glowcoil
You are talking about latent space during inference, not weight space during training, and you are talking about interleaving tokens with random Gaussian tokens, not constraining values to lie on a manifold within a larger space. Whether or not the thing you are describing is meaningful or useful, it is basically unrelated to the original article, and you are not using the term "modular manifold" to refer to the same thing.
> At this point you might be wondering, isn’t this a problem in many languages? Doesn’t Java also allow data races? And yes, Java does allow data races, but the Java developers spent a lot of effort to ensure that even programs with data races remain entirely well-defined. They even developed the first industrially deployed concurrency memory model for this purpose, many years before the C++11 memory model. The result of all of this work is that in a concurrent Java program, you might see unexpected outdated values for certain variables, such as a null pointer where you expected the reference to be properly initialized, but you will never be able to actually break the language and dereference an invalid dangling pointer and segfault at address 0x2a. In that sense, all Java programs are thread-safe.
And:
> Java programmers will sometimes use the terms “thread safe” and “memory safe” differently than C++ or Rust programmers would. From a Rust perspective, Java programs are memory- and thread-safe by construction. Java programmers take that so much for granted that they use the same term to refer to stronger properties, such as not having “unintended” data races or not having null pointer exceptions. However, such bugs cannot cause segfaults from invalid pointer uses, so these kinds of issues are qualitatively very different from the memory safety violation in my Go example. For the purpose of this blog post, I am using the low-level Rust and C++ meaning of these terms.
Java is in fact thread-safe in the sense of the term used in the article, unlike Go, so it is not a counterexample to the article's point at all.
The issue you are describing now is called perspective distortion (https://en.wikipedia.org/wiki/Perspective_distortion), and it is something that also happens with physical cameras when using a wide-angle lens. There is no single correct answer for dealing with this: similarly to the situation with map projections, every projection is a compromise between different types of distortion.
Anyway, if you're writing a ray tracer it's possible to use whatever projection you want, but if you're using the rasterizer in the GPU you're stuck with rectilinear projection and any alternate projection has to be approximated some other way (such as via post-processing, like you mention).
LLQ: A wait-free SPSC linked-list queue with recyclable nodes written in Rust
OK, let's consider block-local variables to have indeterminate storage location unless their address is taken. It doesn't substantively change the situation. Sometimes the compiler will store that variable in a register, sometimes it won't store it anywhere at all (if it gets constant-folded away), and sometimes it will store it on the stack. In the last case, it will generate and optimize code under the assumption that no aliasing loads or stores are being performed at that location on the stack, so we're back where we started.
This is exactly what I described above. Odin does not define the behavior of a program which indirectly pokes at stack memory, and it is thus able to perform optimizations which exploit the fact that that behavior is left undefined.
> The compiler does not get to look at that and say "well this looks like undefined behavior, let me get rid of this line!".
This is a misleading caricature of the relationship between optimizations and undefined behavior. C compilers do not hunt for possible occurrences of undefined behavior so they can gleefully get rid of lines of code. They perform optimizing transformations which are guaranteed to preserve the behavior of valid programs. Some programs are considered invalid (those which execute invalid operations like out-of-bounds array accesses at runtime), and those same optimizing transformations are simply not required to preserve the behavior of such programs. Odin does not work fundamentally differently in this regard.
If you want to get rid of a particular source of undefined behavior entirely, you either have to catch and reject all programs which contain that behavior at compile time, or you have to actually define the behavior (possibly at some runtime cost) so that compiler optimizations can preserve it. The way Odin defines the results of integer overflow and bit shifts larger than the width of the operand is a good example of the latter.
C does have a particularly broad and programmer-hostile set of UB-producing operations, and I applaud Odin both for entirely removing particular sources of UB (integer overflow, bit shifts) and for making it easier to avoid it in general (bounds-checked slices, an optional type). These are absolutely good things. However, I consider it misleading and false to claim that Odin has no UB whatsoever; you can insist on calling it something else, but that doesn't change the practical implications.
In a language with raw pointers, essentially all optimizations rely on this type of assumption. Forbidding the compiler from making the assumption that undefined behavior will not occur essentially amounts to forbidding the compiler from optimizing at all. If that is indeed what you want, then what you want is something closer to a macro assembler than a high-level language with an optimizing compiler like C. It's a valid thing to want, but you can't have your cake and eat it too.
As a basic example, if the compiler guarantees that the write will result in a deterministic segmentation fault, then that address must never be reused by future allocations (including stack allocations!), and the compiler is not allowed to perform basic optimizations like dead store elimination and register promotion for accesses to that address, because those can prevent the segfault from occurring.
If the compiler guarantees that the write will result in either a segfault or a valid write to that memory location, depending on the current state of the allocator, what guarantees does the compiler make about those writes? If some other piece of code is also performing reads and writes at that location, is the write guaranteed to be visible to that code? This essentially rules out dead store elimination, register promotion, constant folding, etc. for both pieces of code, because those optimizations can prevent one piece of code from observing the other's writes. Worse, what if the two pieces of code are on different threads? And so on.
If the compiler doesn't guarantee a deterministic crash, and it doesn't guarantee whether or not the write is visible to other code using the same region of memory, and it doesn't provide any ordering or atomicity guarantees for the write if it does end up being visible to other code, and then it performs a bunch of optimizations that can affect all of those things in surprising ways: congratulations, your language has undefined behavior. You can insist on calling it something else, but you haven't changed the fundamental situation.
First, Rust's borrow checker incurs zero runtime overhead for any pointer operations (whether dereferencing, copying, or dropping a pointer) and requires no extra storage at runtime (no reference counts or generation numbers); it's entirely a set of compile-time checks. Generational references, on the other hand, require storing an extra piece of data alongside both every heap allocation and every non-owning reference, and they incur an extra operation at every dereference.
Second, since Rust's borrow checker exists entirely at compile time, it doesn't introduce any runtime failures. If a program violates the rules of the borrow checker, it won't compile; if a program compiles successfully, the borrow checker does not insert any conditional runtime panics or aborts. Generational references, in comparison, consist entirely of a set of runtime checks; you won't found out if you violated the rules of generational references until it happens at runtime during a particular execution and your program crashes.
Finally, Rust's borrow checker applies to references of all kinds, whether they point to a heap-allocated object, a stack-allocated object, an inline field inside a larger allocation, a single entry in an array, or even an object allocated on a different heap and passed over FFI. Its checks still apply even in scenarios where there is no heap. Generational references, on the other hand, are entirely specific to heap-allocated objects. They don't work for stack-allocated objects, they don't work for foreign objects allocated on a different heap, and they don't work in a scenario with no heap at all.
All of these are fundamental differences which mean that Vale's generational reference system is not at all a replacement for Rust's borrow checker. It's not zero-overhead, it doesn't catch errors at compile time, and it's fundamentally specific to heap-allocated objects. In these ways it's more comparable to Rust's Rc, which introduces runtime overhead and is specific to heap-allocated objects, or RefCell, which performs checks at runtime that can result in aborting the program.
They're relatively straightforward due to the fact that CLAP is a simple, pure-C ABI, and there are already some fully functional plugins making use of them (e.g. https://github.com/robbert-vdh/nih-plug).
The very specific API design that you've described is not possible in Rust, but it is strange to equate this with the entirety of RAII. In any case, there are many alternative APIs (some with no sacrifice in speed or safety!) that are perfectly possible in Rust.
Rust safety is about being able to take an unsafe component, encapsulate its implementation details, and encode sound usage patterns for that component in a public API which can then be statically checked by the compiler. This allows the difficult problem of determining whether an entire codebase is sound, memory-safe, and free of undefined behavior to be factored into many smaller, more tractable problems of verifying that individual components are sound given their APIs. You can even do this with wrappers and bindings to C libraries, and there are many examples of this in the Rust ecosystem.
This book seems to be discussing distributed object systems, which is a sense of the word "component" that has little or nothing to do with the sense used by game developers in reference to the ECS architecture. Distributed object systems are designed to enable an object-oriented design philosophy to be used for a system which spans multiple address spaces or machines. The entity-component-system architecture is a methodology for organizing data layout and program functionality, usually within a single address space on a single machine, where the data associated with a given entity is spread across multiple components or subsystems and associated by index relations (much like a relational database), rather than being all grouped together in one place (as encouraged in OOP).
These two concepts (distributed object systems and ECS) are designed to solve different problems, they are generally used in different scenarios, and they apply at different levels of system organization. There is so little resemblance between the two that I have to conclude someone calling them "sides of the same coin" is either completely unfamiliar with one of them or is being deliberately misleading.
Basedrop: A garbage collector for real-time audio in Rust
> Update (7 May): I did a test to see which threads in the fragment shader get scheduled to the same SIMD group, and there’s not enough coherence to make this workable. In the image below, all pixels are replaced by their mean in the SIMD group (active thread mask + simd_sum)
I cloned the piet-gpu repository and was able to render a very nice image of the Ghostscript tiger: https://imgur.com/a/swyW0gl
> I did, and mentioned in the docs, here’s a quote: “I didn’t want to experiment with GPU-based splines. AFAIK the research is not there just yet.”
Not what I said. I said that you didn't investigate the project discussed in the original blog post before declaring, in your words, that "the quality is not good" and comparing it to your own library.
Vrmacs and piet-gpu are two totally different types of renderer. Vrmacs draws paths by decomposing them into triangles, rendering them with the GPU rasterizer, and antialiasing edges using screen-space derivatives in the fragment shader. This approach works great for large paths, or paths without too much detail per pixel, but it isn't really able to render small text, or paths with a lot of detail per pixel, with the necessary quality. (Given this and the other factors you mentioned in your reply, rendering text on the CPU with Freetype is a perfectly reasonable engineering choice and I am not criticizing it in the slightest.)
In comparison, piet-gpu decomposes paths into line segments, clips them to pixel boundaries, and analytically computes pixel coverage values using the shoelace formula/Green's theorem, all in compute shaders. This is more similar to what Freetype itself does, and it is perfectly capable of rendering high-quality small text on the GPU, in way that Vrmacs isn't without shelling out to Freetype.
Again, to be clear, I'm not criticizing any of the design choices that went into Vrmacs; it looks like it occupies a sweet spot similar to NanoVG or Dear ImGui, where it can take good advantage of the GPU for performance while still being simple and portable. My only point here is that you performed insufficient investigation of piet-gpu before confidently making an uninformed claim about it and putting it in a somewhat nonsensical comparison with your own project.
I notice that your renderer doesn't even attempt to render text on the GPU and instead just blits glyphs from an atlas texture rendered with Freetype on the CPU: https://github.com/Const-me/Vrmac/blob/master/Vrmac/Draw/Sha...
In contrast, piet-gpu (the subject of the original blog post) has high enough path rendering quality (and performance) to render glyphs purely on the GPU. This makes it clear you didn't even perform a cursory investigation of the project before making a comment to dump on it and promote your own library.
It's axis-aligned and box-filtered so there are no shades of gray, which means gamma is irrelevant. The white-on-black line appears to have a greater thickness than the black-on-white line.
There's a lot of empirical research showing that reading performance is better with positive-polarity (black-on-white) text than with negative-polarity text [1, 2], probably because the higher overall luminance results in a smaller pupil size and thus a sharper retinal image [3]. So, white-on-black lines appear thicker than black-on-white lines because the eye doesn't focus as sharply on them. This is true regardless of which color space blending is performed in.
Given this fact, if one wants to achieve uniform perceptual line thickness for black-on-white and white-on-black text, a more principled approach than messing with color blending would be to vary line width or the footprint of the antialiasing filter based on the luminance of the text (and possibly the background). This is the approach Apple and Adobe have taken for years with stem darkening/font dilation.
[1]: http://jdobr.es/pdf/Dobres-etal-2016-Displays.pdf
[2]: https://www.uni-duesseldorf.de/home/fileadmin/redaktion/Oeff...
[3]: http://www.psychologie.hhu.de/fileadmin/redaktion/Oeffentlic...