GPUs open the potential to forecast urban weather for drones and air taxis • Hacker News Board

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#31

Post by carabiner » Thu, Sep 30, 2021, 3:52 PM UTC

amelius wrote: post #25

mwambua wrote: post #9

For some context, WRF ( https://github.com/wrf-model/WRF ), the current state-of-the-art weather modeling system (also developed by NCAR) can (to my limited knowledge) only run on CPU. There have been efforts to run it on GPU ( https://wrfg.net )... though it doesn't look like it's been kept up to date. It seems like FastEddy mostly replaces WRF-LES, which is used for high-resolution localized modeling.

What's at the heart of it? Is it solving large sparse linear equations? If so, wouldn't it be relatively simple to swap-in a CUDA implementation of this solver?

https://en.m.wikipedia.org/wiki/Navier–Stokes_equations#Nonl...

> The Navier–Stokes equations are nonlinear partial differential equations in the general case and so remain in almost every real situation. In some cases, such as one-dimensional flow and Stokes flow (or creeping flow), the equations can be simplified to linear equations. The nonlinearity makes most problems difficult or impossible to solve and is the main contributor to the turbulence that the equations model.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#32

Post by amelius » Thu, Sep 30, 2021, 8:18 PM UTC

carabiner wrote: post #31

amelius wrote: post #25

Earlier quoted context omitted.

What's at the heart of it? Is it solving large sparse linear equations? If so, wouldn't it be relatively simple to swap-in a CUDA implementation of this solver?

https://en.m.wikipedia.org/wiki/Navier–Stokes_equations#Nonl... > The Navier–Stokes equations are nonlinear partial differential equations in the general case and so remain in almost every real situation. In some cases, such as one-dimensional flow and Stokes flow (or creeping flow), the equations can be simplified to linear equations. The nonlinearity makes most problems difficult or impossible to solve and is the m…

It is not strange to find a linear equation solver at the heart of a nonlinear equation solver, though.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#33

Post by dahart » Thu, Sep 30, 2021, 8:43 PM UTC

sampo wrote: post #22

wongarsu wrote: post #11

Earlier quoted context omitted.

It's useful to think of GPUs as CPUs with low core frequency but incredibly wide SIMD instructions. They are very good at doing the exact same instructions in parallel (apply this formula to 4 million pixels), they are fairly bad at anything that includes branches or loops (doesn't fit the SIMD model), or random memory access (only so much memory bandwidth to go around between all the cores). That makes GPUs generall…

> They are very good at doing the exact same instructions in parallel (apply this formula to 4 million pixels) In numerical fluid mechanics, you rather want to apply the same formula to the nearest 27 (3x3x3) or 125 (5x5x5) pixels in the 3-dimensional array. And then store the result to another 3-dimensional array. Or maybe for calculating a value in a 3d array A, you need to apply a formula that looks at the nearest…

This is true for pixels too; think about a median or blur operation, or a 2d fluid sim, you might gather and combine the nearest NxN pixels. 2d and 3d are nearly identical, the main difference is just indexing. Parent comment was talking about the single formula that produces the output value written to the cell, which as you say might well involve reading inputs from many nearby cells. Fluid mechanics can also be thought of as a single formula per output cell. (And normally it’s most natural to organize GPU threads so that they are 1:1 with output cells.)