I have never used Qualcomm's OpenCL driver, but it is not unknown to get the NVIDIA driver into a state where some kernel is stuck in a running state, or some memory is allocated long after the originating process has terminated. This is usually down to application bugs, sure - but no application bug should be able to wedge the driver. While developing GPU kernels, the code will certainly be buggy, and hence the driver should be robust. For that matter, maybe I am running untrusted GPU code, and anytime the driver gets in a weird or stuck state, I am uneasy that it might not be many steps away from an exploitable situation. We don't accept this in CPU operating systems, so why should it be acceptable for GPUs? We are talking unprivileged code - nothing runs as root. Ever since I first got into GPGPU programming (about 2012), I noticed that they were far less robust in the face of buggy code than I was accustomed to.
It is also common in my experience for buggy GPU code to crash displays if the GPU is simultaneously used to drive a monitor. This usually happens for kernels that go into infinite loops, or out-of-memory conditions.
It is my understanding that modern GPU drivers even have watchdog systems that notice when they get stuck and forcibly reboot them, which to me is mere symptom treatment.
Yes, this also stood out to me. I usually think of CPUs and memory having parity in the early 80s, but I never bothered to check for sure. I do remember some early computer architects writing about memory being faster than the CPU!
I'm not a Jax expert. Accelerate's 'map' allows for almost arbitrary sequential code - there is some fine print, because it's an embedded language, and the biggest fine print is that nested parallelism is not allowed. You can define your own Haskell-level higher order functions, and Accelerate will handle them just fine, because essentially all the Haskell-level computation is "compiled away" (by being run) before the Accelerate code is JIT-compiled at run-time. You can consider Haskell to be a meta-language in which you ultimately construct Accelerate program terms, and then those are compiled and run - not too dissimilar from how Jax does it, actually.
Recursion works, but for an uninteresting reason: the recursion is on the Haskell side, and will essentially be unrolled before Accelerate gets its hand on it. This allows you to do some fun things (like partially evaluating a ray tracer on its scene description), but it's often not what you want, and Accelerate provides some combinators (that look like higher-order Haskell functions) for expressing sequential looping.
One big difference compared to NumPy (which you may or may not care about depending on how picky you are), is that Accelerate is a higher-order programming model. Basically, you can have 'map' (with a user-provided function), and it will go fast, in contrast to NumPy's model where only first-order operations go fast.
Making Haskell programs go faster. I will say that Accelerate is in most cases not faster than similar libraries for other languages (e.g. Jax), but the integration with normal Haskell is very pleasant. As Haskell is a very nice and practical language for general-purpose programming, it's convenient to be able to use Accelerate for those parts where numerical performance is critical (but not so critical that you rewrite the entire program in CUDA or C).
> Isn't the data they capture so valuable that they (Microsoft) are happy to eat the cost?
Even if that is true, unless the value of the data corresponds to near-term revenue, then eventually the cost may simply not be possible to meet. Or for that matter, the capital to manage the increasing load may simply not exist - it does not matter how much valuable data you have, if the supply of hardware cannot keep up with your demand.
Also, I suspect that most of the "data" obtained by the incessant hammering on GitHub is not very valuable. Most business code is routine, and getting Copilot to help out with generating enormous amounts of it may not contribute much in return.
This page reads like an exasperated response to constant discussions and requests for how to extract strontium nitrate from road flares, and emphasizes that it is hard and pointless in the first place. I never noticed such discussions, but maybe it's outside of my bubble! Quite an amusing read nonetheless.
As others have mentioned, such tools exist. However, I believe they do more harm than help. Good --help output does not make for good --man output. In particular, while man pages are terse, good ones are more than just lists of command line options, and the part of them that are a list of command line options will usually have more detail than --help. The writing of documentation is a place where I often see programmers employ automation inappropriately.
That depends on the language. I have used (and implemented) languages where arrays are modeled as a function from an index space to some expression. During compilation, this is used to drive various optimisations. For those arrays that need a run-time representation, they may be stored in the classic way (a dense region of memory accessed with offsets computed from indexes), but also in more complicated ways, such as some kind of tree structure. These are still, semantically, arrays at the language level.
The post explains that 'a[i]' can easily enough be written as 'a i'. Your suggestions do not resemble the current function application syntax in the language discussed in the post. The question is not whether a terse slice syntax can exist (clearly it can), but whether a syntactic similarity between indexing and application can also be extended to a syntactic similarity between slicing and application.
Depending on how you look at things, functions can also be mutated at run-time. Most impure languages allow you to define a function that has some internal state and changes it whenever it is applied. In C you would use 'static' variables, but languages with closures allow for a more robust approach. Scheme textbooks are full of examples that use this trick to define counters or other objects via closures. You can well argue that these functions do not "mutate", they merely access some data that is mutated, but there is no observable difference from the perspective of the caller.
In some sense, Go does not allow you to change the major version. Packages with the same name but different major versions are treated as different packages.
It is basically dependent types, but there is a specific and intentional omission (no true dependent products) that interacts with another feature (the ability to hide sizes) that ultimately causes the mess. I elaborated on it here: https://futhark-lang.org/blog/2025-09-26-the-biggest-semanti...
This blog post showcases V in a positive light. I suppose it is good that people can have productive experiences with it now, although I don't see from this post why it is a significant improvement on Go.
The problems discussed (performance, compiler fragility) are somewhat worrying though. My impression is still that V is not particularly robust and focuses on flashy things instead of getting the basics right. I must admit that it is however still hard to look at V objectively, given the near-fradulent presentation it had when it was first announced.
It is also common in my experience for buggy GPU code to crash displays if the GPU is simultaneously used to drive a monitor. This usually happens for kernels that go into infinite loops, or out-of-memory conditions.
It is my understanding that modern GPU drivers even have watchdog systems that notice when they get stuck and forcibly reboot them, which to me is mere symptom treatment.