Both CPUs & GPUs provide SIMD with the most standard conceptual unit being 16 bytes/128 bits; for example a Vector of 4 floats (x,y,z,w). Simplifying: CPUs then parallelize more through pipelining future instructions so they proceed faster through a program. Then next step is multiple cores which run independent programs. GPUs on the other hand … Read more
I am the developer of VexCL, but I really like what Kyle Lutz, the author of Boost.Compute, had to say on the same subject on Boost mailing list. In short, from the user standpoint Thrust, Boost.Compute, AMD’s Bolt and probably Microsoft’s C++ AMP all implement an STL-like API, while VexCL is an expression template based … Read more
If you use GPU regularly, runtime durations will become shorter and shorter and disconnections more frequent. The cooldown period before you can connect to another GPU will extend from hours to days to weeks. Google tracks everything. They not only know your accounts’ usage but also the usage of accounts that appear related to your … Read more
The accepted answer doesn’t work for me, probably because nvidia-smi has different formats across different versions/hardware. I’m using a much cleaner command: nvidia-smi | grep ‘python’ | awk ‘{ print $3 }’ | xargs -n1 kill -9 You can replace $3 in the awk expression to fit your nvidia-smi output. It is the n-th column … Read more
1) No – not for the general case of C# – obviously anything can be created for some subset of the language 2) Yes – HLSL using Direct X or Open GL 3) Not generally possible – CPU and GPU coding are fundamentally different Basically you can’t think of CPU and GPU coding as being … Read more
PyTorch can provide you total, reserved and allocated info: t = torch.cuda.get_device_properties(0).total_memory r = torch.cuda.memory_reserved(0) a = torch.cuda.memory_allocated(0) f = r-a # free inside reserved Python bindings to NVIDIA can bring you the info for the whole GPU (0 in this case means first GPU device): from pynvml import * nvmlInit() h = nvmlDeviceGetHandleByIndex(0) info … Read more
UPDATE GPU Version __global__ void hash (float *largeFloatingPointArray,int largeFloatingPointArraySize, int *dictionary, int size, int num_blocks) { int x = (threadIdx.x + blockIdx.x * blockDim.x); // Each thread of each block will float y; // compute one (or more) floats int noOfOccurrences = 0; int a; while( x < size ) // While there is work … Read more
I think GeForce TITAN is great and is widely used in Machine Learning (ML). In ML, single precision is enough in most of cases. More detail on the performance of the GTX line (currently GeForce 10) can be found in Wikipedia, here. Other sources around the web support this claim. Here is a quote from … Read more
If you develop in Visual Studio 2013 and 2015 versions, you can use their GPU Usage tool: GPU Usage Tool in Visual Studio (video) https://www.youtube.com/watch?v=Gjc5bPXGkTE GPU Usage Visual Studio 2015 https://msdn.microsoft.com/en-us/library/mt126195.aspx GPU Usage tool in Visual Studio 2013 Update 4 CTP1 (blog) http://blogs.msdn.com/b/vcblog/archive/2014/09/05/gpu-usage-tool-in-visual-studio-2013-update-4-ctp1.aspx GPU Usage for DirectX in Visual Studio (blog) http://blogs.msdn.com/b/ianhu/archive/2014/12/16/gpu-usage-for-directx-in-visual-studio.aspx Screenshot from MSDN: … Read more
As mentioned in the docs, XLA stands for “accelerated linear algebra”. It’s Tensorflow’s relatively new optimizing compiler that can further speed up your ML models’ GPU operations by combining what used to be multiple CUDA kernels into one (simplifying because this isn’t that important for your question). To your question, my understanding is that XLA … Read more