The following code copies an array to the GPU and executes a simple operation onįunction evolve! ( currdata :: AbstractArray, prevdata :: AbstractArray, dx, dy, a, dt ) nx, ny = size ( currdata ). Us to write generic code which works on both types. The CuArray type closely resembles Base.Array which enables ( ROCArray for AMD) instead of regular Base.Array arrays. GPU programming with Julia can be as simple as using CuArray They are divided into “streaming multiprocessors” (SMs). This means that data needs to be transfered toĪnd from the GPU during the execution of a program.Ĭores in a GPU are arranged into a particular structure. The large number of compute elements on a GPU (in the thousands) can enableĮxtreme scaling for data parallel tasks (single-program multiple-data, SPMD) Some key aspects of GPUs that need to be kept in mind: In comparison and they share data, allowing to pack more cores on a single chip. Structure and packs several cores on a single chip.
This will help us understand the rationale behind the GPU programming methodsĪ comparison of CPU and GPU architectures. We first briefly discuss the hardware differences between GPUs and CPUs. Has created a Colab notebook that can be reused for Julia computing on Colab. Google Colab does not support Julia, but a JuliaHub, a commercial cloud platform fromĪccess to Julia’s ecosystem of packages and GPU hardware.Īccount and a manual Julia installation, but using simple NVIDIA GPUs is free. To an NVIDIA GPU and the necessary software stack. To fully experience the walkthrough in this episode we need to have access Of the toolkit upon the first import: using CUDA.
#Cudalaunch nvprof install
Install the drivers, and let Julia automatically install the correct version For installation on other workstations one can follow the To use the Julia GPU stack, one needs to have NVIDIA drivers installed and
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