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The Wuhan Virus that destroyed Python [ggml Manifesto]

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  The Wuhan Virus that destroyed Python [ggml Manifesto] Mild Shock <janburse@fastmail.fm> - 2026-07-22 21:00 +0200
    Deadlock Exorcism: Switch from Push to Pull [A pi-calculus Specification of Prolog] (Re: The Wuhan Virus that destroyed Python [ggml Manifesto]) Mild Shock <janburse@fastmail.fm> - 2026-07-23 00:23 +0200
      Why do you even need a mpmc queue? [Thunder Kittens] (Re: Deadlock Exorcism: Switch from Push to Pull) Mild Shock <janburse@fastmail.fm> - 2026-07-23 08:43 +0200
        Trivial balancing example for (int i=0; i<global_id; i++) (Re: Why do you even need a mpmc queue? [Thunder Kittens]) Mild Shock <janburse@fastmail.fm> - 2026-07-23 08:57 +0200
          Enqueue/dequeue need not be fast and can spinn ["fairness" questions] (Was: Trivial balancing example for (int i=0; i<global_id; i++)) Mild Shock <janburse@fastmail.fm> - 2026-07-23 09:11 +0200
            The Pixel Phone AI Experiment Song (Re: Enqueue/dequeue need not be fast and can spinn ["fairness" questions] ) Mild Shock <janburse@fastmail.fm> - 2026-07-23 09:21 +0200
        Re: Why do you even need a mpmc queue? [Thunder Kittens] (Re: Deadlock Exorcism: Switch from Push to Pull) Ross Finlayson <ross.a.finlayson@gmail.com> - 2026-07-23 08:24 -0700
    Potential Python Recovery: Free Threading [3.13 release] (Was: The Wuhan Virus that destroyed Python [ggml Manifesto]) Mild Shock <janburse@fastmail.fm> - 2026-07-23 10:19 +0200
      Re: Potential Python Recovery: Free Threading [3.13 release] (Was: The Wuhan Virus that destroyed Python [ggml Manifesto]) Ross Valikhanov <kavna@rl.ru> - 2026-07-23 16:01 +0000
    Re: The Wuhan Virus that destroyed Python [ggml Manifesto] Ramon Dubenkov <omd@nnk.ru> - 2026-07-23 13:38 +0000
    The things XILINX braught to the AMD table (Was: The Wuhan Virus that destroyed Python [ggml Manifesto]) Mild Shock <janburse@fastmail.fm> - 2026-07-23 18:47 +0200
      NIVIDIA evacuated its Chinese market [Tau Scaling] (Was: The things XILINX braught to the AMD table) Mild Shock <janburse@fastmail.fm> - 2026-07-23 19:11 +0200
      NVIDIA evacuated its Chinese market [Tau Scaling] (Re: The things XILINX braught to the AMD table) Mild Shock <janburse@fastmail.fm> - 2026-07-23 19:12 +0200
        Re: NVIDIA evacuated its Chinese market [Tau Scaling] (Re: The things XILINX braught to the AMD table) Lane W <cactus_DAC@yahoo.com> - 2026-07-23 11:22 -0600
          Micro penis mother sung arias (Was: NVIDIA evacuated its Chinese market [Tau Scaling]) Mild Shock <janburse@fastmail.fm> - 2026-07-24 14:38 +0200
            Re: Micro penis mother sung arias (Was: NVIDIA evacuated its Chinese market [Tau Scaling]) Lane W <cactus_DAC@yahoo.com> - 2026-07-24 07:15 -0600
              Micro penis brain is in constant hiatus (Was: Micro penis mother sung arias) Mild Shock <janburse@fastmail.fm> - 2026-07-24 15:24 +0200
                Re: Micro penis brain is in constant hiatus (Was: Micro penis mother sung arias) Mild Shock <janburse@fastmail.fm> - 2026-07-24 15:36 +0200
                Ignoramus or Ignorabimus: I don't care (π-WAM) (Re: Micro penis brain is in constant hiatus) Mild Shock <janburse@fastmail.fm> - 2026-07-24 15:38 +0200
                  Re: Ignoramus or Ignorabimus: I don't care (π-WAM) (Re: Micro penis brain is in constant hiatus) Lane W <cactus_DAC@yahoo.com> - 2026-07-24 08:31 -0600
                    You are a moron, brainless putin payed (Was: Ignoramus or Ignorabimus: I don't care (π-WAM)) Mild Shock <janburse@fastmail.fm> - 2026-07-24 18:01 +0200
                      Re: You are a moron, brainless putin payed (Was: Ignoramus or Ignorabimus: I don't care (π-WAM)) Lane W <cactus_DAC@yahoo.com> - 2026-07-24 10:27 -0600
                        Yeah keep reading my posts, uninspired fool (Was: You are a moron, brainless putin payed) Mild Shock <janburse@fastmail.fm> - 2026-07-24 19:45 +0200
                          Re: Yeah keep reading my posts, uninspired fool (Was: You are a moron, brainless putin payed) Lane W <cactus_DAC@yahoo.com> - 2026-07-24 12:11 -0600
                            LoL (Was: Yeah keep reading my posts, uninspired fool ) Mild Shock <janburse@fastmail.fm> - 2026-07-24 20:12 +0200
                              Re: LoL (Was: Yeah keep reading my posts, uninspired fool ) Lane W <cactus_DAC@yahoo.com> - 2026-07-24 12:53 -0600
                  Out of the blue accusation span 15 days [Empirical USENET study] (Was: Ignoramus or Ignorabimus: I don't care (π-WAM)) Mild Shock <janburse@fastmail.fm> - 2026-07-24 20:26 +0200
    Little Data Center on Your Palm [AI Laptops for 500 USD] (Was: The Wuhan Virus that destroyed Python [ggml Manifesto] Mild Shock <janburse@fastmail.fm> - 2026-07-24 17:58 +0200
      2008: 4 Blades + Tesla S1070 versus 2026: 1 AI Laptop (Re: Little Data Center on Your Palm [AI Laptops for 500 USD]) Mild Shock <janburse@fastmail.fm> - 2026-07-24 18:16 +0200
      Re: Little Data Center on Your Palm [AI Laptops for 500 USD] (Was: The Wuhan Virus that destroyed Python [ggml Manifesto] Bradford Babkoff <ffb@odbb.ru> - 2026-07-24 18:05 +0000
        LoL (Was: Little Data Center on Your Palm [AI Laptops for 500 USD]) Mild Shock <janburse@fastmail.fm> - 2026-07-24 20:11 +0200
    Hurry the blue bus doesnt stop indefinitely (Was: The Wuhan Virus that destroyed Python [ggml Manifesto]) Mild Shock <janburse@fastmail.fm> - 2026-07-24 20:36 +0200
      Not SIMD, a MIMD design for NVIDIA Volta (Re: Hurry the blue bus doesnt stop indefinitely) Mild Shock <janburse@fastmail.fm> - 2026-07-24 20:57 +0200
        Could take 3-4 months find machine / browser (Was Not SIMD, a MIMD design for NVIDIA Volta) Mild Shock <janburse@fastmail.fm> - 2026-07-24 21:15 +0200
        The Koan of pi-WAM queues [FORTRAN-S] (Was: Not SIMD, a MIMD design for NVIDIA Volta) Mild Shock <janburse@fastmail.fm> - 2026-07-26 19:52 +0200
          The turbo capping of AI Laptops (Re: The Koan of pi-WAM queues [FORTRAN-S]) Mild Shock <janburse@fastmail.fm> - 2026-07-26 20:01 +0200
          Re: The Koan of pi-WAM queues [FORTRAN-S] (Was: Not SIMD, a MIMD design for NVIDIA Volta) Ross Finlayson <ross.a.finlayson@gmail.com> - 2026-07-26 20:33 -0700
            Why forget Bulgarians, never on my mind (Re: The Koan of pi-WAM queues [FORTRAN-S] (Was: Not SIMD, a MIMD design for NVIDIA Volta) Mild Shock <janburse@fastmail.fm> - 2026-07-27 09:14 +0200
              miniTriton CUDA is an alternative to torch variants (Was: Why forget Bulgarians, never on my mind) Mild Shock <janburse@fastmail.fm> - 2026-07-27 09:40 +0200
                Andrej Karpathy original gangster of Budget Laptop (Was: miniTriton CUDA is an alternative to torch variants) Mild Shock <janburse@fastmail.fm> - 2026-07-27 09:51 +0200

Page 2 of 2 — ← Prev page 1 [2]

#671640 — You are a moron, brainless putin payed (Was: Ignoramus or Ignorabimus: I don't care (π-WAM))

Hi,

You are a moron, and you represent putin payed
trolls from the army of brainless troll morons.

Bye

Lane W schrieb:
> Mild Shock has no idea who I am or what I represent

[toc] | [prev] | [next] | [standalone]

#671642 — Re: You are a moron, brainless putin payed (Was: Ignoramus or Ignorabimus: I don't care (π-WAM))

Mild Shock wrote:
> Hi,
> 
> You are a moron, and you represent putin payed
> trolls from the army of brainless troll morons.
> 
> Bye
> 
> Lane W schrieb:
>> Mild Shock has no idea

You are one of those cerebral asshats in the first episode of Star Trek.

[toc] | [prev] | [next] | [standalone]

#671643 — Yeah keep reading my posts, uninspired fool (Was: You are a moron, brainless putin payed)

Hi,

Yeah keep reading my posts, uninspired fool.
Seems you got a glimps of imagination from my post:

 > From: Mild Shock <janburse@fastmail.fm>
 > Subject: NVIDIA evacuated its Chinese market [Tau Scaling]
 > Date: Thu, 23 Jul 2026 19:13:51 +0200

> How it started:
> 
> Captain: Throw the switch, Scotty!
> Enterprise: Cloaking Device makes it invisible
> Spock: Military secrets are the most fleeting of all.
> Kirk Escapes the Romulans - The Enterprise Incident
> https://www.youtube.com/watch?v=AusAGjwlql8

But copying others in trope, is not the same
as jolting a trope into a conservation.
It still makes you a lame copist. Maybe you

don't know with whom you are dealing with, right?
I don't know who you are, but I will look for you,
I will find you and I will let you run my pi-WAM

on your sputnik commodore c64 with 8088.

Bye

Lane W schrieb:
> Mild Shock wrote:
>> Hi,
>>
>> You are a moron, and you represent putin payed
>> trolls from the army of brainless troll morons.
>>
>> Bye
>>
>> Lane W schrieb:
>>> Mild Shock has no idea
> 
> You are one of those cerebral asshats in the first episode of Star Trek.

[toc] | [prev] | [next] | [standalone]

#671647 — Re: Yeah keep reading my posts, uninspired fool (Was: You are a moron, brainless putin payed)

Mild Shock wrote:
> Hi,
> 
> Yeah keep reading my posts, uninspired fool.
> Seems you got a glimps of imagination from my post:
> 
>  > From: Mild Shock <janburse@fastmail.fm>
>  > Subject: NVIDIA evacuated its Chinese market [Tau Scaling]
>  > Date: Thu, 23 Jul 2026 19:13:51 +0200
> 
>> How it started:
>>
>> Captain: Throw the switch, Scotty!
>> Enterprise: Cloaking Device makes it invisible
>> Spock: Military secrets are the most fleeting of all.
>> Kirk Escapes the Romulans - The Enterprise Incident
>> https://www.youtube.com/watch?v=AusAGjwlql8
> 
> But copying others in trope, is not the same
> as jolting a trope into a conservation.
> It still makes you a lame copist. Maybe you

These tropes of yours would be funnier if they were closer to truth. 
That's not even the right ballpark, Mild Shock. If I were an alpaca I 
would spit right on your nose & mouth.

[toc] | [prev] | [next] | [standalone]

#671649 — LoL (Was: Yeah keep reading my posts, uninspired fool )

LoL

Lane W schrieb:
> Mild Shock wrote:
>> Hi,
>>
>> Yeah keep reading my posts, uninspired fool.
>> Seems you got a glimps of imagination from my post:
>>
>>  > From: Mild Shock <janburse@fastmail.fm>
>>  > Subject: NVIDIA evacuated its Chinese market [Tau Scaling]
>>  > Date: Thu, 23 Jul 2026 19:13:51 +0200
>>
>>> How it started:
>>>
>>> Captain: Throw the switch, Scotty!
>>> Enterprise: Cloaking Device makes it invisible
>>> Spock: Military secrets are the most fleeting of all.
>>> Kirk Escapes the Romulans - The Enterprise Incident
>>> https://www.youtube.com/watch?v=AusAGjwlql8
>>
>> But copying others in trope, is not the same
>> as jolting a trope into a conservation.
>> It still makes you a lame copist. Maybe you
> 
> These tropes of yours would be funnier if they were closer to truth. 
> That's not even the right ballpark, Mild Shock. If I were an alpaca I 
> would spit right on your nose & mouth.

[toc] | [prev] | [next] | [standalone]

#671654 — Re: LoL (Was: Yeah keep reading my posts, uninspired fool )

Mild Shock wrote:
> LoL
> 
> Lane W schrieb:
>> Mild Shock wrote:
>>> Hi,
>>>
>>> Yeah keep reading my posts, uninspired fool.
>>> Seems you got a glimps of imagination from my post:
>>>
>>>  > From: Mild Shock <janburse@fastmail.fm>
>>>  > Subject: NVIDIA evacuated its Chinese market [Tau Scaling]
>>>  > Date: Thu, 23 Jul 2026 19:13:51 +0200
>>>
>>>> How it started:
>>>>
>>>> Captain: Throw the switch, Scotty!
>>>> Enterprise: Cloaking Device makes it invisible
>>>> Spock: Military secrets are the most fleeting of all.
>>>> Kirk Escapes the Romulans - The Enterprise Incident
>>>> https://www.youtube.com/watch?v=AusAGjwlql8
>>>
>>> But copying others in trope, is not the same
>>> as jolting a trope into a conservation.
>>> It still makes you a lame copist. Maybe you
>>
>> These tropes of yours would be funnier if they were closer to truth. 
>> That's not even the right ballpark, Mild Shock. If I were an alpaca I 
>> would spit right on your nose & mouth.
> 
I don't see how you can dispute that you made two of the same post, one 
right after the other. Not in the right ballpark? It's more exact than a 
geometry formula. That I called you an ass was not so far from the truth 
either?

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#671650 — Out of the blue accusation span 15 days [Empirical USENET study] (Was: Ignoramus or Ignorabimus: I don't care (π-WAM))

Hi,

Again I posted everything here:

 > 11.4 Giga Lips with a Budget Laptop
 > https://github.com/Jean-Luc-Picard-2021/gigabudget

The repo says, same time when I posted
the link first time:

 > This repository was archived by the
 > owner on Jul 9, 2026. It is now read-only.

Now a USENET user, who had already entitled
himself for a couple of irrational accusations

towards my side, is asking this question:

Chris M. Thomasson schrieb, Jul 24, 2026
 > Show an outline of what you
 > need you compute shader to do?

Bravo, thats a delay of a wooping 15 days.

Bye

Mild Shock schrieb:
> Hi,
> 
> If any of you guys do not understand what
> is meant by or what the implications are:
> 
> 11.4 Giga Lips with a Budget Laptop
> https://github.com/Jean-Luc-Picard-2021/gigabudget
> 
> Well I wouldn't care less. There are two
> outcomes for numb nuts:
> 
> - Ignoramus: They don't understand it, but
>    they will understand it before they die.
> 
> - Ignorabimus: They don't understand it, and
>    will never understand it, and they die.
> 
> So who cares, its not my problem, you people
> are stupid as fuck, and slow as fuck...
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Micro penis brain is in constant hiatus.
>> He can even not detect a trope.
>>
>> LoL
>>
>> Bye
>>
>> Lane W schrieb:
>>> Mild Shock wrote:
>>>> Hi,
>>>>
>>>> My mother is worried that I fucked Lane W.
>>>> aka Micro Penis mother 24 hours straight.
>>>> She was screaming, basically singing all
>>>>
>>>> the arias from operas that Luciano Pavarotti
>>>> usually sings. You Lane W. aka Micro Penis
>>>> should have heard it, since you
>>>>
>>>> live in the basement of your mothers house.
>>>
>>> No, actually remarkably, I don't. According to google I live 433 
>>> miles away from her.
>>>
>>> Strike!
>>>
>>> See, what i said about you was spot on.
>>>
>>> What you said about me was generic and incorrect.
>>>
>>> You really suck, man.
>>
> 

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#671639 — Little Data Center on Your Palm [AI Laptops for 500 USD] (Was: The Wuhan Virus that destroyed Python [ggml Manifesto]

Ni,

Now you can compare this here from 2008
with modern AI Laptops for 500-1000 USD:

Google spotlights data center inner workings
https://web.archive.org/web/20131019063218/http://news.cnet.com/8301-10784_3-9955184-7.html

There is a striking similarity, only what
once occupied a rack, has now the size
of your plam, all inside one silicon chip:

- Multiple CPU cores on the same chip
- Multiple GPU units on the same chip
- Network on the same chip communication
- Crossbar caches on the same chip
- Disk controllers on the same chip
- Multi channel RAM access on the same chip

Pretty cool!

P.S.: Example such devices with iGPU:

Intel(R) Core(TM) Ultra 7 258V
AMD Ryzen AI 7 350 w/ Radeon 860M
Apple A18 Pro, Darwin Kernel Version 25.5.0
Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU

Mild Shock schrieb:
> Hi,
> 
> Remember when first all local AI was Python
> and PyTorch APIs. And then suddently people strated
> using bare metal C/C++ Code. Here is the story:
> 
> How it started:
> 
> GPT-J or GPT-J-6B is an open-source large
> language model (LLM) developed by EleutherAI
> in 2021. As the name suggests, it is a
> generative pre-trained transformer model
> designed to produce human-like text that
> continues from a prompt.
> https://www.eleuther.ai/
> 
> How it was going [Georgi Gerganov]:
> 
> So a few days later comes out the LLaMA, I do
> some calculations and I figure out “Okay, 65
> billion parameters. You probably need about
> 40 gigs of RAM, with 4-bit quantization. So
> this can run on a MacBook. Why not do it?”
> 
> Why I was able to do it so quickly - basically,
> for all that I saw it’s pretty much GPT-J architecture
> with some modifications, like some extra memorization
> layers. It’s minor changes. Basically, again, the
> existing code for the GPT-J, I just simply
> modified it there, it happened pretty quickly.
> https://changelog.com/podcast/532
> 
> Georgi Gerganov, Bulgarian, now with Hugging
> Face, ggml-cann also running on Chinese AI chips.
> ggml Manifesto https://github.com/ggml-org/ggml
> 
> Bye
> 

[toc] | [prev] | [next] | [standalone]

#671641 — 2008: 4 Blades + Tesla S1070 versus 2026: 1 AI Laptop (Re: Little Data Center on Your Palm [AI Laptops for 500 USD])

Hi,

Feature    2008: 4 Blades + Tesla S1070
CPU Cores    16
CPU Clock (GHz)    2.5
CPU IPC (est.)    1
CPU Throughput (units)    16 x 2.5 ×1.0=40
GPU Cores    960 (4x T10P)
GPU Clock (GHz)    1.5
GPU IPC (est.)    ~1.0 (FMA)
GPU Throughput (units)    960 x 1.5x1.0=1440
Total Compute (CPU+GPU)    40 + 1440 = 1480
Memory Capacity    16-20 GB (DDR2)
Storage I/O    ~400 MB/s (HDDs)
Power Consumption    ~1500 W
Physical Size    8-12 RU + 1U GPU
Cost (2008 USD)    ~$33,000

Feature    2026: 1 Al Laptop
CPU Cores    Aug 16
CPU Clock (GHz)    4.5
CPU IPC (est.)    2
CPU Throughput (units)    16 x 4.5 ×2.0=144 (or 72 for 8c)
GPU Cores    4096
GPU Clock (GHz)    ~2.0
GPU IPC (est.)    ~1.5 (modern)
GPU Throughput (units)    4096 x 2.0x1.5=12288
Total Compute (CPU+GPU)    144 + 12288 = 12432 (or 72+12288 for 8c)
Memory Capacity    16-32 GB (DDR5)
Storage I/O    ~7000 MB/s (NVMe)
Power Consumption    ~50-100 W
Physical Size    1 laptop bag
Cost (2008 USD)    ~$500-1000

Feature    Winner
CPU Cores    Tie
CPU Clock (GHz)    Laptop (1.8x faster)
CPU IPC (est.)    Laptop (2x better)
CPU Throughput (units)    Laptop: 1.8-3.6x faster
GPU Cores    Laptop: 4.3x more cores
GPU Clock (GHz)    Laptop (1.33x faster)
GPU IPC (est.)    Laptop (1.5x better)
GPU Throughput (units)    Laptop: 8.5x more GPU throughput
Total Compute (CPU+GPU)    Laptop: 8.4x more total compute
Memory Capacity    Laptop (more, faster)
Storage I/O    Laptop: 17x faster
Power Consumption    Laptop: 15-30x more efficient
Physical Size    Laptop
Cost (2008 USD)    Laptop: 33-66x cheaper

Bye

Mild Shock schrieb:
> Ni,
> 
> Now you can compare this here from 2008
> with modern AI Laptops for 500-1000 USD:
> 
> Google spotlights data center inner workings
> https://web.archive.org/web/20131019063218/http://news.cnet.com/8301-10784_3-9955184-7.html 
> 
> 
> There is a striking similarity, only what
> once occupied a rack, has now the size
> of your plam, all inside one silicon chip:
> 
> - Multiple CPU cores on the same chip
> - Multiple GPU units on the same chip
> - Network on the same chip communication
> - Crossbar caches on the same chip
> - Disk controllers on the same chip
> - Multi channel RAM access on the same chip
> 
> Pretty cool!
> 
> P.S.: Example such devices with iGPU:
> 
> Intel(R) Core(TM) Ultra 7 258V
> AMD Ryzen AI 7 350 w/ Radeon 860M
> Apple A18 Pro, Darwin Kernel Version 25.5.0
> Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Remember when first all local AI was Python
>> and PyTorch APIs. And then suddently people strated
>> using bare metal C/C++ Code. Here is the story:
>>
>> How it started:
>>
>> GPT-J or GPT-J-6B is an open-source large
>> language model (LLM) developed by EleutherAI
>> in 2021. As the name suggests, it is a
>> generative pre-trained transformer model
>> designed to produce human-like text that
>> continues from a prompt.
>> https://www.eleuther.ai/
>>
>> How it was going [Georgi Gerganov]:
>>
>> So a few days later comes out the LLaMA, I do
>> some calculations and I figure out “Okay, 65
>> billion parameters. You probably need about
>> 40 gigs of RAM, with 4-bit quantization. So
>> this can run on a MacBook. Why not do it?”
>>
>> Why I was able to do it so quickly - basically,
>> for all that I saw it’s pretty much GPT-J architecture
>> with some modifications, like some extra memorization
>> layers. It’s minor changes. Basically, again, the
>> existing code for the GPT-J, I just simply
>> modified it there, it happened pretty quickly.
>> https://changelog.com/podcast/532
>>
>> Georgi Gerganov, Bulgarian, now with Hugging
>> Face, ggml-cann also running on Chinese AI chips.
>> ggml Manifesto https://github.com/ggml-org/ggml
>>
>> Bye
>>
> 

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#671645 — Re: Little Data Center on Your Palm [AI Laptops for 500 USD] (Was: The Wuhan Virus that destroyed Python [ggml Manifesto]

Mild Shock wrote:


> Now you can compare this here from 2008 with modern AI Laptops for
> 500-1000 USD:

you fucking irrelevant indolent impertinent puerile imbecile. This guy 
thinks shit is AI laptops. You are a shame to your country.

[toc] | [prev] | [next] | [standalone]

#671648 — LoL (Was: Little Data Center on Your Palm [AI Laptops for 500 USD])

LoL

Bradford Babkoff schrieb:
> Mild Shock wrote:
> 
> 
>> Now you can compare this here from 2008 with modern AI Laptops for
>> 500-1000 USD:
> 
> you fucking irrelevant indolent impertinent puerile imbecile. This guy
> thinks shit is AI laptops. You are a shame to your country.
> 

[toc] | [prev] | [next] | [standalone]

#671652 — Hurry the blue bus doesnt stop indefinitely (Was: The Wuhan Virus that destroyed Python [ggml Manifesto])

Hi,

Ride the snake
He's old and his skin is cold
The west is the best
The west is the best
Get here and we'll do the rest
The blue bus is calling us
The blue bus is calling us
Driver, where you taking us?

Apocalypse Now intro: The Doors, The End {1979}
https://www.youtube.com/watch?v=CIrvSJwwJUE

Bye

> Hi,
> 
> Again I posted everything here:
> 
>> 11.4 Giga Lips with a Budget Laptop
>> https://github.com/Jean-Luc-Picard-2021/gigabudget
> 
> The repo says, same time when I posted
> the link first time:
> 
>> This repository was archived by the
>> owner on Jul 9, 2026. It is now read-only.
> 
> Now a USENET user, who had already entitled
> himself for a couple of irrational accusations
> 
> towards my side, is asking this question:
> 
> Chris M. Thomasson schrieb, Jul 24, 2026
>> Show an outline of what you
>> need you compute shader to do?
> 
> Bravo, thats a delay of a wooping 15 days.
> 
> Bye 


Mild Shock schrieb:
> Hi,
> 
> Remember when first all local AI was Python
> and PyTorch APIs. And then suddently people strated
> using bare metal C/C++ Code. Here is the story:
> 
> How it started:
> 
> GPT-J or GPT-J-6B is an open-source large
> language model (LLM) developed by EleutherAI
> in 2021. As the name suggests, it is a
> generative pre-trained transformer model
> designed to produce human-like text that
> continues from a prompt.
> https://www.eleuther.ai/
> 
> How it was going [Georgi Gerganov]:
> 
> So a few days later comes out the LLaMA, I do
> some calculations and I figure out “Okay, 65
> billion parameters. You probably need about
> 40 gigs of RAM, with 4-bit quantization. So
> this can run on a MacBook. Why not do it?”
> 
> Why I was able to do it so quickly - basically,
> for all that I saw it’s pretty much GPT-J architecture
> with some modifications, like some extra memorization
> layers. It’s minor changes. Basically, again, the
> existing code for the GPT-J, I just simply
> modified it there, it happened pretty quickly.
> https://changelog.com/podcast/532
> 
> Georgi Gerganov, Bulgarian, now with Hugging
> Face, ggml-cann also running on Chinese AI chips.
> ggml Manifesto https://github.com/ggml-org/ggml
> 
> Bye
> 

[toc] | [prev] | [next] | [standalone]

#671655 — Not SIMD, a MIMD design for NVIDIA Volta (Re: Hurry the blue bus doesnt stop indefinitely)

Hi,

Its not tested on some Single Instruction/
Multiple Data (SIMD) GPU. It was only tested on
AI Laptops with Multiple instruction, Multiple

Data (GPU) architecture for the scalar registers
per logical thread. As introduced by NVIDIA Volta
in around 2017:

 > the first product was not announced until May 2017
 > https://en.wikipedia.org/wiki/Volta_%28microarchitecture%29

Although I wrote the code of Hack VM with SIMD
in mind, I never tested it on a pure SIMD GPU,
and I never ported boot.mjs or boot2.mjs to

WebGL2 / GLSL. I uploaded WebGPU / WGSL. Among the
tester I had were these AI Laptops, that could all
run WebGPU / WGSL in a browser:

 > Intel(R) Core(TM) Ultra 7 258V
 > AMD Ryzen AI 7 350 w/ Radeon 860M
 > Apple A18 Pro, Darwin Kernel Version 25.5.0
 > Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU

Some AI Laptops had WebGPU / WGSL still behind
a browser flag, since its relatively new on ARM.
Also the above AI Laptops have all a iGPU and

not a separate GPU card.

Bye

Mild Shock schrieb:> Hi,
 >
 >  > Show an outline of what you need you compute shader to do?
 >
 > Its all on GitHub , for the 100-th time .
 > Just RTFM , i.e. study the repo and the
 > medim article. Just follow this link:
 >
 > 11.4 Giga Lips with a Budget Laptop
 > https://github.com/Jean-Luc-Picard-2021/gigabudget
 >
 > Whats wrong with you guys, did the AI boom
 > suck out all your braincells. I really have
 > no words for being that stupid and slow.
 >
 > Bye
 >
 > In particular the repo contains two versions
 > of a Hack VM, written in WebGPU / WGSL:
 >
 > Hack VM: Version 1.0
 > 
https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/boot.mjs 

 >
 >
 > Hack VM: Version 2.0
 > 
https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example64/boot2.mjs 

 >
 >
 > Version 1.0 is for a single compute shader
 > expriment. And Version 2.o is for a multi
 > compute shader experiment.

Mild Shock schrieb:
> Hi,
> 
> Ride the snake
> He's old and his skin is cold
> The west is the best
> The west is the best
> Get here and we'll do the rest
> The blue bus is calling us
> The blue bus is calling us
> Driver, where you taking us?
> 
> Apocalypse Now intro: The Doors, The End {1979}
> https://www.youtube.com/watch?v=CIrvSJwwJUE
> 
> Bye
> 
>> Hi,
>>
>> Again I posted everything here:
>>
>>> 11.4 Giga Lips with a Budget Laptop
>>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>
>> The repo says, same time when I posted
>> the link first time:
>>
>>> This repository was archived by the
>>> owner on Jul 9, 2026. It is now read-only.
>>
>> Now a USENET user, who had already entitled
>> himself for a couple of irrational accusations
>>
>> towards my side, is asking this question:
>>
>> Chris M. Thomasson schrieb, Jul 24, 2026
>>> Show an outline of what you
>>> need you compute shader to do?
>>
>> Bravo, thats a delay of a wooping 15 days.
>>
>> Bye 
> 
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Remember when first all local AI was Python
>> and PyTorch APIs. And then suddently people strated
>> using bare metal C/C++ Code. Here is the story:
>>
>> How it started:
>>
>> GPT-J or GPT-J-6B is an open-source large
>> language model (LLM) developed by EleutherAI
>> in 2021. As the name suggests, it is a
>> generative pre-trained transformer model
>> designed to produce human-like text that
>> continues from a prompt.
>> https://www.eleuther.ai/
>>
>> How it was going [Georgi Gerganov]:
>>
>> So a few days later comes out the LLaMA, I do
>> some calculations and I figure out “Okay, 65
>> billion parameters. You probably need about
>> 40 gigs of RAM, with 4-bit quantization. So
>> this can run on a MacBook. Why not do it?”
>>
>> Why I was able to do it so quickly - basically,
>> for all that I saw it’s pretty much GPT-J architecture
>> with some modifications, like some extra memorization
>> layers. It’s minor changes. Basically, again, the
>> existing code for the GPT-J, I just simply
>> modified it there, it happened pretty quickly.
>> https://changelog.com/podcast/532
>>
>> Georgi Gerganov, Bulgarian, now with Hugging
>> Face, ggml-cann also running on Chinese AI chips.
>> ggml Manifesto https://github.com/ggml-org/ggml
>>
>> Bye
>>
> 

[toc] | [prev] | [next] | [standalone]

#671656 — Could take 3-4 months find machine / browser (Was Not SIMD, a MIMD design for NVIDIA Volta)

Hi,

It could one take 3-4 months to find a suitable
machine and suitable browser, so that MIMD is
utilized, especially if you have:

 > A Sputnik Commodore C64 with 8088
 > from the basement of your mother

But maybe somebody figures out it also runs on
SIMD. Its not in my focus to test the SIMD
platform, I do not intend to go back in time

to 2008, and a Tesla S1070:

Tesla S1070 was an professional graphics card by NVIDIA
Its price at launch was 7999 US Dollars.
https://www.techpowerup.com/gpu-specs/tesla-s1070.c1540

Also not in 2026, a RTX 5090:

NVIDIA® RTX™ 5090 most powerful GeForce GPU ever made
With Boost Clock Speed its at 3779 USD
https://marketplace.nvidia.com/en-us/consumer/graphics-cards/?locale=en-us&page=1&limit=15&gpu=RTX+5090&has_offer=is_bestselling

The title of the experiment is really Budget Laptop.
What is a litte unspoken in the title, that the Laptop
is an AI Laptop. But you see it in the description:

 > 11.4 Giga Lips with a Budget Laptop
 > At the end of 2025 we acquired a couple of AI Laptops
 > https://github.com/Jean-Luc-Picard-2021/gigabudget

These AI Laptops are quite affordable ,
500 USD to 1000 USD.

Bye

Mild Shock schrieb:
> Hi,
> 
> Its not tested on some Single Instruction/
> Multiple Data (SIMD) GPU. It was only tested on
> AI Laptops with Multiple instruction, Multiple
> 
> Data (GPU) architecture for the scalar registers
> per logical thread. As introduced by NVIDIA Volta
> in around 2017:
> 
>  > the first product was not announced until May 2017
>  > https://en.wikipedia.org/wiki/Volta_%28microarchitecture%29
> 
> Although I wrote the code of Hack VM with SIMD
> in mind, I never tested it on a pure SIMD GPU,
> and I never ported boot.mjs or boot2.mjs to
> 
> WebGL2 / GLSL. I uploaded WebGPU / WGSL. Among the
> tester I had were these AI Laptops, that could all
> run WebGPU / WGSL in a browser:
> 
>  > Intel(R) Core(TM) Ultra 7 258V
>  > AMD Ryzen AI 7 350 w/ Radeon 860M
>  > Apple A18 Pro, Darwin Kernel Version 25.5.0
>  > Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU
> 
> Some AI Laptops had WebGPU / WGSL still behind
> a browser flag, since its relatively new on ARM.
> Also the above AI Laptops have all a iGPU and
> 
> not a separate GPU card.
> 
> Bye
> 
> Mild Shock schrieb:> Hi,
>  >
>  >  > Show an outline of what you need you compute shader to do?
>  >
>  > Its all on GitHub , for the 100-th time .
>  > Just RTFM , i.e. study the repo and the
>  > medim article. Just follow this link:
>  >
>  > 11.4 Giga Lips with a Budget Laptop
>  > https://github.com/Jean-Luc-Picard-2021/gigabudget
>  >
>  > Whats wrong with you guys, did the AI boom
>  > suck out all your braincells. I really have
>  > no words for being that stupid and slow.
>  >
>  > Bye
>  >
>  > In particular the repo contains two versions
>  > of a Hack VM, written in WebGPU / WGSL:
>  >
>  > Hack VM: Version 1.0
>  > 
> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/boot.mjs 
> 
>  >
>  >
>  > Hack VM: Version 2.0
>  > 
> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example64/boot2.mjs 
> 
>  >
>  >
>  > Version 1.0 is for a single compute shader
>  > expriment. And Version 2.o is for a multi
>  > compute shader experiment.
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Ride the snake
>> He's old and his skin is cold
>> The west is the best
>> The west is the best
>> Get here and we'll do the rest
>> The blue bus is calling us
>> The blue bus is calling us
>> Driver, where you taking us?
>>
>> Apocalypse Now intro: The Doors, The End {1979}
>> https://www.youtube.com/watch?v=CIrvSJwwJUE
>>
>> Bye
>>
>>> Hi,
>>>
>>> Again I posted everything here:
>>>
>>>> 11.4 Giga Lips with a Budget Laptop
>>>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>>
>>> The repo says, same time when I posted
>>> the link first time:
>>>
>>>> This repository was archived by the
>>>> owner on Jul 9, 2026. It is now read-only.
>>>
>>> Now a USENET user, who had already entitled
>>> himself for a couple of irrational accusations
>>>
>>> towards my side, is asking this question:
>>>
>>> Chris M. Thomasson schrieb, Jul 24, 2026
>>>> Show an outline of what you
>>>> need you compute shader to do?
>>>
>>> Bravo, thats a delay of a wooping 15 days.
>>>
>>> Bye 
>>
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Remember when first all local AI was Python
>>> and PyTorch APIs. And then suddently people strated
>>> using bare metal C/C++ Code. Here is the story:
>>>
>>> How it started:
>>>
>>> GPT-J or GPT-J-6B is an open-source large
>>> language model (LLM) developed by EleutherAI
>>> in 2021. As the name suggests, it is a
>>> generative pre-trained transformer model
>>> designed to produce human-like text that
>>> continues from a prompt.
>>> https://www.eleuther.ai/
>>>
>>> How it was going [Georgi Gerganov]:
>>>
>>> So a few days later comes out the LLaMA, I do
>>> some calculations and I figure out “Okay, 65
>>> billion parameters. You probably need about
>>> 40 gigs of RAM, with 4-bit quantization. So
>>> this can run on a MacBook. Why not do it?”
>>>
>>> Why I was able to do it so quickly - basically,
>>> for all that I saw it’s pretty much GPT-J architecture
>>> with some modifications, like some extra memorization
>>> layers. It’s minor changes. Basically, again, the
>>> existing code for the GPT-J, I just simply
>>> modified it there, it happened pretty quickly.
>>> https://changelog.com/podcast/532
>>>
>>> Georgi Gerganov, Bulgarian, now with Hugging
>>> Face, ggml-cann also running on Chinese AI chips.
>>> ggml Manifesto https://github.com/ggml-org/ggml
>>>
>>> Bye
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#671673 — The Koan of pi-WAM queues [FORTRAN-S] (Was: Not SIMD, a MIMD design for NVIDIA Volta)

Hi,

You see it all boils down to find your inner peace
by an immaculate inception of some queue datatype.

KOAN/Fortran-S was an early 1990s research programming
system for distributed-memory multiprocessors . Developed
at ENS Lyon in the early 1990s . Often listed alongside
other historical parallel programming efforts.

The Message Passing: The research explicitly
compared the SVM approach against message passing
on the same hardware . The finding was that SVM
could achieve good performance without the low-level

complexity of managing explicit messages, though
the best results often came from a hybrid approach (sic!)
Here is an interesting baseline, from Java,
a class ElevenSingle that only does:

     public static void run() {
         for (int A = 1; A < 192; A++) {
             int Y = (771-A)/3;
             for (int B = A; B < Y; B++) {
                 int Z = (771-A-B)/2;
                 for (int C = B; C < Z; C++) {
                     int D = 711-A-B-C;
                     if (A*B*C == 711000000/D &&
                           711000000 % D == 0)
     System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
                 }
             }
         }
     }

And then compare it to ElevenMulti, doing some
Work Balancing Scheduler Tetris Game with 8 cores:

ElevenSingle
A=120, B=125, C=150, D=316
6.628 ms

ElevenMulti
A=120, B=125, C=150, D=316
1.941 ms

Not great, not terrible!

Bye

Mild Shock schrieb:
> Hi,
> 
> Its not tested on some Single Instruction/
> Multiple Data (SIMD) GPU. It was only tested on
> AI Laptops with Multiple instruction, Multiple
> 
> Data (GPU) architecture for the scalar registers
> per logical thread. As introduced by NVIDIA Volta
> in around 2017:
> 
>  > the first product was not announced until May 2017
>  > https://en.wikipedia.org/wiki/Volta_%28microarchitecture%29
> 
> Although I wrote the code of Hack VM with SIMD
> in mind, I never tested it on a pure SIMD GPU,
> and I never ported boot.mjs or boot2.mjs to
> 
> WebGL2 / GLSL. I uploaded WebGPU / WGSL. Among the
> tester I had were these AI Laptops, that could all
> run WebGPU / WGSL in a browser:
> 
>  > Intel(R) Core(TM) Ultra 7 258V
>  > AMD Ryzen AI 7 350 w/ Radeon 860M
>  > Apple A18 Pro, Darwin Kernel Version 25.5.0
>  > Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU
> 
> Some AI Laptops had WebGPU / WGSL still behind
> a browser flag, since its relatively new on ARM.
> Also the above AI Laptops have all a iGPU and
> 
> not a separate GPU card.
> 
> Bye
> 
> Mild Shock schrieb:> Hi,
>  >
>  >  > Show an outline of what you need you compute shader to do?
>  >
>  > Its all on GitHub , for the 100-th time .
>  > Just RTFM , i.e. study the repo and the
>  > medim article. Just follow this link:
>  >
>  > 11.4 Giga Lips with a Budget Laptop
>  > https://github.com/Jean-Luc-Picard-2021/gigabudget
>  >
>  > Whats wrong with you guys, did the AI boom
>  > suck out all your braincells. I really have
>  > no words for being that stupid and slow.
>  >
>  > Bye
>  >
>  > In particular the repo contains two versions
>  > of a Hack VM, written in WebGPU / WGSL:
>  >
>  > Hack VM: Version 1.0
>  > 
> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/boot.mjs 
> 
>  >
>  >
>  > Hack VM: Version 2.0
>  > 
> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example64/boot2.mjs 
> 
>  >
>  >
>  > Version 1.0 is for a single compute shader
>  > expriment. And Version 2.o is for a multi
>  > compute shader experiment.
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Ride the snake
>> He's old and his skin is cold
>> The west is the best
>> The west is the best
>> Get here and we'll do the rest
>> The blue bus is calling us
>> The blue bus is calling us
>> Driver, where you taking us?
>>
>> Apocalypse Now intro: The Doors, The End {1979}
>> https://www.youtube.com/watch?v=CIrvSJwwJUE
>>
>> Bye
>>
>>> Hi,
>>>
>>> Again I posted everything here:
>>>
>>>> 11.4 Giga Lips with a Budget Laptop
>>>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>>
>>> The repo says, same time when I posted
>>> the link first time:
>>>
>>>> This repository was archived by the
>>>> owner on Jul 9, 2026. It is now read-only.
>>>
>>> Now a USENET user, who had already entitled
>>> himself for a couple of irrational accusations
>>>
>>> towards my side, is asking this question:
>>>
>>> Chris M. Thomasson schrieb, Jul 24, 2026
>>>> Show an outline of what you
>>>> need you compute shader to do?
>>>
>>> Bravo, thats a delay of a wooping 15 days.
>>>
>>> Bye 
>>
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Remember when first all local AI was Python
>>> and PyTorch APIs. And then suddently people strated
>>> using bare metal C/C++ Code. Here is the story:
>>>
>>> How it started:
>>>
>>> GPT-J or GPT-J-6B is an open-source large
>>> language model (LLM) developed by EleutherAI
>>> in 2021. As the name suggests, it is a
>>> generative pre-trained transformer model
>>> designed to produce human-like text that
>>> continues from a prompt.
>>> https://www.eleuther.ai/
>>>
>>> How it was going [Georgi Gerganov]:
>>>
>>> So a few days later comes out the LLaMA, I do
>>> some calculations and I figure out “Okay, 65
>>> billion parameters. You probably need about
>>> 40 gigs of RAM, with 4-bit quantization. So
>>> this can run on a MacBook. Why not do it?”
>>>
>>> Why I was able to do it so quickly - basically,
>>> for all that I saw it’s pretty much GPT-J architecture
>>> with some modifications, like some extra memorization
>>> layers. It’s minor changes. Basically, again, the
>>> existing code for the GPT-J, I just simply
>>> modified it there, it happened pretty quickly.
>>> https://changelog.com/podcast/532
>>>
>>> Georgi Gerganov, Bulgarian, now with Hugging
>>> Face, ggml-cann also running on Chinese AI chips.
>>> ggml Manifesto https://github.com/ggml-org/ggml
>>>
>>> Bye
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#671674 — The turbo capping of AI Laptops (Re: The Koan of pi-WAM queues [FORTRAN-S])

Hi,

Mostlikely we see the turbo capping of certain
CPU designs, that have turbo mode when CPU
is used with mostly only one thread active,

but throttles down when more threads are active.
This has even resulted in designs with performance
cores and economy cores.

So the factor below for 8 cores is only:

6.628 ms / 1.941 ms = 3.415

But if you discount for turbo versus non-turbo,
i.e. 5 GHz versus 3 GHz or so. You see that
the machine was not utilized very badly:

3.415 * 5 / 3 = 5.691

The class ElevenMulti does use 6 workers,
and 1 producer and 1 consumer.

Bye

Mild Shock schrieb:
> Hi,
> 
> You see it all boils down to find your inner peace
> by an immaculate inception of some queue datatype.
> 
> KOAN/Fortran-S was an early 1990s research programming
> system for distributed-memory multiprocessors . Developed
> at ENS Lyon in the early 1990s . Often listed alongside
> other historical parallel programming efforts.
> 
> The Message Passing: The research explicitly
> compared the SVM approach against message passing
> on the same hardware . The finding was that SVM
> could achieve good performance without the low-level
> 
> complexity of managing explicit messages, though
> the best results often came from a hybrid approach (sic!)
> Here is an interesting baseline, from Java,
> a class ElevenSingle that only does:
> 
>      public static void run() {
>          for (int A = 1; A < 192; A++) {
>              int Y = (771-A)/3;
>              for (int B = A; B < Y; B++) {
>                  int Z = (771-A-B)/2;
>                  for (int C = B; C < Z; C++) {
>                      int D = 711-A-B-C;
>                      if (A*B*C == 711000000/D &&
>                            711000000 % D == 0)
>      System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
>                  }
>              }
>          }
>      }
> 
> And then compare it to ElevenMulti, doing some
> Work Balancing Scheduler Tetris Game with 8 cores:
> 
> ElevenSingle
> A=120, B=125, C=150, D=316
> 6.628 ms
> 
> ElevenMulti
> A=120, B=125, C=150, D=316
> 1.941 ms
> 
> Not great, not terrible!
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Its not tested on some Single Instruction/
>> Multiple Data (SIMD) GPU. It was only tested on
>> AI Laptops with Multiple instruction, Multiple
>>
>> Data (GPU) architecture for the scalar registers
>> per logical thread. As introduced by NVIDIA Volta
>> in around 2017:
>>
>>  > the first product was not announced until May 2017
>>  > https://en.wikipedia.org/wiki/Volta_%28microarchitecture%29
>>
>> Although I wrote the code of Hack VM with SIMD
>> in mind, I never tested it on a pure SIMD GPU,
>> and I never ported boot.mjs or boot2.mjs to
>>
>> WebGL2 / GLSL. I uploaded WebGPU / WGSL. Among the
>> tester I had were these AI Laptops, that could all
>> run WebGPU / WGSL in a browser:
>>
>>  > Intel(R) Core(TM) Ultra 7 258V
>>  > AMD Ryzen AI 7 350 w/ Radeon 860M
>>  > Apple A18 Pro, Darwin Kernel Version 25.5.0
>>  > Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU
>>
>> Some AI Laptops had WebGPU / WGSL still behind
>> a browser flag, since its relatively new on ARM.
>> Also the above AI Laptops have all a iGPU and
>>
>> not a separate GPU card.
>>
>> Bye
>>
>> Mild Shock schrieb:> Hi,
>>  >
>>  >  > Show an outline of what you need you compute shader to do?
>>  >
>>  > Its all on GitHub , for the 100-th time .
>>  > Just RTFM , i.e. study the repo and the
>>  > medim article. Just follow this link:
>>  >
>>  > 11.4 Giga Lips with a Budget Laptop
>>  > https://github.com/Jean-Luc-Picard-2021/gigabudget
>>  >
>>  > Whats wrong with you guys, did the AI boom
>>  > suck out all your braincells. I really have
>>  > no words for being that stupid and slow.
>>  >
>>  > Bye
>>  >
>>  > In particular the repo contains two versions
>>  > of a Hack VM, written in WebGPU / WGSL:
>>  >
>>  > Hack VM: Version 1.0
>>  > 
>> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/boot.mjs 
>>
>>  >
>>  >
>>  > Hack VM: Version 2.0
>>  > 
>> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example64/boot2.mjs 
>>
>>  >
>>  >
>>  > Version 1.0 is for a single compute shader
>>  > expriment. And Version 2.o is for a multi
>>  > compute shader experiment.
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Ride the snake
>>> He's old and his skin is cold
>>> The west is the best
>>> The west is the best
>>> Get here and we'll do the rest
>>> The blue bus is calling us
>>> The blue bus is calling us
>>> Driver, where you taking us?
>>>
>>> Apocalypse Now intro: The Doors, The End {1979}
>>> https://www.youtube.com/watch?v=CIrvSJwwJUE
>>>
>>> Bye
>>>
>>>> Hi,
>>>>
>>>> Again I posted everything here:
>>>>
>>>>> 11.4 Giga Lips with a Budget Laptop
>>>>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>>>
>>>> The repo says, same time when I posted
>>>> the link first time:
>>>>
>>>>> This repository was archived by the
>>>>> owner on Jul 9, 2026. It is now read-only.
>>>>
>>>> Now a USENET user, who had already entitled
>>>> himself for a couple of irrational accusations
>>>>
>>>> towards my side, is asking this question:
>>>>
>>>> Chris M. Thomasson schrieb, Jul 24, 2026
>>>>> Show an outline of what you
>>>>> need you compute shader to do?
>>>>
>>>> Bravo, thats a delay of a wooping 15 days.
>>>>
>>>> Bye 
>>>
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> Remember when first all local AI was Python
>>>> and PyTorch APIs. And then suddently people strated
>>>> using bare metal C/C++ Code. Here is the story:
>>>>
>>>> How it started:
>>>>
>>>> GPT-J or GPT-J-6B is an open-source large
>>>> language model (LLM) developed by EleutherAI
>>>> in 2021. As the name suggests, it is a
>>>> generative pre-trained transformer model
>>>> designed to produce human-like text that
>>>> continues from a prompt.
>>>> https://www.eleuther.ai/
>>>>
>>>> How it was going [Georgi Gerganov]:
>>>>
>>>> So a few days later comes out the LLaMA, I do
>>>> some calculations and I figure out “Okay, 65
>>>> billion parameters. You probably need about
>>>> 40 gigs of RAM, with 4-bit quantization. So
>>>> this can run on a MacBook. Why not do it?”
>>>>
>>>> Why I was able to do it so quickly - basically,
>>>> for all that I saw it’s pretty much GPT-J architecture
>>>> with some modifications, like some extra memorization
>>>> layers. It’s minor changes. Basically, again, the
>>>> existing code for the GPT-J, I just simply
>>>> modified it there, it happened pretty quickly.
>>>> https://changelog.com/podcast/532
>>>>
>>>> Georgi Gerganov, Bulgarian, now with Hugging
>>>> Face, ggml-cann also running on Chinese AI chips.
>>>> ggml Manifesto https://github.com/ggml-org/ggml
>>>>
>>>> Bye
>>>>
>>>
>>
> 

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#671676 — Re: The Koan of pi-WAM queues [FORTRAN-S] (Was: Not SIMD, a MIMD design for NVIDIA Volta)

On 07/26/2026 10:52 AM, Mild Shock wrote:
> Hi,
>
> You see it all boils down to find your inner peace
> by an immaculate inception of some queue datatype.
>
> KOAN/Fortran-S was an early 1990s research programming
> system for distributed-memory multiprocessors . Developed
> at ENS Lyon in the early 1990s . Often listed alongside
> other historical parallel programming efforts.
>
> The Message Passing: The research explicitly
> compared the SVM approach against message passing
> on the same hardware . The finding was that SVM
> could achieve good performance without the low-level
>
> complexity of managing explicit messages, though
> the best results often came from a hybrid approach (sic!)
> Here is an interesting baseline, from Java,
> a class ElevenSingle that only does:
>
>      public static void run() {
>          for (int A = 1; A < 192; A++) {
>              int Y = (771-A)/3;
>              for (int B = A; B < Y; B++) {
>                  int Z = (771-A-B)/2;
>                  for (int C = B; C < Z; C++) {
>                      int D = 711-A-B-C;
>                      if (A*B*C == 711000000/D &&
>                            711000000 % D == 0)
>      System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
>                  }
>              }
>          }
>      }
>
> And then compare it to ElevenMulti, doing some
> Work Balancing Scheduler Tetris Game with 8 cores:
>
> ElevenSingle
> A=120, B=125, C=150, D=316
> 6.628 ms
>
> ElevenMulti
> A=120, B=125, C=150, D=316
> 1.941 ms
>
> Not great, not terrible!
>
> Bye
>
> Mild Shock schrieb:
>> Hi,
>>
>> Its not tested on some Single Instruction/
>> Multiple Data (SIMD) GPU. It was only tested on
>> AI Laptops with Multiple instruction, Multiple
>>
>> Data (GPU) architecture for the scalar registers
>> per logical thread. As introduced by NVIDIA Volta
>> in around 2017:
>>
>>  > the first product was not announced until May 2017
>>  > https://en.wikipedia.org/wiki/Volta_%28microarchitecture%29
>>
>> Although I wrote the code of Hack VM with SIMD
>> in mind, I never tested it on a pure SIMD GPU,
>> and I never ported boot.mjs or boot2.mjs to
>>
>> WebGL2 / GLSL. I uploaded WebGPU / WGSL. Among the
>> tester I had were these AI Laptops, that could all
>> run WebGPU / WGSL in a browser:
>>
>>  > Intel(R) Core(TM) Ultra 7 258V
>>  > AMD Ryzen AI 7 350 w/ Radeon 860M
>>  > Apple A18 Pro, Darwin Kernel Version 25.5.0
>>  > Snapdragon(R) X - X126100 - Qualcomm(R) Oryon(TM) CPU
>>
>> Some AI Laptops had WebGPU / WGSL still behind
>> a browser flag, since its relatively new on ARM.
>> Also the above AI Laptops have all a iGPU and
>>
>> not a separate GPU card.
>>
>> Bye
>>
>> Mild Shock schrieb:> Hi,
>>  >
>>  >  > Show an outline of what you need you compute shader to do?
>>  >
>>  > Its all on GitHub , for the 100-th time .
>>  > Just RTFM , i.e. study the repo and the
>>  > medim article. Just follow this link:
>>  >
>>  > 11.4 Giga Lips with a Budget Laptop
>>  > https://github.com/Jean-Luc-Picard-2021/gigabudget
>>  >
>>  > Whats wrong with you guys, did the AI boom
>>  > suck out all your braincells. I really have
>>  > no words for being that stupid and slow.
>>  >
>>  > Bye
>>  >
>>  > In particular the repo contains two versions
>>  > of a Hack VM, written in WebGPU / WGSL:
>>  >
>>  > Hack VM: Version 1.0
>>  >
>> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example63/boot.mjs
>>
>>  >
>>  >
>>  > Hack VM: Version 2.0
>>  >
>> https://github.com/Jean-Luc-Picard-2021/gigabudget/blob/main/course/example64/boot2.mjs
>>
>>  >
>>  >
>>  > Version 1.0 is for a single compute shader
>>  > expriment. And Version 2.o is for a multi
>>  > compute shader experiment.
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Ride the snake
>>> He's old and his skin is cold
>>> The west is the best
>>> The west is the best
>>> Get here and we'll do the rest
>>> The blue bus is calling us
>>> The blue bus is calling us
>>> Driver, where you taking us?
>>>
>>> Apocalypse Now intro: The Doors, The End {1979}
>>> https://www.youtube.com/watch?v=CIrvSJwwJUE
>>>
>>> Bye
>>>
>>>> Hi,
>>>>
>>>> Again I posted everything here:
>>>>
>>>>> 11.4 Giga Lips with a Budget Laptop
>>>>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>>>
>>>> The repo says, same time when I posted
>>>> the link first time:
>>>>
>>>>> This repository was archived by the
>>>>> owner on Jul 9, 2026. It is now read-only.
>>>>
>>>> Now a USENET user, who had already entitled
>>>> himself for a couple of irrational accusations
>>>>
>>>> towards my side, is asking this question:
>>>>
>>>> Chris M. Thomasson schrieb, Jul 24, 2026
>>>>> Show an outline of what you
>>>>> need you compute shader to do?
>>>>
>>>> Bravo, thats a delay of a wooping 15 days.
>>>>
>>>> Bye
>>>
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> Remember when first all local AI was Python
>>>> and PyTorch APIs. And then suddently people strated
>>>> using bare metal C/C++ Code. Here is the story:
>>>>
>>>> How it started:
>>>>
>>>> GPT-J or GPT-J-6B is an open-source large
>>>> language model (LLM) developed by EleutherAI
>>>> in 2021. As the name suggests, it is a
>>>> generative pre-trained transformer model
>>>> designed to produce human-like text that
>>>> continues from a prompt.
>>>> https://www.eleuther.ai/
>>>>
>>>> How it was going [Georgi Gerganov]:
>>>>
>>>> So a few days later comes out the LLaMA, I do
>>>> some calculations and I figure out “Okay, 65
>>>> billion parameters. You probably need about
>>>> 40 gigs of RAM, with 4-bit quantization. So
>>>> this can run on a MacBook. Why not do it?”
>>>>
>>>> Why I was able to do it so quickly - basically,
>>>> for all that I saw it’s pretty much GPT-J architecture
>>>> with some modifications, like some extra memorization
>>>> layers. It’s minor changes. Basically, again, the
>>>> existing code for the GPT-J, I just simply
>>>> modified it there, it happened pretty quickly.
>>>> https://changelog.com/podcast/532
>>>>
>>>> Georgi Gerganov, Bulgarian, now with Hugging
>>>> Face, ggml-cann also running on Chinese AI chips.
>>>> ggml Manifesto https://github.com/ggml-org/ggml
>>>>
>>>> Bye
>>>>
>>>
>>
>


Oh, that's just "tricks of p-adic arithmetic".

Like other sock-puppet howler trolls, when confronted
with its base incredulity, it will descend to its
lower levers of the pathos variety.

You might be happier learning about Julia trees and
raster ops, instead of shilling yet another Ramanujan
series without saying how it's made.

Bulgarians, that's some real Boris and Natasha crap,
forget Hungarians and Bulgarians.

[toc] | [prev] | [next] | [standalone]

#671682 — Why forget Bulgarians, never on my mind (Re: The Koan of pi-WAM queues [FORTRAN-S] (Was: Not SIMD, a MIMD design for NVIDIA Volta)

Hi,

Whats this "forget" trope of glue sniffing
Rossy Boy with his herpes blisters?

 > Bulgarians, that's some real Boris and Natasha crap,
 > forget Hungarians and Bulgarians.

Why should I forget Bulgarians,
they are never on my mind. Do you
see me doing ggml stuff?

I only hypothesized that it is
over for Python as the machine
learning language or AI inferencing

locally on AI laptops language, and
made the ggml case, so I already forgot
about them. Which might give you a glimps,

why WebGPU was used for this here:

11.4 Giga Lips with a Budget Laptop
https://github.com/Jean-Luc-Picard-2021/gigabudget

Is an interesting choice. Even
github has some Languages statistics,
giving an account what I used:

HTML 67.5% JavaScript 23.1% CSS 9.4%

Have Fun!

Bye

P.S.: The example below is not p-adics,
you complete imbecil moron. Its just:

7-11 cubic Solution by Pritchard & Gries
https://www.cs.cornell.edu/gries/TechReports/83-574.pdf

Ross Finlayson schrieb:
> On 07/26/2026 10:52 AM, Mild Shock wrote:
>> Hi,
>>
>> You see it all boils down to find your inner peace
>> by an immaculate inception of some queue datatype.
>>
>> KOAN/Fortran-S was an early 1990s research programming
>> system for distributed-memory multiprocessors . Developed
>> at ENS Lyon in the early 1990s . Often listed alongside
>> other historical parallel programming efforts.
>>
>> The Message Passing: The research explicitly
>> compared the SVM approach against message passing
>> on the same hardware . The finding was that SVM
>> could achieve good performance without the low-level
>>
>> complexity of managing explicit messages, though
>> the best results often came from a hybrid approach (sic!)
>> Here is an interesting baseline, from Java,
>> a class ElevenSingle that only does:
>>
>>      public static void run() {
>>          for (int A = 1; A < 192; A++) {
>>              int Y = (771-A)/3;
>>              for (int B = A; B < Y; B++) {
>>                  int Z = (771-A-B)/2;
>>                  for (int C = B; C < Z; C++) {
>>                      int D = 711-A-B-C;
>>                      if (A*B*C == 711000000/D &&
>>                            711000000 % D == 0)
>>      System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
>>                  }
>>              }
>>          }
>>      }
>>
>> And then compare it to ElevenMulti, doing some
>> Work Balancing Scheduler Tetris Game with 8 cores:
>>
>> ElevenSingle
>> A=120, B=125, C=150, D=316
>> 6.628 ms
>>
>> ElevenMulti
>> A=120, B=125, C=150, D=316
>> 1.941 ms
>>
>> Not great, not terrible!
>>
>> Bye
>
> Oh, that's just "tricks of p-adic arithmetic".
> 
> Like other sock-puppet howler trolls, when confronted
> with its base incredulity, it will descend to its
> lower levers of the pathos variety.
> 
> You might be happier learning about Julia trees and
> raster ops, instead of shilling yet another Ramanujan
> series without saying how it's made.
> 
> Bulgarians, that's some real Boris and Natasha crap,
> forget Hungarians and Bulgarians.
> 
> 

[toc] | [prev] | [next] | [standalone]

#671683 — miniTriton CUDA is an alternative to torch variants (Was: Why forget Bulgarians, never on my mind)

Hi,

Some counter PyTorch Python trends are
for example OpenAIs Triton. And the variant
miniTriton CUDA vibe produced by Kimi K3 (sic!):

"We further tested whether Kimi K3 could build
a GPU programming system from scratch. Kimi K3
developed MiniTriton, a compact Triton-like
compiler with its own tile-level IR layer over
MLIR, optimization passes, and a PTX code-
generation pipeline.

Across supported roofline benchmarks, MiniTriton
delivers performance on par with or better than
Triton and torch.compile — beating Triton on
certain workloads. Beyond microbenchmarks,
MiniTriton sustains end-to-end nanoGPT training
with stable convergence, the loss curve

closely tracking the reference with only minor
divergence — validating the full pipeline on a
realistic workload. These results demonstrate
that Kimi K3 can build a coherent end-to-end
compiler — from DSL frontend and IR passes to
PTX codegen and runtime — rather than isolated

kernels; its from-scratch Tensor Core path
already rivals Triton’s extensively optimized stack."

GPU Compiler Development
https://www.kimi.com/blog/kimi-k3

Although many GPU corporate stuff is anonymized,
and some AI papers have lists of 30 authors. Here
nanoGPT is mentioned which is tied to the name

Andrej Karpathy. See also here:

Update Nov 2025 nanoGPT has a new and
improved cousin called nanochat.
https://github.com/karpathy/nanogpt

But as can be seen, he moved on to another project.

Bye

Mild Shock schrieb:
> Hi,
> 
> Whats this "forget" trope of glue sniffing
> Rossy Boy with his herpes blisters?
> 
>  > Bulgarians, that's some real Boris and Natasha crap,
>  > forget Hungarians and Bulgarians.
> 
> Why should I forget Bulgarians,
> they are never on my mind. Do you
> see me doing ggml stuff?
> 
> I only hypothesized that it is
> over for Python as the machine
> learning language or AI inferencing
> 
> locally on AI laptops language, and
> made the ggml case, so I already forgot
> about them. Which might give you a glimps,
> 
> why WebGPU was used for this here:
> 
> 11.4 Giga Lips with a Budget Laptop
> https://github.com/Jean-Luc-Picard-2021/gigabudget
> 
> Is an interesting choice. Even
> github has some Languages statistics,
> giving an account what I used:
> 
> HTML 67.5% JavaScript 23.1% CSS 9.4%
> 
> Have Fun!
> 
> Bye
> 
> P.S.: The example below is not p-adics,
> you complete imbecil moron. Its just:
> 
> 7-11 cubic Solution by Pritchard & Gries
> https://www.cs.cornell.edu/gries/TechReports/83-574.pdf
> 
> Ross Finlayson schrieb:
>> On 07/26/2026 10:52 AM, Mild Shock wrote:
>>> Hi,
>>>
>>> You see it all boils down to find your inner peace
>>> by an immaculate inception of some queue datatype.
>>>
>>> KOAN/Fortran-S was an early 1990s research programming
>>> system for distributed-memory multiprocessors . Developed
>>> at ENS Lyon in the early 1990s . Often listed alongside
>>> other historical parallel programming efforts.
>>>
>>> The Message Passing: The research explicitly
>>> compared the SVM approach against message passing
>>> on the same hardware . The finding was that SVM
>>> could achieve good performance without the low-level
>>>
>>> complexity of managing explicit messages, though
>>> the best results often came from a hybrid approach (sic!)
>>> Here is an interesting baseline, from Java,
>>> a class ElevenSingle that only does:
>>>
>>>      public static void run() {
>>>          for (int A = 1; A < 192; A++) {
>>>              int Y = (771-A)/3;
>>>              for (int B = A; B < Y; B++) {
>>>                  int Z = (771-A-B)/2;
>>>                  for (int C = B; C < Z; C++) {
>>>                      int D = 711-A-B-C;
>>>                      if (A*B*C == 711000000/D &&
>>>                            711000000 % D == 0)
>>>      System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
>>>                  }
>>>              }
>>>          }
>>>      }
>>>
>>> And then compare it to ElevenMulti, doing some
>>> Work Balancing Scheduler Tetris Game with 8 cores:
>>>
>>> ElevenSingle
>>> A=120, B=125, C=150, D=316
>>> 6.628 ms
>>>
>>> ElevenMulti
>>> A=120, B=125, C=150, D=316
>>> 1.941 ms
>>>
>>> Not great, not terrible!
>>>
>>> Bye
>>
>> Oh, that's just "tricks of p-adic arithmetic".
>>
>> Like other sock-puppet howler trolls, when confronted
>> with its base incredulity, it will descend to its
>> lower levers of the pathos variety.
>>
>> You might be happier learning about Julia trees and
>> raster ops, instead of shilling yet another Ramanujan
>> series without saying how it's made.
>>
>> Bulgarians, that's some real Boris and Natasha crap,
>> forget Hungarians and Bulgarians.
>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#671684 — Andrej Karpathy original gangster of Budget Laptop (Was: miniTriton CUDA is an alternative to torch variants)

Hi,

Andrej Karpathy was bascially the original gangster
of doing not only AI inferencing but also AI
learning on a Budget Laptop. The nanoGPT project

states the following:

"I only have a macbook (or other cheap
computer). No worries, we can still train a
GPT but we want to dial things down a notch.
I recommend getting the bleeding edge PyTorch
nightly (select it here when installing) as
it is currently quite likely to make your
code more efficient."
https://github.com/karpathy/nanogpt

But meanwhile he has moved to a higher price
segment. Not sure whether he will climbe
down to a lower price segment again:

For example, you can train your own GPT-2
capability LLM (which cost ~$43,000 to train in
2019) for only $48 (~2 hours of 8XH100 GPU node)
and then talk to it over a simple CLI. On a spot
instance, the total cost can be closer to ~$15.
https://github.com/karpathy/nanochat

Bt he taps into the model to rent GPU which
is available with prices in the range of 1-2 $
per hour. Even in Switzerland one can do that,

for example using the provider Exoscale. Since
he rents a cluster of 8 cards of type H100, this
explains his training price still in the 2 digit range.

Bye

P.S.: I could also do my experiment here with
rented GPU cards, and then draw a comparison
from budget laptop to the rented GPU time market:

11.4 Giga Lips with a Budget Laptop
https://github.com/Jean-Luc-Picard-2021/gigabudget

But testing rented GPU is not high priority.

Mild Shock schrieb:
> Hi,
> 
> Some counter PyTorch Python trends are
> for example OpenAIs Triton. And the variant
> miniTriton CUDA vibe produced by Kimi K3 (sic!):
> 
> "We further tested whether Kimi K3 could build
> a GPU programming system from scratch. Kimi K3
> developed MiniTriton, a compact Triton-like
> compiler with its own tile-level IR layer over
> MLIR, optimization passes, and a PTX code-
> generation pipeline.
> 
> Across supported roofline benchmarks, MiniTriton
> delivers performance on par with or better than
> Triton and torch.compile — beating Triton on
> certain workloads. Beyond microbenchmarks,
> MiniTriton sustains end-to-end nanoGPT training
> with stable convergence, the loss curve
> 
> closely tracking the reference with only minor
> divergence — validating the full pipeline on a
> realistic workload. These results demonstrate
> that Kimi K3 can build a coherent end-to-end
> compiler — from DSL frontend and IR passes to
> PTX codegen and runtime — rather than isolated
> 
> kernels; its from-scratch Tensor Core path
> already rivals Triton’s extensively optimized stack."
> 
> GPU Compiler Development
> https://www.kimi.com/blog/kimi-k3
> 
> Although many GPU corporate stuff is anonymized,
> and some AI papers have lists of 30 authors. Here
> nanoGPT is mentioned which is tied to the name
> 
> Andrej Karpathy. See also here:
> 
> Update Nov 2025 nanoGPT has a new and
> improved cousin called nanochat.
> https://github.com/karpathy/nanogpt
> 
> But as can be seen, he moved on to another project.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Whats this "forget" trope of glue sniffing
>> Rossy Boy with his herpes blisters?
>>
>>  > Bulgarians, that's some real Boris and Natasha crap,
>>  > forget Hungarians and Bulgarians.
>>
>> Why should I forget Bulgarians,
>> they are never on my mind. Do you
>> see me doing ggml stuff?
>>
>> I only hypothesized that it is
>> over for Python as the machine
>> learning language or AI inferencing
>>
>> locally on AI laptops language, and
>> made the ggml case, so I already forgot
>> about them. Which might give you a glimps,
>>
>> why WebGPU was used for this here:
>>
>> 11.4 Giga Lips with a Budget Laptop
>> https://github.com/Jean-Luc-Picard-2021/gigabudget
>>
>> Is an interesting choice. Even
>> github has some Languages statistics,
>> giving an account what I used:
>>
>> HTML 67.5% JavaScript 23.1% CSS 9.4%
>>
>> Have Fun!
>>
>> Bye
>>
>> P.S.: The example below is not p-adics,
>> you complete imbecil moron. Its just:
>>
>> 7-11 cubic Solution by Pritchard & Gries
>> https://www.cs.cornell.edu/gries/TechReports/83-574.pdf
>>
>> Ross Finlayson schrieb:
>>> On 07/26/2026 10:52 AM, Mild Shock wrote:
>>>> Hi,
>>>>
>>>> You see it all boils down to find your inner peace
>>>> by an immaculate inception of some queue datatype.
>>>>
>>>> KOAN/Fortran-S was an early 1990s research programming
>>>> system for distributed-memory multiprocessors . Developed
>>>> at ENS Lyon in the early 1990s . Often listed alongside
>>>> other historical parallel programming efforts.
>>>>
>>>> The Message Passing: The research explicitly
>>>> compared the SVM approach against message passing
>>>> on the same hardware . The finding was that SVM
>>>> could achieve good performance without the low-level
>>>>
>>>> complexity of managing explicit messages, though
>>>> the best results often came from a hybrid approach (sic!)
>>>> Here is an interesting baseline, from Java,
>>>> a class ElevenSingle that only does:
>>>>
>>>>      public static void run() {
>>>>          for (int A = 1; A < 192; A++) {
>>>>              int Y = (771-A)/3;
>>>>              for (int B = A; B < Y; B++) {
>>>>                  int Z = (771-A-B)/2;
>>>>                  for (int C = B; C < Z; C++) {
>>>>                      int D = 711-A-B-C;
>>>>                      if (A*B*C == 711000000/D &&
>>>>                            711000000 % D == 0)
>>>>      System.out.println("A="+A+", B="+B+", C="+C+", D="+D);
>>>>                  }
>>>>              }
>>>>          }
>>>>      }
>>>>
>>>> And then compare it to ElevenMulti, doing some
>>>> Work Balancing Scheduler Tetris Game with 8 cores:
>>>>
>>>> ElevenSingle
>>>> A=120, B=125, C=150, D=316
>>>> 6.628 ms
>>>>
>>>> ElevenMulti
>>>> A=120, B=125, C=150, D=316
>>>> 1.941 ms
>>>>
>>>> Not great, not terrible!
>>>>
>>>> Bye
>>>
>>> Oh, that's just "tricks of p-adic arithmetic".
>>>
>>> Like other sock-puppet howler trolls, when confronted
>>> with its base incredulity, it will descend to its
>>> lower levers of the pathos variety.
>>>
>>> You might be happier learning about Julia trees and
>>> raster ops, instead of shilling yet another Ramanujan
>>> series without saying how it's made.
>>>
>>> Bulgarians, that's some real Boris and Natasha crap,
>>> forget Hungarians and Bulgarians.
>>>
>>>
>>
> 

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