Shadama

Squeak Smalltalk has several automatic runtime optimizations and compilers like JIT, parallel load balancing compiler [1], adaptive compiler [2] and a metacircular simulator and byte code virtual machine written in itself that allows you to do runtime optimisations on GPUs. The byte codes are of course replaced with the native GPU instructions at runtime. There are dozens of scientific papers and active research is still being done [1]. I've worked on automatic parallel runtime optimizations and adaptive compilers since 1981. We make reconfigurable hardware (chips and wafers) that also optimises at runtime. Truffle/GraalVM is very rigid and overly complicated [6]. With a meta compiler like Ometa or Ohm we can give any programming language the runtime adaptive compilation for GPUs [3][4]. I'm currently adapting my adaptive compiler to Appl

You could polish it by using variable size precision floats or at least quadruple size 128 bit floating point. This requires you to create a programming language compiler or use my parallel Squeak programming language (it is portable) and have that run on Webassembly or WebGL. It would be easier to have it run directly on CPU, GPU and Neural Engine hardware. The cheapest hardware today would be the M4 Mac mini or design your own chips (see my other post in this thread). An example of this polished solution is [1] but this example does not yet use high precision floating point [2]. [1] https://tinlizzie.org/~ohshima/shadama2/ [2] https://github.com/yoshikiohshima/Shadama

You can help with the reverse engineering of Apple Silicon done by a dozen people worldwide, that is how we find out the GPU and NPU instructions[1-4]. There is over 43 trillion float operations per second to unlock at 8 terabit per second 'unified' memory bandwidth and 270 gigabits per second networking (less on the smaller chips).... [1] https://github.com/AsahiLinux/gpu [2] https://github.com/dougallj/applegpu [3] https://github.com/antgroup-skyward/ANETools/tree/main/ANEDi... [4] https://github.com/hollance/neural-engine You can use a high level APIs like MLX, Metal or CoreML to compute other things on the GPU and NPU. Shadama [5] is an example programming language that translates (with Ometa) matrix calculations into WebGPU or WebGL APIs (I forget which). You can do exactly the same with the MLX, Metal or CoreML APIs and only pay ar

Super cool. The video is 6 years old and looks like the development mostly stopped 3 years ago? https://github.com/yoshikiohshima/Shadama While it makes sense for students, I feel like for anything coding related to succeed today it must target VS Code as a top target environment.