GPU-Accelerated ODE Solving in R with Julia, the Language of Libraries
August 24 2020 in Differential Equations, Julia, Programming, R, Uncategorized | Tags: diffeqr, differentialequations, gpu, high-performance, jit, r | Author: Christopher Rackauckas
R is a widely used language for data science, but due to performance most of its underlying library are written in C, C++, or Fortran. Julia is a relative newcomer to the field which has busted out since its 1.0 to become one of the top 20 most used languages due to its high performance libraries for scientific computing and machine learning. Julia’s value proposition has been its high performance in high level language, known as solving the two language problem, which has allowed allowed the language to build a robust, mature, and expansive package ecosystem. While this has been a major strength for package developers, the fact remains that there are still large and robust communities in other high level languages like R and Python. Instead of spawning distracting language wars, we should ask the … READ MORE
Neural Jump SDEs (Jump Diffusions) and Neural PDEs
June 5 2019 in Differential Equations, Julia, Mathematics, Stochastics | Tags: CUDA, differentiable programming, DifferentialEquations.jl, gpu, julia, stochastic differential equations | Author: Christopher Rackauckas
This is just an exploration of some new neural models I decided to jot down for safe keeping. DiffEqFlux.jl gives you the differentiable programming tools to allow you to use any DifferentialEquations.jl problem type (DEProblem) mixed with neural networks. We demonstrated this before, not just with neural ordinary differential equations, but also with things like neural stochastic differential equations and neural delay differential equations.
At the time we made DiffEqFlux, we were the “first to the gate” for many of these differential equations types and left it as an open question for people to find a use for these tools. And judging by the Arxiv papers that went out days after NeurIPS submissions were due, it looks like people now have justified some machine learning use cases for them. There were two separate papers on neural … 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