Come for an introduction to programming the GPU by the lead architect of CUDA. CUDA's unique in being a programming language designed and built hand-in-hand with the hardware that it runs on. Stepping up from last year's "How GPU Computing Works" deep dive into the architecture of the ...
down in the speaker's previous GTC talks "How GPU Computing Works" and "How CUDA Programming Works" (although there is no requirement to have seen them), we'll start from first principles to apply everything we know about parallel and GPU programming to create a CUDA application from ...
cuda_learning learning how CUDA works project list: custom op [Done] CUDA 编程基础 memory & reduction [Done] GPU的内存体系及其优化指南 Gemm [Done] 通用矩阵乘法:从入门到熟练 Transformer [Done] 基础算子: LayerNorm 算子的 CUDA 实现与优化 SoftMax 算子的 CUDA 实现与优化 Cross Entropy 的 ...
As we know, we can use LD_PRELOAD to intercept the CUDA driver API, and through the example code provided by the Nvidia, I know that CUDA Runtime symbols cannot be hooked but the underlying driver ones can, so can I get the conclusion “CUDA runtime API will call driver API”? And ...
You should probably be having your cudaGraphicsGLRegisterBuffer call done in the host code, and have your CUDA<–>OpenGL include header(s), such as “cuda_gl_interop.h”. I would highly advise looking at an existing project that works, and building off of their framework. Take a look at...
CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran. In the previous three posts of this CUDA Fortran series we laid the groundwork for the major thrust of the series: how to optimize CUDA Fortran code. In ...
All device operations (kernels and data transfers) in CUDA run in a stream. When no stream is specified, the default stream (also called the “null stream”) is used. The default stream is different from other streams because it is a synchronizing stream with respect to operations on the ...
Mono is an Open Source free programming language project. It is an implementation of Microsoft’s .NET Framework based on the European association for standardizing information and communication systems (ECMA) standards for C# language and Common Language Runtime (CLR). The MonoC#compiler was started...
int) if random_action else torch.argmax(output)][0] if torch.cuda.is_available(): # put on GPU if CUDA is available action_index = action_index.cuda() action[action_index] = 1 # get next state and reward image_data_1, reward, terminal = game_state.frame_step(action) image_data...
to launch each batchtrain_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=4) # Create a Resnet model, loss function, and optimizer objects. To run on GPU, move model and loss to a GPU devicedevice = torch.device("cuda:0")...