A bunch of stuff has happened since I published my post on The State of GPGPU in Rust. Most importantly, Denys Zariaiev (@denzp) released his work on a custom linker for Rust CUDA kernels, and a build.rs helper crate to make it easier to use. These two crates eliminate many of the problems I referred to in my previous post. The linker solves most of the “invalid PTX file” problems, while the ptx-builder crate does all of the magic that Accel was doing behind the scenes.
At work a few months ago, we started experimenting with GPU-acceleration. My boss asked if I was interested. I didn’t know anything about programming GPUs, so of course I said “Heck yes, I’m interested!“. I needed to learn about GPUs in a hurry, and that led to my GPU Path Tracer series. That was a lot of fun, but it showed me that CUDA support in Rust is pretty poor.
Hello! Welcome to my third and final post on my GPU-accelerated Path Tracer in Rust. In the last post, we implemented all of the logic necessary to build a true path tracer. Problem is, even on the GPU it’s terrifically slow. This post is (mostly) about fixing that. But first, we need to fix a bug or two, because I goofed. *sad trombone* Step -1: Fixing Bugs /u/anderslanglands on Reddit pointed out that, since I’m using Cosine-weighted Importance Sampling, I need to do some extra math to avoid biasing the results.
Hello, and welcome to part two of my series on writing a GPU-accelerated path tracer in Rust. I’d meant to have this post up sooner, but nothing ruins my productivity quite like Games Done Quick. I’m back now, though, so it’s time to turn the GPU ray-tracer from the last post into a real path tracer. Tracing Paths As mentioned last time, Path Tracing is an extension to Ray Tracing which attempts to simulate global illumination.
Well, it’s that time again. This is the start of a second series of articles on raytracing in Rust following on from my previous series. This time, I’ll be doing all of the rendering on a GPU using Accel - see my previous post on Accel. I thought this would be a good project for learning about GPU programming, see. Second, this time I want to write a path tracer, rather than a raytracer.
NOTE: Much of what I discuss below is no longer accurate. For the past month or so, I’ve been working on a follow-up to my series on Writing a Raytracer in Rust. This time around, I’ll be talking about writing a GPU-accelerated Path Tracer. As always, I’m writing it in Rust - including the GPU kernel code. Compiling Rust for GPUs at this point is difficult and error-prone, so I thought it would be good to start with some documentation on that aspect of the problem before diving into path tracing.
Way back in the dim mists of history (back in university) I wrote myself a custom RSS reader in Java and called it JARVIS1. You see, I read a lot of webcomics. Like, a lot. Some webcomics provide RSS feeds, but some don’t, and as my collection grew it started to become a hassle to use Firefox’s live bookmarks to manage it all. Ultimately, I wrote up a quick Swing GUI to use as a single interface for keeping up with blogs and tracking which comics had published updates since the last time I’d checked2.
I’m pleased to announce the release of Criterion.rs v0.2, available today. Version 0.2 provides a number of new features including HTML reports and throughput measurements, fixes a handful of bugs, and adds a new, more powerful way to configure and construct your benchmarks. It also breaks backwards compatibility with the 0.1 versions in a number of small but important ways. Read on to learn more! What is Criterion.rs? Criterion.rs is a statistics-driven benchmarking library for Rust.
When I initially announced the release of Criterion.rs, I didn’t expect that there would be so much demand for benchmarking on stable Rust. Now, I’d like to announce the release of Criterion.rs 0.1.2, which supports the stable compiler. This post is an introduction to benchmarking with Criterion.rs and a discussion of reasons why you might or might not want to do so. What is Criterion.rs? Criterion.rs is a benchmarking library for Rust that aims to bring solid statistical confidence to benchmarking Rust code, while maintaining good ease-of-use, even for programmers without a background in statistics.
After I released the first version of Criterion.rs, (a statistics-driven benchmarking tool for Rust) I was asked about using it to detect performance regressions as part of a cloud-based continuous integration (CI) pipeline such as Travis-CI or Appveyor. That got me wondering - does it even make sense to do that? Cloud-CI pipelines have a lot of potential to introduce noise into the benchmarking process - unpredictable load on the physical hosts of the build VM’s, or even unpredictable migrations of VMs between physical hosts.