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Show HN: A physically-based GPU ray tracer written in Julia

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Re: Show HN: A physically-based GPU ray tracer written in Julia

#21

Earlier quoted context omitted.

What prediction? Maybe I need to rephrase what I said: My prediction is, that if Julia ever wants to have a shot at replacing Python, it absolutely has to solve the first time to first x problem! That's what I mean by shipping fully ahead of time compiled binaries and interpreting more glue code - which both have the potential to solve the first time to x problem.

The prediction I was referring to was the one in the parent comment. (The one I was commenting under)

Ah sorry :D

Re: Show HN: A physically-based GPU ray tracer written in Julia

#22

It's says: >the reference implementation from Physically Based Rendering (Pharr, Jakob, Humphreys) I'd like to know a little about the process you went through for the port. That book * sounds like an excellent resource to start from but what was it like using it and the code? * https://pbrt.org/

I've done lots of manually refactoring of the initial Prototype in Trace.jl (by Anton Smirnov, who I think ported an earlier version of the pbrt book). This helped familiarizing myself with the math and infrastructure and the general problems a raytracer faces and lay the ground work for the general architecture and what to pay attention to for fast GPU execution. One key insight was, that its possible to not need to have an UberMaterial, but instead use a MultiTypeSet for storing different materials and lights, which allows fast and concretely typed iterations.

Then I found that pbrt moved away from the initial design and I used claude code to port large parts of the new C++ code to Julia. This lead to a pretty bad port and I had lots of back and forth to fix bugs, improve the GPU acceleration, make the code more concise and "Julian" and correct the AIs mistakes and bogus design decisions ;) This polish isn't really over yet, but it works well enough and is fast enough for a beta release!

Re: Show HN: A physically-based GPU ray tracer written in Julia

#24

I don't hear nearly as much about Julia as I used to. A few years ago the view was that it was about to replace Python as the language of choice for data science. Seems like that didn't happen?

I don't know about everyone else, but slow Julia compilation continues to cause me ongoing suffering to this day. I don't think they're ever going to "fix" this. On a standard GitHub Actions Windows worker, installing the public Julia packages I use, precompiling, and compiling the sysimage takes over an hour. That's not an exaggeration. I had to juice the worker up to a custom 4x sized worker to get the wall clock time to something reasonable.

It took me days to get that build to work; doing this compilation once in CI so you don't have to do it on every machine is trickier than it sounds in Julia. The "obvious" way (install packages in Docker, run container on target machine) does not work because Julia wants to see exactly the same machine that it was precompiled on. It ends up precompiling again every time you run the container on other machines. I nearly shed a tear the first time I got Julia not to precompile everything again on a new machine.

R and Python are done in five minutes on the standard worker and it was easy; it's just the amount of time it takes to download and extract the prebuilt binaries. Do that inside a Docker container and it's portable as expected. I maintain Linux and Windows environments for the three languages and Julia causes me the most headaches, by far. I absolutely do not care about the tiny improvement in performance from compiling for my particular microarch; I would opt into prebuilt x86_64 generic binaries if Julia had them. I'm very happy to take R's and Python's prebuilt binaries.

Re: Show HN: A physically-based GPU ray tracer written in Julia

#25

[flagged]

Well I'm a bit of an AMD "fanboy" and really dislike NVIDIA's vendor lock in. I'm not sure what you mean by dynamic dispatch across GPU backends - nothing should be dynamic there and most easier primitives map quite nicely between vendors (e.g. local memory, work groups etc). To be honest, the BVH/TLAS has been pretty simple in comparison to the wavefront infrastructure. We haven't done anything fancy yet, but the performance is still really good. I'm sure there are still lots of things we can do to improve performance, but right now I've concentrated on getting something usable out. Right now, we're mostly matching pbrt-v4 performance, but I couldn't compare to their NVIDIA only GPU acceleration without an NVIDIA gpu. I can just say that the performance is MUCH better than what I initially aimed for and it feels equally usable as some of the state of the art renderers I've been using. A 1:1 comparison is still missing though, since it's not easy to do a good comparison without comparing apples to oranges (already mapping materials and light types from one render to another is not trivial).

Re: Show HN: A physically-based GPU ray tracer written in Julia

#26

I don't hear nearly as much about Julia as I used to. A few years ago the view was that it was about to replace Python as the language of choice for data science. Seems like that didn't happen?

Versus Python, it seems to fork into the "thinkers" vs "doers" camp. Julia provides a level of abstraction that some people find comforting. I thought I could use it as a sort of open source Matlab for a lot of thinky, 1-based index code I had lying around. It didn't meet my needs. And "spend half an hour waiting for a Jupyter notebook to boot up" is real. Great for some but it's not compatible with the way I work.

Elsewhere someone used the term "janky" and perhaps it's the fact that there are so many incredibly smart people around it that makes it so janky. By way of example, somebody needed to check disk space and the architect told him to shell out to Python.

Remember when LLVM first came out and it got kudos for the quality of its error messages? Well if you miss the old-school 1980s GCC experience the nonsense that eventually comes out of the Julia compiler after an hour will relight that flame.

Want to use greek letters and other symbols that don't appear on your keyboard as variable names? You've found your people.

Re: Show HN: A physically-based GPU ray tracer written in Julia

#27

I don't hear nearly as much about Julia as I used to. A few years ago the view was that it was about to replace Python as the language of choice for data science. Seems like that didn't happen?

IMO it just had too many rough edges. Very slow compilation, correctness issues ( https://yuri.is/not-julia/ ), kinda janky tooling (not nearly as bad as pip tbf). Even basic language mistakes like implicit variable declaration and 1-based indexing (in 2012??). Yes 1-based indexing is a mistake. It leads to significantly less elegant code - especially for generic code - and is no harder to understand than 1-based ind…

> Yes 1-based indexing is a mistake. It leads to significantly less elegant code - especially for generic code - and is no harder to understand than 1-based indexing for people capable of programming.

Some would argue that 0-based indexing is significantly less elegant for numerical/scientific code, but that depends on whether they come from a MATLAB/Fortran or Python/C(++) background.

A decision was made to target the MATLAB/Fortran (and unhappy? Python/C++) crowd first, thus the choice of 1-based indexing and column-major order, but at the end of the day it's a matter of personal preference.

0-based indexing would have made it easier to reach a larger audience, however.

> and is no harder to understand than 1-based indexing for people capable of programming.

The same could be said the other way around ;-)

Re: Show HN: A physically-based GPU ray tracer written in Julia

#28
post #7

Earlier quoted context omitted.

Ok. Did you see this: https://blog.yiningkarlli.com/2019/05/nested-dielectrics.htm... And I'm curious how you solve it.

Sorry, I was on my phone. This doesn't seem to be a problem of the description language, but rather how the integrator and materials work internally, so this works the same way in Julia currently. I do think though, that its more approachable to add experimental features like this in the Julia version. Would certainly be an interesting project! I do want to over time get further away from the pbrt-v4 architecture and…

I think it was a problem with the language as well as how they handle it internally. It was basically the algorithm that dictates how the language works, and consequently there was no way to have one material touch more than one other material. But I might misremember.

Anyway, I'm looking at this from the user's perspective. I wanted to do some physics-based ray-tracing with lenses and pbrt is what I ended up trying. As such, I really needed the multi-material aspect to work correctly. Also, it would be nice to be able to describe surfaces using a z=f(x,y) kind of formulation, or a way to place a hook in the renderer.

Re: Show HN: A physically-based GPU ray tracer written in Julia

#30

[flagged]

Well I'm a bit of an AMD "fanboy" and really dislike NVIDIA's vendor lock in. I'm not sure what you mean by dynamic dispatch across GPU backends - nothing should be dynamic there and most easier primitives map quite nicely between vendors (e.g. local memory, work groups etc). To be honest, the BVH/TLAS has been pretty simple in comparison to the wavefront infrastructure. We haven't done anything fancy yet, but the pe…

[flagged]
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