Here are 15 BitTorrent clients for your consideration. The right client software can make downloading a painless experience, while a poorly built one can be a hassle and security risk. BitTorrent can make it easier to download everything from books, data and documents to software and other media, but it's only as easy as the program you use. The link to the example notebook is here: dnokes/pytorch_examples/blob/master/simpleGpuVsCpuExample.The BitTorrent protocol is a peer-to-peer sharing system that allows users all over the world to download and share data by essentially farming out file distribution and hosting to users instead of relying on a host or content mirrors. 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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