OpenCL is more flexible than CUDA to allow programs to be executed on different architectures. The centrepiece of OpenCL is a kernel, which is a function (written in a C-like language) that can be applied to chunks of input data.įor an introduction to parallel programming follow this online course based on CUDA, or buy this book by Tim Mattson et al. The project PyOpenCL is probably the easiest way to get started with GP-GPU on a Mac.
CUDA FOR MAC INSTALL
To install CUDA on MacOs follow the official documentation. NVIDIA external GPU cards (eGPU) can be used by a MacOS systems with a Thunderbolt 3 port and MacOS High Sierra 10.13.4 or later. The two most popular ML frameworks Keras and PyTorch support GPU acceleration based on the general-purpose GPU library NVIDIA CUDA. While there isn’t a solution for all possible applications, there is still a workaround for simple architectures based on a parallel programming language called OpenCL. Unfortunately, this kind of hardware can not be used directly to speed-up calculations that are typical in Machine Learning applications, such as training a CNN.
CUDA FOR MAC PRO
IMac and MacBook Pro compu ters are equipped with an AMD Radeon GPU card. One rule of thumb to remember is that 1K CPUs = 16K cores = 3GPUs, although the kind of operations a CPU can perform vastly outperforms those of a single GPU core. This is the quintessential massively parallel operation, which constitutes one of the main reasons why GPUs are vital to Machine Learning. Training a neural network involves a very large number of matrix multiplications. Otherwise, you are probably in the right place, so keep reading! If your code involves the generation of random numbers, parallel programming may not be the best solution (however, see here). If your code involves a number of if or case statements, you may want to run on a CPU using e.g. Massively parallel programming is very useful to speed up calculations where the same operation is applied multiple times on similar inputs. This is a good solution to do light ML development on a Mac without a NVIDIA eGPU card.
PlaidML is a software framework that enables Keras to execute calculations on a GPU using OpenCL instead of CUDA.
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