XPost: sci.physics, comp.lang.prolog   
   From: janburse@fastmail.fm   
      
   Hi,   
      
   The bottom line is often, PRAMs might be   
   closer to physics. Especially for certain   
   machine learning algorithms or questions   
      
   from modelling perception or action. You   
   might get better results if you model the   
   problem in terms of Boltzman machines,   
      
   or whatever from the arsenal of physics.   
      
   Bye   
      
   Mild Shock schrieb:   
   > Hi,   
   >   
   > PRAM effects are a little bit contrived in AI   
   > accelerators, since they work with matrix tiles,   
   > that are locally cached to the tensor core.   
   >   
   > But CRCW is quite cool for machine learning.   
   > When the weights get updated. ChatGPT suggested   
   > me to read this paper:   
   >   
   > Hogwild!: A Lock-Free Approach to   
   > Parallelizing Stochastic Gradient Descent   
   > https://arxiv.org/pdf/1106.5730   
   >   
   > Didn't read yet...   
   >   
   > You might also have read the recent report how   
   > Google trained Gemini. They had to deal with other   
   > issues as well, like failure of a whole   
   >   
   > tensore core.   
   >   
   > Bye   
   >   
   > Mild Shock schrieb:   
   >> Hi,   
   >>   
   >> Simulation is not so easy. You would need an   
   >> element of non-determinism, or if you want   
   >> call it randomness. Because PRAM has this   
   >>   
   >> instructions, ERCW, CRCW, etc..   
   >>   
   >> - Concurrent read concurrent write (CRCW)—   
   >> multiple processors can read and write. A   
   >> CRCW PRAM is sometimes called a concurrent   
   >> random-access machine.   
   >> https://en.wikipedia.org/wiki/Parallel_RAM   
   >>   
   >> Modelling via von Neuman what happens there   
   >> can be quite challenging. At least it doesn't   
   >> allow for a direct modelling.   
   >>   
   >> What a later processor sees, depends extremly   
   >> on the timing and which processor "wins" the   
   >> write.   
   >>   
   >> Also I don't know what it would buy you   
   >> intellectually to simulate a PRAM on a random   
   >> von Neuman machine. The random von Neuman   
   >>   
   >> machine could need more steps than the PRAM   
   >> in summary, because it has to simulate a PRAM.   
   >> But I guess its the intellectual questioning   
   >>   
   >> that needs also a revision when confronted   
   >> with the new architecture of unified memory   
   >> and tensor processing cores.   
   >>   
   >> Bye   
   >>   
   >> Maciej Woźniak schrieb:   
   >>> On 12/1/2025 12:15 PM, Mild Shock wrote:   
   >>>> Hi,   
   >>>>   
   >>>> You wrote:   
   >>>>   
   >>>>  > No, they don't, they just add one (or some)   
   >>>>  > more layer on top of it.   
   >>>>   
   >>>> Techically they are not von Neuman architecture.   
   >>>> Unified Memory with Multiple Tensor Cores is   
   >>>> not von Neuman architecture.   
   >>>   
   >>> We can use von Neumann architecture   
   >>> to emulate other architectures, but as long as it   
   >>> is performed by our computers it is technically   
   >>> von Neumann's.   
   >>>   
   >>   
   >   
      
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