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We could argue the same about highly parallel computations, yet discrete GPU cards exist. I am also old enough to remember the same thing attempted for physics

by aredox 2y ago

We could argue the same about highly parallel computations, yet discrete GPU cards exist. I am also old enough to remember the same thing attempted for physics in games, PhysX, which had its own cards (PPU) built by ASUS and a couple others. There is also hardware acceleration for many audio and video codecs. "GC as a codec" doesn't strike me as something crazy: both are upgraded from time to time, but both are stable enough that hardware implementations are relevant over several years. Android phones would certainly benefit from it!

cmrdporcupine 2y ago
I think "do this matrix math and draw these polygons Really Really Fast" is specialized enough that GPUs are on the whole compelling as specialized hardware. That and this is a massive huge market which can benefit from economies of scale and Moore's law itself. Accelerating a particular language runtime is a different story. TBH people don't seem to have a problem generally with "throwing cycles away" and adopting languages like Python which have a pretty slow execution story. There's clearly not much of an economic advantage in optimization for processor throughput, otherwise more stuff would be getting [re]written in systems languages like C/C++/Rust/Zig etc, which would be a lot cheaper than developing custom hardware. And frankly most of the stuff that gets funding and people seem to get excited about in our industry right now... doesn't need much CPU. It's mostly just glue languages waiting on I/O. Apart from the ML/LLM hype right now, which relies heavily on... GPUs. I have a book here somewhere on the Linn Rekursiv hardware. Custom OO system in hardware in the 80s. Up there with Lisp machines as exotic and interesting "paths not taken".
- aredox 2y ago
  Thanks, Rekursiv is very interesting, and the Wikipedia article gave me the keywords to dig further on the topic of "high-level language computer architecture"

by aredox 2y ago

cmrdporcupine 2y ago

I think "do this matrix math and draw these polygons Really Really Fast" is specialized enough that GPUs are on the whole compelling as specialized hardware. That and this is a massive huge market which can benefit from economies of scale and Moore's law itself. Accelerating a particular language runtime is a different story. TBH people don't seem to have a problem generally with "throwing cycles away" and adopting languages like Python which have a pretty slow execution story. There's clearly not much of an economic advantage in optimization for processor throughput, otherwise more stuff would be getting [re]written in systems languages like C/C++/Rust/Zig etc, which would be a lot cheaper than developing custom hardware. And frankly most of the stuff that gets funding and people seem to get excited about in our industry right now... doesn't need much CPU. It's mostly just glue languages waiting on I/O. Apart from the ML/LLM hype right now, which relies heavily on... GPUs. I have a book here somewhere on the Linn Rekursiv hardware. Custom OO system in hardware in the 80s. Up there with Lisp machines as exotic and interesting "paths not taken".

aredox 2y ago
Thanks, Rekursiv is very interesting, and the Wikipedia article gave me the keywords to dig further on the topic of "high-level language computer architecture"