Solving Systems of Stochastic PDEs and using GPUs in Julia
December 11 2017 in CUDA, Differential Equations, Julia, Mathematics, Stochastics | Tags: | Author: Christopher Rackauckas
What I want to describe in this post is how to solve stochastic PDEs in Julia using GPU parallelism. I will go from start to finish, describing how to use the type-genericness of the DifferentialEquations.jl library in order to write a code that uses within-method GPU-parallelism on the system of PDEs. This is mostly a proof of concept: the most efficient integrators for this problem are not compatible with GPU parallelism yet, and the GPU parallelism isn’t fully efficient yet. However, I thought it would be nice to show an early progress report showing that it works and what needs to be fixed in Base Julia and various libraries for us to get the full efficiency.
Edit May 2019
As of DifferentialEquations.jl v6.4.0, this is no longer a proof of concept. The whole library, including implicit solvers with GMRES, etc., and … READ MORE
DifferentialEquations.jl 2.0: State of the Ecosystem
May 8 2017 in CUDA, Differential Equations, HPC, Julia, Mathematics, Stochastics, Uncategorized | Tags: | Author: Christopher Rackauckas
In this blog post I want to summarize what we have accomplished with DifferentialEquations’ 2.0 release and detail where we are going next. I want to put the design changes and development work into a larger context so that way everyone can better understand what has been achieved, and better understand how we are planning to tackle our next challenges.
If you find this project interesting and would like to support our work, please star our Github repository. Thanks!
Now let’s get started.
DifferentialEquations.jl 1.0: The Core
Before we start talking about 2.0, let’s understand first what 1.0 was all about. DifferentialEquations.jl 1.0 was about answering a single question: how can we put the wide array of differential equations into one simple and efficient interface. The result of this was the common interface explained in the first blog post. Essentially, we created … READ MORE
Multiple-GPU Parallelism on the HPC with Julia
February 28 2016 in CUDA, HPC, Julia, Programming | Tags: CUDA, gpu, HPC, julia | Author: Christopher Rackauckas
This is the exciting Part 3 to using Julia on an HPC. First I got you started with using Julia on multiple nodes. Second, I showed you how to get the code running on the GPU. That gets you pretty far. However, if you got a trial allocation on Cometand started running jobs, you may have noticed when looking at the architecture that you’re not getting to use the full GPU. In the job script I showed you, I asked for 2 GPUs. Why? Well, that’s because the flagship NVIDIA GPU, the Tesla K80, is actually a duel GPU and you have to control the two parts separately. You may have been following along on your own computer and have been wondering how you use the multiple GPUs in your setup as well. This tutorial will … READ MORE
Tutorial for Julia on the HPC with GPUs
February 23 2016 in CUDA, HPC, Julia, Programming | Tags: CUDA, gpu, HPC, julia | Author: Christopher Rackauckas
This is a continuous of my previous post on using Julia on the XSEDE Comet HPC. Check that out first for an explanation of the problem. In that problem, we wished to solve for the area of a region where a polynomial was less than 1, which was calculated by code like: READ MORE