ck VS ArrayFire

Maybe I'm missing something, but x is not volatile and the compiler is free to assume that it is not modified concurrently outside the bounds of C's memory model. Compilers can and do hoist out loop invariants, and https://github.com/concurrencykit/ck/commit/b54ae5c4ace9b94442bbb46858449069f566d269 seems like an example of compilers doing what you say they don't. What am I missing?

Recommend checking out http://concurrencykit.org instead.

If you're interested in how other approaches work, or how one achieves concurrency on shared mutable state without mutual exclusion, would recommend checking out concurrency kit.

There are plenty of practical solutions to the safe memory reclamation problem in C. The language just doesn't force one on you.

From epoch-based reclamation (https://github.com/concurrencykit/ck/blob/master/include/ck_..., especially with the multiplexing extension to Fraser's classic scheme), to quiescence schemes (https://liburcu.org/), or hazard pointers (https://github.com/facebook/folly/blob/master/folly/synchron..., or https://pvk.ca/Blog/2020/07/07/flatter-wait-free-hazard-poin...)... or even simple using a type-stable (https://www.usenix.org/legacy/publications/library/proceedin...) memory allocator.

In my experience, it's easier to write code that is resilient to hiccups in C than in Java. Solving SMR with GC only offers something close to lock-freedom when you can guarantee global GC pauses are short enough... and common techniques to bound pauses, like explicitly managed freelists land you back in the same problem space as C.

ck - Concurrency primitives, safe memory reclamation mechanisms and non-blocking data structures. BSD-2-Clause

Indeed they do, https://github.com/concurrencykit/ck

Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.

Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.

So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).

For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.

What about Arrayfire. https://github.com/arrayfire/arrayfire

Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters

Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.

Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709