Generally speaking, everything you see via your computer that doesn’t belong to the local device is “downloaded”. What to do if the download speed is not fast enough? How can we speed up torrent downloads? Keep on reading. But there is always a way to solve the problem. When we download torrents, we are sure to encounter some issues which include slow torrent downloading speed. Through torrents, we can get all kinds of resources from all over the world. Torrents have brought a lot of convenience to our life. The link to the example notebook is here: dnokes/pytorch_examples/blob/master/simpleGpuVsCpuExample.Torrenting is a method that is used quite frequently in our life. Print('EMA Time Elasped (CPU): '+str(timeElasped_emaCPU)) PricePathsGPU_CPU=pricePathsGPU.cpu().numpy()ĮmaCPU=emaNPathsCPU(pricePathsGPU_CPU,lookbackCPU)
Print('EMA Time Elasped (GPU): '+str(timeElasped_emaGPU)) LookbackGPU=torch.tensor(90.0,dtype=dtype,device=cuda0)ĮmaGPU=emaNPathsGPU(pricePathsGPU,lookbackGPU,dtype,cuda0) Here is how I call them: cuda0 = vice('cuda:0') # iterate over each point in time and compute the EMAĮma=a * (pricePaths-ema[t-1,ĭef emaNPathsGPU(pricePaths,lookback,dtype,device):Įma=torch.zeros(T,nPaths,dtype=dtype,device=device)
These are the simple functions - one for the CPU and one for the GPU: def emaNPathsCPU(pricePaths,lookback):Įma = pricePaths PricePaths=torch.exp(torch.cumsum(torch.cat((torch.log(S0)*torch.ones(1,nPaths,dtype=dtype,device=cuda0), PricePaths=numpy.exp(numpy.cumsum(x,axis=0))ĭef assetPathsGPU(S0,mu,sigma,T,nRows,nPaths,dtype,device): X=ncatenate((math.log(S0)*numpy.ones((1,int(nPaths))),increments)) Increments = nudt + sidt*(int(nRows),int(nPaths)) The following functions are to create data to use in the simple example further below: import numpyįrom timeit import default_timer as timerĭef assetPathsCPU(S0,mu,sigma,T,nRows,nPaths):
I am looking for some guidance as to how to speed up the following simple code. I have been trying to use PyTorch to speed up some simple embarrassingly parallel computations with little success.
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