#blas — Public Fediverse posts

Микроядро SME2 sgemm: 1024 умножения-сложения за проход

В этой части мы возьмём первый из пропусков BLIS — микроядро sgemm ; сначала зафиксируем форму аккумулятора 32×32 в четырёх тайлах ZA, затем разберём горячий цикл по K, потом эпилог с alpha , beta и ловушкой NaN, а в конце посмотрим, какие числа производительности даёт именно это ядро. Следующая часть оставит структуру почти той же, но заменит геометрию тайла и условия существования ядра.

https://habr.com/ru/articles/1071462/

#BLIS #GEMM #SME2 #Apple_Silicon #микроядро #кэшблокинг #упаковка_данных #высокопроизводительные_вычисления #линейная_алгебра #BLAS

Микроядро SME2 sgemm: 1024 умножения-сложения за проход

В этой части мы возьмём первый из пропусков BLIS — микроядро sgemm ; сначала зафиксируем форму аккумулятора 32×32 в четырёх тайлах ZA, затем разберём горячий цикл по K, потом эпилог с alpha , beta и ловушкой NaN, а в конце посмотрим, какие числа производительности даёт именно это ядро. Следующая часть оставит структуру почти той же, но заменит геометрию тайла и условия существования ядра.

https://habr.com/ru/articles/1071462/

#BLIS #GEMM #SME2 #Apple_Silicon #микроядро #кэшблокинг #упаковка_данных #высокопроизводительные_вычисления #линейная_алгебра #BLAS

Микроядро SME2 sgemm: 1024 умножения-сложения за проход

В этой части мы возьмём первый из пропусков BLIS — микроядро sgemm ; сначала зафиксируем форму аккумулятора 32×32 в четырёх тайлах ZA, затем разберём горячий цикл по K, потом эпилог с alpha , beta и ловушкой NaN, а в конце посмотрим, какие числа производительности даёт именно это ядро. Следующая часть оставит структуру почти той же, но заменит геометрию тайла и условия существования ядра.

https://habr.com/ru/articles/1071462/

#BLIS #GEMM #SME2 #Apple_Silicon #микроядро #кэшблокинг #упаковка_данных #высокопроизводительные_вычисления #линейная_алгебра #BLAS

https://www.europesays.com/es/714791/ Blas Latorre, entrenador de Isabel Preysler: «Llego a las tres de la tarde y en ese momento el servicio la despierta y le va preparando el desayuno» #blas #Celebrities #Desayuno #despierta #Entertainment #entrenador #Entretenimiento #ES #España #Famosos #isabel #latorre #llego #momento #preparando #preysler #servicio #Spain #tarde #tres

BLIS: недостающую среднюю ступеньку построили тридцать лет назад

В этой части мы начнём с тупика, в который приводит голый цикл FMOPA ; затем покажем, какую часть GEMM BLIS уже построил за нас; после этого разберём пять циклов BLIS и место микроядра внутри них. Финал главы должен сделать дальнейший план конкретным: что именно нужно добавить для Apple SME, а что уже относится к готовой «мебели» фреймворка.

https://habr.com/ru/articles/1069058/

#BLIS #GEMM #SME2 #Apple_Silicon #микроядро #кэшблокинг #упаковка_данных #высокопроизводительные_вычисления #линейная_алгебра #BLAS

BLIS: недостающую среднюю ступеньку построили тридцать лет назад

В этой части мы начнём с тупика, в который приводит голый цикл FMOPA ; затем покажем, какую часть GEMM BLIS уже построил за нас; после этого разберём пять циклов BLIS и место микроядра внутри них. Финал главы должен сделать дальнейший план конкретным: что именно нужно добавить для Apple SME, а что уже относится к готовой «мебели» фреймворка.

https://habr.com/ru/articles/1069058/

#BLIS #GEMM #SME2 #Apple_Silicon #микроядро #кэшблокинг #упаковка_данных #высокопроизводительные_вычисления #линейная_алгебра #BLAS

BLIS: недостающую среднюю ступеньку построили тридцать лет назад

В этой части мы начнём с тупика, в который приводит голый цикл FMOPA ; затем покажем, какую часть GEMM BLIS уже построил за нас; после этого разберём пять циклов BLIS и место микроядра внутри них. Финал главы должен сделать дальнейший план конкретным: что именно нужно добавить для Apple SME, а что уже относится к готовой «мебели» фреймворка.

https://habr.com/ru/articles/1069058/

#BLIS #GEMM #SME2 #Apple_Silicon #микроядро #кэшблокинг #упаковка_данных #высокопроизводительные_вычисления #линейная_алгебра #BLAS

https://www.europesays.com/es/689295/ De San Blas a MVP de un Mundial: «¿A quién no le gusta la WNBA?» #Baloncesto #Basketball #blas #Deportes #ES #España #le #mundial #mvp #san #SanBlas #Spain #Sports

Re-reading the old BLAS level 3 and #FLAME papers about microkernels and performance for performant #GEMM implementations in #BLAS, #GotoBLAS and #BLIS. I had forgotten how well written they are!

Re-reading the old BLAS level 3 and #FLAME papers about microkernels and performance for performant #GEMM implementations in #BLAS, #GotoBLAS and #BLIS. I had forgotten how well written they are!

Question for the #stats folks working with #mgcv and #gam / #bam:
I'm dealing with a dataset with a high temporal autocorrelation, so after a lengthy discussion with the author of `gratia` (my current PI), we concluded that I should move away from AR(1) models and rather work with #NCV.
No problems with the setup, but the run time is forever.
My first model has been running for 20h by now, with no indication of how long it will take.
I have already switched over to #OpenBLAS, but it seems weird that my CPU load is constantly at only 15-25%. The only indication that something is happening is that RAM usage is slowly but steadily increasing.
I'm dealing with ~40k data points across ~300 time series, and a NCV window of 7 data points, so nothing crazy. The model includes two- and three-way interactions, and random smooths, but all of those things are biologically relevant.

I'd happily take any suggestions on how to speed things up, how to get proper CPU usage, or at least get an estimate of how far along the calculations are.

I might eventually need to switch over to #twlss, from the current `tw()`, and that will make things worse as that is not supported by `bam()`.

I'm currently on Windows, but I might be able to run things on an #HPC if needed, but not sure if I can easily fiddle with #BLAS there.

Question for the #stats folks working with #mgcv and #gam / #bam:
I'm dealing with a dataset with a high temporal autocorrelation, so after a lengthy discussion with the author of `gratia` (my current PI), we concluded that I should move away from AR(1) models and rather work with #NCV.
No problems with the setup, but the run time is forever.
My first model has been running for 20h by now, with no indication of how long it will take.
I have already switched over to #OpenBLAS, but it seems weird that my CPU load is constantly at only 15-25%. The only indication that something is happening is that RAM usage is slowly but steadily increasing.
I'm dealing with ~40k data points across ~300 time series, and a NCV window of 7 data points, so nothing crazy. The model includes two- and three-way interactions, and random smooths, but all of those things are biologically relevant.

I'd happily take any suggestions on how to speed things up, how to get proper CPU usage, or at least get an estimate of how far along the calculations are.

I might eventually need to switch over to #twlss, from the current `tw()`, and that will make things worse as that is not supported by `bam()`.

I'm currently on Windows, but I might be able to run things on an #HPC if needed, but not sure if I can easily fiddle with #BLAS there.

Question for the #stats folks working with #mgcv and #gam / #bam:
I'm dealing with a dataset with a high temporal autocorrelation, so after a lengthy discussion with the author of `gratia` (my current PI), we concluded that I should move away from AR(1) models and rather work with #NCV.
No problems with the setup, but the run time is forever.
My first model has been running for 20h by now, with no indication of how long it will take.
I have already switched over to #OpenBLAS, but it seems weird that my CPU load is constantly at only 15-25%. The only indication that something is happening is that RAM usage is slowly but steadily increasing.
I'm dealing with ~40k data points across ~300 time series, and a NCV window of 7 data points, so nothing crazy. The model includes two- and three-way interactions, and random smooths, but all of those things are biologically relevant.

I'd happily take any suggestions on how to speed things up, how to get proper CPU usage, or at least get an estimate of how far along the calculations are.

I might eventually need to switch over to #twlss, from the current `tw()`, and that will make things worse as that is not supported by `bam()`.

I'm currently on Windows, but I might be able to run things on an #HPC if needed, but not sure if I can easily fiddle with #BLAS there.

Question for the #stats folks working with #mgcv and #gam / #bam:
I'm dealing with a dataset with a high temporal autocorrelation, so after a lengthy discussion with the author of `gratia` (my current PI), we concluded that I should move away from AR(1) models and rather work with #NCV.
No problems with the setup, but the run time is forever.
My first model has been running for 20h by now, with no indication of how long it will take.
I have already switched over to #OpenBLAS, but it seems weird that my CPU load is constantly at only 15-25%. The only indication that something is happening is that RAM usage is slowly but steadily increasing.
I'm dealing with ~40k data points across ~300 time series, and a NCV window of 7 data points, so nothing crazy. The model includes two- and three-way interactions, and random smooths, but all of those things are biologically relevant.

I'd happily take any suggestions on how to speed things up, how to get proper CPU usage, or at least get an estimate of how far along the calculations are.

I might eventually need to switch over to #twlss, from the current `tw()`, and that will make things worse as that is not supported by `bam()`.

I'm currently on Windows, but I might be able to run things on an #HPC if needed, but not sure if I can easily fiddle with #BLAS there.

Did you recently update your #rstats to 4.6? Is your matrix algebra slow? Not #BLAS ting? `sessionInfo()` shows which BLAS library R uses. Switching from reference BLAS to Apple Accelerate is easily a 50x speedup.

Let me know if this worked for you. 🚀

Post: uatz.org/blog/2026-06-04-r-blas-macos/ #rstats

Did you recently update your #rstats to 4.6? Is your matrix algebra slow? Not #BLAS ting? `sessionInfo()` shows which BLAS library R uses. Switching from reference BLAS to Apple Accelerate is easily a 50x speedup.

Let me know if this worked for you. 🚀

Post: uatz.org/blog/2026-06-04-r-blas-macos/ #rstats

Did you recently update your #rstats to 4.6? Is your matrix algebra slow? Not #BLAS ting? `sessionInfo()` shows which BLAS library R uses. Switching from reference BLAS to Apple Accelerate is easily a 50x speedup.

Let me know if this worked for you. 🚀

Post: uatz.org/blog/2026-06-04-r-blas-macos/ #rstats

Did you recently update your #rstats to 4.6? Is your matrix algebra slow? Not #BLAS ting? `sessionInfo()` shows which BLAS library R uses. Switching from reference BLAS to Apple Accelerate is easily a 50x speedup.

Let me know if this worked for you. 🚀

Post: uatz.org/blog/2026-06-04-r-blas-macos/ #rstats

Did you recently update your #rstats to 4.6? Is your matrix algebra slow? Not #BLAS ting? `sessionInfo()` shows which BLAS library R uses. Switching from reference BLAS to Apple Accelerate is easily a 50x speedup.

Let me know if this worked for you. 🚀

Post: uatz.org/blog/2026-06-04-r-blas-macos/ #rstats

#SAN #BLAS #ASUNCIÓN multi-search-tag-explorer.aepiot.ro/advanced-sea... #JAMES #LEIGH #JOYNES #CLERGYMAN www.perplexity.ai/search/new?q... nopzon.com/895e7387/glo... aePiot: Be the first in Web 4.0. Build nodes, dominate SEO.

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#SAN #BLAS #ASUNCIÓN multi-search-tag-explorer.aepiot.ro/advanced-sea... #JAMES #LEIGH #JOYNES #CLERGYMAN www.perplexity.ai/search/new?q... nopzon.com/895e7387/glo... aePiot: Be the first in Web 4.0. Build nodes, dominate SEO.

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#Copilot and I are about 30% away from creating a #Pascal version of #LAPACK using #BLAS. We are about two days away from achieving 80% of LAPACK. Then we will tweak it using some GPU acceleration to make its speed comparable to some python libraries like Numpy.

It is important to note that one must be very disciplined in keeping clean documentations, a thorough and tight testing cycle, a rigid workflow pattern, or an AI will tend to skip tests, become sloppy and lose focus.

#AI #LLM

#Copilot and I are about 30% away from creating a #Pascal version of #LAPACK using #BLAS. We are about two days away from achieving 80% of LAPACK. Then we will tweak it using some GPU acceleration to make its speed comparable to some python libraries like Numpy.

It is important to note that one must be very disciplined in keeping clean documentations, a thorough and tight testing cycle, a rigid workflow pattern, or an AI will tend to skip tests, become sloppy and lose focus.

#AI #LLM

#Copilot and I are about 30% away from creating a #Pascal version of #LAPACK using #BLAS. We are about two days away from achieving 80% of LAPACK. Then we will tweak it using some GPU acceleration to make its speed comparable to some python libraries like Numpy.

It is important to note that one must be very disciplined in keeping clean documentations, a thorough and tight testing cycle, a rigid workflow pattern, or an AI will tend to skip tests, become sloppy and lose focus.

#AI #LLM

#Copilot and I are about 30% away from creating a #Pascal version of #LAPACK using #BLAS. We are about two days away from achieving 80% of LAPACK. Then we will tweak it using some GPU acceleration to make its speed comparable to some python libraries like Numpy.

It is important to note that one must be very disciplined in keeping clean documentations, a thorough and tight testing cycle, a rigid workflow pattern, or an AI will tend to skip tests, become sloppy and lose focus.

#AI #LLM

#AI illiteracy is real. While still arguing with a bunch of AI haters, #Copilot and I just finished our #Pascal #BLAS level 1-3 Implementation plus eigenvalue, cholesky, and sparse #matrix, so we will never need #python, #C, C#, #Rust, ... for our Small Language Project. We will expand our Pascal Numeric Library (PNL) v1.0 to something like #Numpy and #Pytorch, but with static arrays, deterministic data structure, no referencing, no pointer arithmetic.

#LLM #programming #computer

#AI illiteracy is real. While still arguing with a bunch of AI haters, #Copilot and I just finished our #Pascal #BLAS level 1-3 Implementation plus eigenvalue, cholesky, and sparse #matrix, so we will never need #python, #C, C#, #Rust, ... for our Small Language Project. We will expand our Pascal Numeric Library (PNL) v1.0 to something like #Numpy and #Pytorch, but with static arrays, deterministic data structure, no referencing, no pointer arithmetic.

#LLM #programming #computer

#AI illiteracy is real. While still arguing with a bunch of AI haters, #Copilot and I just finished our #Pascal #BLAS level 1-3 Implementation plus eigenvalue, cholesky, and sparse #matrix, so we will never need #python, #C, C#, #Rust, ... for our Small Language Project. We will expand our Pascal Numeric Library (PNL) v1.0 to something like #Numpy and #Pytorch, but with static arrays, deterministic data structure, no referencing, no pointer arithmetic.

#LLM #programming #computer

#AI illiteracy is real. While still arguing with a bunch of AI haters, #Copilot and I just finished our #Pascal #BLAS level 1-3 Implementation plus eigenvalue, cholesky, and sparse #matrix, so we will never need #python, #C, C#, #Rust, ... for our Small Language Project. We will expand our Pascal Numeric Library (PNL) v1.0 to something like #Numpy and #Pytorch, but with static arrays, deterministic data structure, no referencing, no pointer arithmetic.

#LLM #programming #computer

While arguing with some AI haters, #Copilot and I created this Pure #Pascal #BLAS (Level 1,2,3 Core) Implementation in less than 1 day. We encountered many serious problems, including drifting of workflow pattern, getting stuck in a Delphi error loop, overhauling our original design... But as long as you understand AI, keep good documentations, maintain the core structure of the problem,.. you will be able to work with AI successfully. Don't hesitate to use more than one #AI at a time.

#LLM

While arguing with some AI haters, #Copilot and I created this Pure #Pascal #BLAS (Level 1,2,3 Core) Implementation in less than 1 day. We encountered many serious problems, including drifting of workflow pattern, getting stuck in a Delphi error loop, overhauling our original design... But as long as you understand AI, keep good documentations, maintain the core structure of the problem,.. you will be able to work with AI successfully. Don't hesitate to use more than one #AI at a time.

#LLM

While arguing with some AI haters, #Copilot and I created this Pure #Pascal #BLAS (Level 1,2,3 Core) Implementation in less than 1 day. We encountered many serious problems, including drifting of workflow pattern, getting stuck in a Delphi error loop, overhauling our original design... But as long as you understand AI, keep good documentations, maintain the core structure of the problem,.. you will be able to work with AI successfully. Don't hesitate to use more than one #AI at a time.

#LLM

While arguing with some AI haters, #Copilot and I created this Pure #Pascal #BLAS (Level 1,2,3 Core) Implementation in less than 1 day. We encountered many serious problems, including drifting of workflow pattern, getting stuck in a Delphi error loop, overhauling our original design... But as long as you understand AI, keep good documentations, maintain the core structure of the problem,.. you will be able to work with AI successfully. Don't hesitate to use more than one #AI at a time.

#LLM

What is #BLAS?

BLAS is a set of fast matrix routines originally written in #Fortran.
If you’re tired of dynamic types, hidden references, ownership rules, and endless “stream” abstractions, Free #Pascal + BLAS gives you old‑school, deterministic HPC #programming with none of the modern noise.

#Copilot and I will be using Free Pascal and BLAS for our Small Language Model project #SLM. No more #C, #python, #Rust, or C#

#AI #LLM #computer

What is #BLAS?

BLAS is a set of fast matrix routines originally written in #Fortran.
If you’re tired of dynamic types, hidden references, ownership rules, and endless “stream” abstractions, Free #Pascal + BLAS gives you old‑school, deterministic HPC #programming with none of the modern noise.

#Copilot and I will be using Free Pascal and BLAS for our Small Language Model project #SLM. No more #C, #python, #Rust, or C#

#AI #LLM #computer

What is #BLAS?

BLAS is a set of fast matrix routines originally written in #Fortran.
If you’re tired of dynamic types, hidden references, ownership rules, and endless “stream” abstractions, Free #Pascal + BLAS gives you old‑school, deterministic HPC #programming with none of the modern noise.

#Copilot and I will be using Free Pascal and BLAS for our Small Language Model project #SLM. No more #C, #python, #Rust, or C#

#AI #LLM #computer

What is #BLAS?

BLAS is a set of fast matrix routines originally written in #Fortran.
If you’re tired of dynamic types, hidden references, ownership rules, and endless “stream” abstractions, Free #Pascal + BLAS gives you old‑school, deterministic HPC #programming with none of the modern noise.

#Copilot and I will be using Free Pascal and BLAS for our Small Language Model project #SLM. No more #C, #python, #Rust, or C#

#AI #LLM #computer

Why do people use #python, a glue language, which is so slow? The only reason is the AI ecosystem.

#Copilot and I just tested Free Pascal and BLAS for its speed without using #numpy or #pytorch. The result is amazing. It took less than a second to do a 1024x1024 #matrix multiplication.

We will be using Free #Pascal and #BLAS to write our Small Language Model #SLM using #NNUE.

#AI #LLM

Why do people use #python, a glue language, which is so slow? The only reason is the AI ecosystem.

#Copilot and I just tested Free Pascal and BLAS for its speed without using #numpy or #pytorch. The result is amazing. It took less than a second to do a 1024x1024 #matrix multiplication.

We will be using Free #Pascal and #BLAS to write our Small Language Model #SLM using #NNUE.

#AI #LLM

Why do people use #python, a glue language, which is so slow? The only reason is the AI ecosystem.

#Copilot and I just tested Free Pascal and BLAS for its speed without using #numpy or #pytorch. The result is amazing. It took less than a second to do a 1024x1024 #matrix multiplication.

We will be using Free #Pascal and #BLAS to write our Small Language Model #SLM using #NNUE.

#AI #LLM

Why do people use #python, a glue language, which is so slow? The only reason is the AI ecosystem.

#Copilot and I just tested Free Pascal and BLAS for its speed without using #numpy or #pytorch. The result is amazing. It took less than a second to do a 1024x1024 #matrix multiplication.

We will be using Free #Pascal and #BLAS to write our Small Language Model #SLM using #NNUE.

#AI #LLM

The plot thickens #BLAS #rstats #lapack
(When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement)
FlexiBLAS makes this benchmarks a breeze

The plot thickens #BLAS #rstats #lapack
(When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement)
FlexiBLAS makes this benchmarks a breeze

The plot thickens #BLAS #rstats #lapack
(When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement)
FlexiBLAS makes this benchmarks a breeze

The plot thickens #BLAS #rstats #lapack
(When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement)
FlexiBLAS makes this benchmarks a breeze

The plot thickens #BLAS #rstats #lapack
(When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement)
FlexiBLAS makes this benchmarks a breeze

The plot thickens #BLAS #rstats #lapack (When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement) FlexiBLAS makes this benchmarks a breeze

The plot thickens #BLAS #rstats #lapack (When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement) FlexiBLAS makes this benchmarks a breeze

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats. The things I have to do for the love of electronic health records analytics #bigdata #blas

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats. The things I have to do for the love of electronic health records analytics #bigdata #blas

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats.
The things I have to do for the love of electronic health records analytics #bigdata #blas

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats.
The things I have to do for the love of electronic health records analytics #bigdata #blas

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats.
The things I have to do for the love of electronic health records analytics #bigdata #blas

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats.
The things I have to do for the love of electronic health records analytics #bigdata #blas

I wonder if the #lapack that comes with #AOCL is being picked up by flexiblas in #rstats.
The things I have to do for the love of electronic health records analytics #bigdata #blas

Por san #Blas, la #cigüeña verás.

#cigüeñablanca #ciconiaciconia #fedipajareo #ColmenarViejo

Por san #Blas, la #cigüeña verás.

#cigüeñablanca #ciconiaciconia #fedipajareo #ColmenarViejo

Por san #Blas, la #cigüeña verás.

#cigüeñablanca #ciconiaciconia #fedipajareo #ColmenarViejo

Por san #Blas, la #cigüeña verás.

#cigüeñablanca #ciconiaciconia #fedipajareo #ColmenarViejo

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