#blas — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #blas, aggregated by home.social.
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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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#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.
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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.
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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.
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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.
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#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.
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#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.
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#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.
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#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.
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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.
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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.
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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.
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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.
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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#
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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#
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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#
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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#
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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.
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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.
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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.
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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.
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Ultra-efficient AS Monaco return to winning ways to get back on track against Rennes https://www.byteseu.com/1762956/ #AnsuFati #ASMonaco #BayernMunich #Blas #Brassier #DenisZakaria #Embolo #FolarinBalogun #Frankowski #Golovin #Ilenikhena #Jacquet #Juventus #Kamara #KassoumOuattara #Kehrer #KrépinDiatta #Lepaul #Ligue1 #lyon #MaghnesAkliouche #MamadouCoulibaly #MikaBiereth #Monaco #Nantes #PhilippKöhn #Rennes #SébastienPocognoli #Seidu #StadeRennais #StanisIdumbo #szymanski #Teze #Vanderson
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Developing a BLAS Library for the AMD AI Engine [pdf]
https://uni.tlaan.nl/thesis/msc_thesis_tristan_laan_aieblas.pdf
#HackerNews #Developing #BLAS #AMD #AI #Engine #AI #Library #PDF #Research
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https://www.europesays.com/es/297675/ Pase de revista a la escultura de Blas de Lezo en Cádiz #arte #ArteYDiseño #Arts #ArtsAndDesign #blas #cadiz #Design #Diseño #Entertainment #Entretenimiento #ES #escultura #España #lezo #pase #revista #Spain
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https://www.europesays.com/es/202080/ Nach: «A veces me siento en la grada de la cancha de San Blas y me veo con 13 años jugando al baloncesto» #alicante #anos #Baloncesto #blas #Cancha #destino #Entertainment #Entretenimiento #EntrevistaNachAlicante #ES #España #grada #HipHopEspanol #jugando #Music #Música #MúsicaUrbana #nach #NuevoDiscoNach #Rap #san #SanBlas #siento #Spain #veces #veo
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Another post on #Quansight PBC blog: "BLAS/LAPACK #packaging"
https://labs.quansight.org/blog/blas-lapack-packaging
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#BLAS and #LAPACK are the standard libraries for linear algebra. The original implementation, often called Netlib LAPACK, developed since the 1980s, nowadays serves primarily as the origin of the standard interface, the reference implementation and a conformance test suite. The end users usually use optimized implementations of the same interfaces. The choice ranges from generically tuned libraries such as OpenBLAS and BLIS, through libraries focused on specific hardware such as Intel® oneMKL, Arm Performance Libraries or the Accelerate framework on macOS, to ATLAS that aims to automatically optimize for a specific system.The diversity of available libraries, developed in parallel with the standard interfaces, along with vendor-specific extensions and further downstream changes, adds quite a bit of complexity around using these libraries in software, and distributing such software afterwards. This problem entangles implementation authors, consumer software authors, build system maintainers and distribution maintainers. Software authors generally wish to distribute their packages built against a generically optimized BLAS/LAPACK implementation. Advanced users often wish to be able to use a different implementation, more suited to their particular needs. Distributions wish to be able to consistently build software against their system libraries, and ideally provide users the ability to switch between different implementations. Then, build systems need to provide the scaffolding for all of that.
I have recently taken up the work to provide such a scaffolding for the Meson build system; to add support for BLAS and LAPACK dependencies to Meson. While working on it, I had to learn a lot about BLAS/LAPACK packaging: not only how the different implementations differ from one another, but also what is changed by their respective downstream packaging. In this blog post, I would like to organize and share what I have learned.
""" -
Another post on #Quansight PBC blog: "BLAS/LAPACK #packaging"
https://labs.quansight.org/blog/blas-lapack-packaging
"""
#BLAS and #LAPACK are the standard libraries for linear algebra. The original implementation, often called Netlib LAPACK, developed since the 1980s, nowadays serves primarily as the origin of the standard interface, the reference implementation and a conformance test suite. The end users usually use optimized implementations of the same interfaces. The choice ranges from generically tuned libraries such as OpenBLAS and BLIS, through libraries focused on specific hardware such as Intel® oneMKL, Arm Performance Libraries or the Accelerate framework on macOS, to ATLAS that aims to automatically optimize for a specific system.The diversity of available libraries, developed in parallel with the standard interfaces, along with vendor-specific extensions and further downstream changes, adds quite a bit of complexity around using these libraries in software, and distributing such software afterwards. This problem entangles implementation authors, consumer software authors, build system maintainers and distribution maintainers. Software authors generally wish to distribute their packages built against a generically optimized BLAS/LAPACK implementation. Advanced users often wish to be able to use a different implementation, more suited to their particular needs. Distributions wish to be able to consistently build software against their system libraries, and ideally provide users the ability to switch between different implementations. Then, build systems need to provide the scaffolding for all of that.
I have recently taken up the work to provide such a scaffolding for the Meson build system; to add support for BLAS and LAPACK dependencies to Meson. While working on it, I had to learn a lot about BLAS/LAPACK packaging: not only how the different implementations differ from one another, but also what is changed by their respective downstream packaging. In this blog post, I would like to organize and share what I have learned.
""" -
Another post on #Quansight PBC blog: "BLAS/LAPACK #packaging"
https://labs.quansight.org/blog/blas-lapack-packaging
"""
#BLAS and #LAPACK are the standard libraries for linear algebra. The original implementation, often called Netlib LAPACK, developed since the 1980s, nowadays serves primarily as the origin of the standard interface, the reference implementation and a conformance test suite. The end users usually use optimized implementations of the same interfaces. The choice ranges from generically tuned libraries such as OpenBLAS and BLIS, through libraries focused on specific hardware such as Intel® oneMKL, Arm Performance Libraries or the Accelerate framework on macOS, to ATLAS that aims to automatically optimize for a specific system.The diversity of available libraries, developed in parallel with the standard interfaces, along with vendor-specific extensions and further downstream changes, adds quite a bit of complexity around using these libraries in software, and distributing such software afterwards. This problem entangles implementation authors, consumer software authors, build system maintainers and distribution maintainers. Software authors generally wish to distribute their packages built against a generically optimized BLAS/LAPACK implementation. Advanced users often wish to be able to use a different implementation, more suited to their particular needs. Distributions wish to be able to consistently build software against their system libraries, and ideally provide users the ability to switch between different implementations. Then, build systems need to provide the scaffolding for all of that.
I have recently taken up the work to provide such a scaffolding for the Meson build system; to add support for BLAS and LAPACK dependencies to Meson. While working on it, I had to learn a lot about BLAS/LAPACK packaging: not only how the different implementations differ from one another, but also what is changed by their respective downstream packaging. In this blog post, I would like to organize and share what I have learned.
""" -
Another post on #Quansight PBC blog: "BLAS/LAPACK #packaging"
https://labs.quansight.org/blog/blas-lapack-packaging
"""
#BLAS and #LAPACK are the standard libraries for linear algebra. The original implementation, often called Netlib LAPACK, developed since the 1980s, nowadays serves primarily as the origin of the standard interface, the reference implementation and a conformance test suite. The end users usually use optimized implementations of the same interfaces. The choice ranges from generically tuned libraries such as OpenBLAS and BLIS, through libraries focused on specific hardware such as Intel® oneMKL, Arm Performance Libraries or the Accelerate framework on macOS, to ATLAS that aims to automatically optimize for a specific system.The diversity of available libraries, developed in parallel with the standard interfaces, along with vendor-specific extensions and further downstream changes, adds quite a bit of complexity around using these libraries in software, and distributing such software afterwards. This problem entangles implementation authors, consumer software authors, build system maintainers and distribution maintainers. Software authors generally wish to distribute their packages built against a generically optimized BLAS/LAPACK implementation. Advanced users often wish to be able to use a different implementation, more suited to their particular needs. Distributions wish to be able to consistently build software against their system libraries, and ideally provide users the ability to switch between different implementations. Then, build systems need to provide the scaffolding for all of that.
I have recently taken up the work to provide such a scaffolding for the Meson build system; to add support for BLAS and LAPACK dependencies to Meson. While working on it, I had to learn a lot about BLAS/LAPACK packaging: not only how the different implementations differ from one another, but also what is changed by their respective downstream packaging. In this blog post, I would like to organize and share what I have learned.
""" -
Another post on #Quansight PBC blog: "BLAS/LAPACK #packaging"
https://labs.quansight.org/blog/blas-lapack-packaging
"""
#BLAS and #LAPACK are the standard libraries for linear algebra. The original implementation, often called Netlib LAPACK, developed since the 1980s, nowadays serves primarily as the origin of the standard interface, the reference implementation and a conformance test suite. The end users usually use optimized implementations of the same interfaces. The choice ranges from generically tuned libraries such as OpenBLAS and BLIS, through libraries focused on specific hardware such as Intel® oneMKL, Arm Performance Libraries or the Accelerate framework on macOS, to ATLAS that aims to automatically optimize for a specific system.The diversity of available libraries, developed in parallel with the standard interfaces, along with vendor-specific extensions and further downstream changes, adds quite a bit of complexity around using these libraries in software, and distributing such software afterwards. This problem entangles implementation authors, consumer software authors, build system maintainers and distribution maintainers. Software authors generally wish to distribute their packages built against a generically optimized BLAS/LAPACK implementation. Advanced users often wish to be able to use a different implementation, more suited to their particular needs. Distributions wish to be able to consistently build software against their system libraries, and ideally provide users the ability to switch between different implementations. Then, build systems need to provide the scaffolding for all of that.
I have recently taken up the work to provide such a scaffolding for the Meson build system; to add support for BLAS and LAPACK dependencies to Meson. While working on it, I had to learn a lot about BLAS/LAPACK packaging: not only how the different implementations differ from one another, but also what is changed by their respective downstream packaging. In this blog post, I would like to organize and share what I have learned.
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Wspominałem już może, że pracuję nad przejściem #Gentoo z na wpół zepsutego eselect-ldso dla #BLAS / #LAPACK, na #FlexiBLAS. Oznacza to również, że czeka nas okres przejściowy, w czasie którego obydwa rozwiązania będą wspierane.
Plus jest taki, że stan "po" jest kompatybilny pod względem ABI ze stanem "przed" (a przynajmniej powinien być — pracujemy z autorami, by poprawić ostatnie niedociągnięcia). Zastępujemy libblas.so, liblapack.so i inne biblitoteki dowiązaniami symbolicznymi, więc programy skompilowane przed zmianą po prostu zaczną używać FlexiBLAS.
Minus jest taki, że w drugą stronę nie jest tak łatwo. Po zastąpieniu biblitotek dowiązaniami, nowoskompilowane programy będą odczytywać SONAME z biblioteki docelowej, a więc zaczną się wiązać bezpośrednio z FlexiBLAS. Co za tym idzie, powrót do stanu poprzedniego będzie wymagał ich ponownej kompilacji.
Aby tego uniknąć, musielibyśmy zamiast dowiązań symbolicznych zastosować jakieś biblioteki pośredniczące, które miałyby "stare" SONAME, a korzystąły z funkcji FlexiBLAS. Niestety, nic prostego tu nie zadziała — musiałbym jakoś "wyeksportować" symbole z FlexiBLAS, i najlepiej podzielić je na odpowiednie biblioteki, żeby `-Wl,--as-needed` nic nie wycięło. Tylko jak to zrobić?
Cóż, eselect-ldso tworzy jakieś biblioteki, więc może uda się coś wykorzystać. No i szukam w źródłach, i nic nie mogę znaleźć. W końcu do mnie dociera, że cała logika dodana jest przez łatki Gentoo. A te łatki są po prostu paskudne. W OpenBLAS tworzymy dodatkowe biblioteki libblas.so, itp., które zawierają kopie obiektów z OpenBLAS i wiążą się z libopenblas, żeby pobrać brakujące zależności. Nawet nie wiążą się jedna z drugą, więc każda duplikuje sporo kodu niezależnie. Łatki dla BLIS są jeszcze gorsze — tu libblas.so i libcblas.so to praktycznie kopie libblis.so, z poszczególnymi "niepotrzebnymi" symbolami ukrytymi przy pomocy "visibility".
No cóż, można się było tego spodziewać po projekcie z #GSoC.
