDevice(4). -
idx = cutorch.getDevice(): Returns the currently set GPU device index. -
count = cutorch.getDeviceCount(): Gets the number of availabl 781F e GPUs. -
freeMemory, totalMemory = cutorch.getMemoryUsage(devID): Gets the total and free memory in bytes for the given device ID. -
cutorch.seed([devID])- Sets and returns a random seed for the current or specified device. -
cutorch.seedAll()- Sets and returns a random seed for all available GPU devices. -
cutorch.initialSeed([devID])- Returns the seed for the current or specified device -
cutorch.manualSeed(seed [, device])- Sets a manually specified RNG seed for the current or specified device -
cutorch.manualSeedAll(seed)- Sets a manually specified RNG seed for all available GPUs -
cutorch.getRNGState([device])- returns the current RNG state in the form of a byte tensor, for the current or specified device. -
cutorch.setRNGState(state [, device])- Sets the RNG state from a previously saved state, on the current or specified device. -
cutorch.getState()- Returns the global state of the cutorch package. This state is not for users, it stores the raw RNG states, cublas handles and other thread and device-specific stuff. -
cutorch.withDevice(devID, f)- This is a convenience for multi-GPU code, that takes in a device ID as well as a function f. It switches cutorch to the new device, executes the function f, and switches back cutorch to the original device.
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. Bydefault, this is0, 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) isn. This is preserved across device switches. 1-N are user streams,0is the default stream.n = cutorch.getStream(): returns the current stream active. By default, returns0.cutorch.setDefaultStream(): an alias forcutorch.setStream(0)cutorch.streamWaitFor(streamWaiting, {streamsToWaitOn...}): A 1-to-N-way barrier.streamWaitingwill 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 tocutorch.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 viastreamWaitFor.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 tocudaStreamSynchronize(stream)for the current device. Blocks the CPU until stream completes its queued kernels/events.
Transfering a FloatTensor src to the GPU:
dest = src:cuda() -- dest is on the current GPUAllocating a tensor on a given GPU:
Allocate src on GPU 3
cutorch.setDevice(3)
src = torch.CudaTensor(100)Copying a CUDA tensor from one GPU to another:
Given a tensor called src on GPU 1, if you want to create it's clone on GPU 2, then:
cutorch.setDevice(2)
local dest = src:clone()OR
local dest
cutorch.withDevice(2, function() dest = src:clone() end)