#OpenCL

[govindhtech]

AMD Ryzen 7 8700G APU Zen 4 & Polaris Wonders!

AMD Ryzen 7 8700G APU The company formidable main processing unit (APU) with Zen 4 framework and Polaris designs, the AMD Ryzen 7 processor 8700G

The conclusions of the assessments for the Ryzen 5 processor from AMD 8600G had previously revealed this morning, and now some of the most recent measurements from the Ryzen 7 8700G APU graph G have been released made public. Among AMD’s Hawk A point generation of advanced processing units (APUs), the upcoming Ryzen 7 8700G APU will represent the top of the lineup of the The AM5 series desktops APU. That is going to have an identical blend of Zen 4 and RDNA 3 cores in a single monolithic package.

Featuring 16 MB of L3 memory cache and 8 megabytes of L2 cache, the aforementioned AMD Ryzen 7 8700G APU features a total of 8 CPU cores and a total of 16 threads built onto it. It is possible to quicken the clock to 5.10 GHz from its base frequency of 4.20 GHz. A Radeon 780M based on RDNA 3 with 12 compute units and a clock speed of 2.9 GHz is included in the integrated graphics processing unit (GPU). It is anticipated that future Hawk Point APUs would have support for 64GB DDR5 modules, which will allow for a maximum of 256GB of DRAM capacity to be used on the AM5 architecture.

The study ASUS TUF Extreme X670E-PLUS wireless internet chipset with 32GB of DDR5 4800 RAM was used for the performance tests that were carried out. Because of this design, it is anticipated that the performance would be somewhat reduced. The Hawk Point APUs and the AM5 platform are both compatible with faster memory modules, which may lead to improved performance. This is made possible by the greater bandwidth that is advantageous to the integrated graphics processing unit (iGPU).

The AMD Ryzen 7 8700G “Hawk Point” APU was able to reach a performance of 35,427 points in the Vulkan benchmark, while it earned 29,244 points in the OpenCL benchmark. With the Ryzen 5 8600G equipped with the Radeon 760M integrated graphics processing unit, this results in a 15% improvement in Vulkan and an 18% increase in OpenCL. The 760M integrated graphics processing unit (iGPU) has only 8 compute units, but the AMD 780M has 12 compute units.

In spite of the fact that the 760M integrated graphics processing unit (iGPU) has faster DDR5 6000 memory, performance does not seem to rise linearly whenever there are fifty percent more cores. It would seem that this is the maximum performance that the Radeon IGPs are capable of. The results of future testing, particularly those involving overclocking, will be fascinating. However, the Meteor Lake integrated graphics processing units (iGPUs) might be improved with better quality memory configurations (LPDDR5x).

With the debut of the AM5 “Hawk Point” APUs at the end of January, it is anticipated that the RDNA 3 chips would provide increased performance for the integrated graphics processing unit (iGPU). At AMD’s next CES 2024 event, it is anticipated that further details will be discussed and revealed.

Read more on Govindhtech.com

[agapi-kalyptei]

AMD GPU users: Glaze is terribly slow even on a high end CPU (40-60 minutes per 4k (8Mpix) image on 16-core Ryzen 7950X), and doesn't currently (version 1.1.1) work on OpenCL natively. In comparison, for nVidia GPU users, CUDA version should run in 1-3 minutes.

Sadly, currently I can't get it to run using the CUDA emulation, Zluda v3, but it's possible the compatibility will be added in a future version, so keep an eye on it: https://github.com/vosen/ZLUDA/releases/

I have submitted a ticket to the project, so maybe they'll fix it, and maybe it will work with regular desktop drivers (I use Pro drivers 23.Q4).

EDIT: Someone responded to the ticket, but it still crashes on me. But seems like people will make it work sooner or later.

Tumblr is doing some stupid AI shit so go to blog settings > Visibility > Prevent third-party sharing.

[devsnews]

Microsoft recently added support for GPU Video Acceleration by building on top of the existing Mesa 3D D3D12 backend and integrating the VAAPI Mesa frontend. Several Linux media apps use the VAAPI interface to access hardware video acceleration when available, which can now be leveraged in WSLg. Read this article to know more about this feature.

[cerulity]

LANGUAGE(ISH) PROPOSAL

A language that unifies C#, Rust, and CUDA/OpenCL.

Heres why:

C# is a featureful, rich language. There’s so much that the language provides, and so much you can do. It has interfaces, indexers, properties, abstracts, attributes, and more. Where it falls short, however, Rust picks it up. C# variables are not thread-safe by default, and nulls are allowed by default (although the `lock` keyword and `?` suffix do help). There is also no immutability or macros. Rust guarantees a lot with compile-time checking. You know that when a function returns an i32, you WILL get an i32. However, once you get into higher-level code, managing memory safely and efficiently can get painful, and multithreading is a whole other problem. Even if it is safe, Rust gets a bit too eager with it’s management. Having that link between infallible functionality and lenient, intricate structure is good. CUDA/OpenCL is pulled into this because they provide GPU interfacing, which is just nice to have. If you don’t want that, then it’s just C++, which has good memory access.

The ‘language’ part would kinda just be links between the three. FFI can be a problem. C# classes and Rust structs are both different, Rust handles strings differently than C# and C++ (lengthed vs null terminated), and there’s a bit of friction when interfacing between them. The language would simplify the process. You can have “rsstr” and “cstr” and switch between them, or you can just have “str” and convert to it’s native definition (&str, char*, string) when taken as a function parameter or passed through to a function. You can have a “csclass” that can be converted to a “struct” and back.

[kyousystem]

I think I figured out why GNU Backgammon's evaluations have been so stubbornly slow, even despite all of my rewriting, refactoring, and optimizing.

On a whim, I tried turning the "evaluation threads" counter in the options menu all the way down to 1 (from the two dozen or so I had it set at before)... yet the performance / evaluation time was completely identical. I dug a little deeper, and everything I've found thus far has confirmed my suspicion:

The evaluations are all being performed one at a time, in serial.

On one hand, really? Fucking REALLY? I get that this codebase has all the structure and maintainability of a mud puddle, and that the developers are volunteers, but this is egregious!

On the other hand, this will make improving the engine's performance yet further a much simpler task. No need to break out OpenCL if plain ol' threads aren't being properly utilized, heheh.

[scarletfire03]

scarlets linux misadventures episode 1

attempting to install amd gpu drivers and opencl to edit videos

"why cant you find this package my little zenbook"

"you need to install these other 10 things first and then manually install the latest version of amdgpu-install directly from the repo because for some reason amd does not list the latest version that is for ubuntu 24 at all."

"and then it will work?"

👁️👄👁️

[lagtrainzzz]

what's webgl and opencl then

Opengl doesn't stand for "open graphics library." It stands for Openly Gay Lesbians.

[7ooo-ru]

Показана Radxa Orion O6 — первая матплата Armv9 с открытым исходным кодом

Radxa в сотрудничестве с Arm China и CIX представила Orion O6, первую материнскую Armv9 плату Mini-ITX SBC с открытым исходным кодом. Модель стоимостью от 200 долларов за 8 ГБ ОЗУ оснащена распаянной SoC CD8180 с 12 ядрами CPU, включая четыре ядра Cortex A720 с частотой 2,8 ГГц и 30 TOPS NPU. Графика обеспечивается графическим процессором Immortalis G720 от Arm с трассировкой лучей и поддержкой Vulkan, OpenCL и OpenGL.

Подробнее https://7ooo.ru/group/2024/12/23/994-pokazana-radxa-orion-o6-pervaya-matplata-armv9-sotkrytym-ishodnym-kodom-grss-367215038.html

[lacyc3]

Van a Scaleway-nél RISC-V-ös, fizikai gép Alibaba TH-1520-as processzorral (4 mag, 4 szál), 16GB memóriával, 128GB MMC memóriával havi 16 eur + áfa árban. Fut rajta az Ubuntu 24.04:

SoC T-Head 1520CPU (C910) RV64GC 4 cores 1,85 GHz GPU (OpenCL 1.1/1.2/2.0, OpenGL ES 3.0/3.1/3.2, Vulkan 1.1/1.2, Android NN HAL) VPU (H.265/H.264/VP9 video encoding/decoding) NPU (4TOPS@INT8 1GHz, Tensorflow, ONNX, Caffe) Évekkel ezelőtt az ARM-et szerettem, lényegében a teljes oldal egy 2 gyufás doboz méretű fizikai vason futott.

Viszont ehhez nekem most keresni kéne problémát, amit meg kell oldani.. :D

Fele ennyiért ugranék, mint gyöngytyúk a takonyra, de így nem éri meg csak "for-fun" szórakozni egy új architektúrával, ami mellett kéne valami backup is, mert 0% az SLA.

[babyawacs]

does #geekbench patch the gpu driver on online link w h a t the fff is happening the dedicated opencl testrun reinstalls another version of thedriver? isit only a bizzarre exclusivity mode vs driver orisit sinister  #hmmmm @debian .@debian . @linux @swiftonsecurity @wired @wireduk @cnet .@cnet @techpowerup @all @world‎ #handsdown #hopeless #before ? #deciphering #ransomwa re  ifthe encryption uses maximum or lower detectability encryption keysize allows which f i l e size: onsystem as randompick specia l pick andor within network traffic ‎ ‎even shuffling the random key pick files onsystem as key is a nobrainer iwould try :

does #geekbench patch the gpu driver on online link w h a t the fff is happening the dedicated opencl testrun reinstalls another version of thedriver? isit only a bizzarre exclusivity mode vs driver orisit sinister #hmmmm @debian .@debian .@linux @swiftonsecurity @wired @wireduk @cnet .@cnet @techpowerup @all @world #handsdown #hopeless #before ? #deciphering #ransomware ifthe encryption uses…

[govindhtech]

Intel’s oneAPI 2024 Kernel_Compiler Feature Improves LLVM

Kernel_Compiler

The kernel_compiler, which was first released as an experimental feature in the fully SYCL2020 compliant Intel oneAPI DPC++/C++ compiler 2024.1 is one of the new features. Here’s another illustration of how Intel advances the development of LLVM and SYCL standards. With the help of this extension, OpenCL C strings can be compiled at runtime into kernels that can be used on a device.

For offloading target hardware-specific SYCL kernels, it is provided in addition to the more popular modes of Ahead-of-Time (AOT), SYCL runtime, and directed runtime compilation.

Generally speaking, the kernel_compiler extension ought to be saved for last!

Nonetheless, there might be some very intriguing justifications for leveraging this new extension to create SYCL Kernels from OpenCL C or SPIR-V code stubs.

Let’s take a brief overview of the many late- and early-compile choices that SYCL offers before getting into the specifics and explaining why there are typically though not always better techniques.

Three Different Types of Compilation 

The ability to offload computational work to kernels running on another compute device that may be installed on the machine, such as a GPU or an FPGA, is what SYCL offers your application. Are there thousands of numbers you need to figure out? Forward it to the GPU!

Power and performance are made possible by this, but it also raises more questions:

Which device are you planning to target? In the future, will that change?

Could it be more efficient if it were customized to parameters that only the running program would know, or do you know the complete domain parameter value for that kernel execution? SYCL offers a number of choices to answer those queries:

Ahead-of-Time (AoT) Compile: This process involves compiling your kernels to machine code concurrently with the compilation of your application.

SYCL Runtime Compilation: This method compiles the kernel while your application is executing and it is being used.

With directed runtime compilation, you can set up your application to generate a kernel whenever you’d want.

Let’s examine each one of these:

1. Ahead of Time (AoT) Compile

You can also precompile the kernels at the same time as you compile your application. All you have to do is specify which devices you would like the kernels to be compiled for. All you need to do is pass them to the compiler with the -fsycl-targets flag. Completed! Now that the kernels have been compiled, your application will use those binaries.

AoT compilation has the advantage of being easy to grasp and familiar to C++ programmers. Furthermore, it is the only choice for certain devices such as FPGAs and some GPUs.

An additional benefit is that your kernel can be loaded, given to the device, and executed without the runtime stopping to compile it or halt it.

Although they are not covered in this blog post, there are many more choices available to you for controlling AoT compilation. For additional information, see this section on compiler and runtime design or the -fsycl-targets article in Intel’s GitHub LLVM User Manual.

SPIR-V

2. SYCL Runtime Compilation (via SPIR-V) 

If no target devices are supplied or perhaps if an application with precompiled kernels is executed on a machine with target devices that differ from what was requested, this is SYCL default mode.

SYCL automatically compiles your kernel C++ code to SPIR-V (Standard Portable Intermediate form), an intermediate form. When the SPIR-V kernel is initially required, it is first saved within your program and then sent to the driver of the target device that is encountered. The SPIR-V kernel is then converted to machine code for that device by the device driver.

The default runtime compilation has the following two main benefits:

First of all, you don’t have to worry about the precise target device that your kernel will operate on beforehand. It will run as long as there is one.

Second, if a GPU driver has been updated to improve performance, your application will benefit from it when your kernel runs on that GPU using the new driver, saving you the trouble of recompiling it.

However, keep in mind that there can be a minor cost in contrast to AoT because your application will need to compile from SPIR-V to machine code when it first delivers the kernel to the device. However, this usually takes place outside of the key performance route, before parallel_for loops the kernel.

In actuality, this compilation time is minimal, and runtime compilation offers more flexibility than the alternative. SYCL may also cache compiled kernels in between app calls, which further eliminates any expenses. See kernel programming cache and environment variables for additional information on caching.

However, if you prefer the flexibility of runtime compilation but dislike the default SYCL behavior, continue reading!

3. Directed Runtime Compilation (via kernel_bundles) 

You may access and manage the kernels that are bundled with your application using the kernel_bundle class in SYCL, which is a programmatic interface.

Here, the kernel_bundle techniques are noteworthy.build(), compile(), and link(). Without having to wait until the kernel is required, these let you, the app author, decide precisely when and how a kernel might be constructed.

Additional details regarding kernel_bundles are provided in the SYCL 2020 specification and in a controlling compilation example.

Specialization Constants 

Assume for the moment that you are creating a kernel that manipulates an input image’s numerous pixels. Your kernel must use a replacement to replace the pixels that match a specific key color. You are aware that if the key color and replacement color were constants instead of parameter variables, the kernel might operate more quickly. However, there is no way to know what those color values might be when you are creating your program. Perhaps they rely on calculations or user input.

Specialization constants are relevant in this situation.

The name refers to the constants in your kernel that you will specialize in at runtime prior to the kernel being compiled at runtime. Your application can set the key and replacement colors using specialization constants, which the device driver subsequently compiles as constants into the kernel’s code. There are significant performance benefits for kernels that can take advantage of this.

The Last Resort – the kernel_compiler 

All of the choices that as a discussed thus far work well together. However, you can choose from a very wide range of settings, including directed compilation, caching, specialization constants, AoT compilation, and the usual SYCL compile-at-runtime behavior.

Using specialization constants to make your program performant or having it choose a specific kernel at runtime are simple processes. However, that might not be sufficient. Perhaps all your software needs to do is create a kernel from scratch.

Here is some source code to help illustrate this. Intel made an effort to compose it in a way that makes sense from top to bottom.

When is It Beneficial to Use kernel_compiler? 

Some SYCL users already have extensive kernel libraries in SPIR-V or OpenCL C. For those, the kernel_compiler is a very helpful extension that enables them to use those libraries rather than a last-resort tool.

Download the Compiler 

Download the most recent version of the Intel oneAPI DPC++/C++ Compiler, which incorporates the kernel_compiler experimental functionality, if you haven’t already. Purchase it separately for Windows or Linux, via well-known package managers only for Linux, or as a component of the Intel oneAPI Base Toolkit 2024.

Read more on Govindhtech.com

[fromdevcom]

Hashcat is a Multiplatform hash cracking software that is popular for password cracking. Hashing a common technique to store the password in various software.  Protected PDF, ZIP, and other format files that are protected by a password. This password is hashed and saved as part of the file itself. Using Hashcat you can easily identify the password of a protected file. The tool is open source and free to use. It works with CPU, GPU and other hardware that support OpenCL runtime. I have hand-curated these Hashcat online tutorials for learning and experimentation. How Hashcat Software Works? Hashcat software can identify the password by using its input as the hashed value. Since hashing is a one-way process it uses different techniques to guess the password. Hashcat can use a simple word list to guess passwords. It also supports brute-force attack that can try to create all possible character combinations for the potential password.  Recent attack features of masking and rule-based attack makes it even more powerful and faster tool to recover the password from a hash. Beginners Hashcat Tutorials : Simple and Focused As a beginner you may want to start simple with these tutorials. You can jump to advanced tutorials if you have already learned basic hashcat commands and usage. frequently_asked_questions [hashcat wiki] - The FAQs listed on official website are the best starting point for any beginner. If you see an error using the tool, you may find a detailed description on that error in this page. Hashcat Tutorial for Beginners Hack Like a Pro: How to Crack Passwords, Part 1 (Principles & Technologies) « Null Byte :: WonderHowTo Hashcat Tutorial - The basics of cracking passwords with hashcat - Laconic Wolf cracking_wpawpa2 [hashcat wiki] KALI – How to crack passwords using Hashcat – The Visual Guide | University of South Wales: Information Security & Privacy Crack WPA/WPA2 Wi-Fi Routers with Aircrack-ng and Hashcat How to Perform a Mask Attack Using hashcat | 4ARMEDHow to Perform a Mask Attack Using hashcat | 4ARMED Cloud Security Professional Services How To Perform A Rule-Based Attack Using Hashcat | 4ARMEDHow To Perform A Rule-Based Attack Using Hashcat | 4ARMED Cloud Security Professional Services Using hashcat to recover your passwords | Linux.org Cracking Passwords With Hashcat | Pengs.WIN! GitHub - brannondorsey/wifi-cracking: Crack WPA/WPA2 Wi-Fi Routers with Airodump-ng and Aircrack-ng/Hashcat Hashcat Video Tutorials and Online Courses To Learn This is a Video courses and tutorials list, you may find it helpful if you prefer video tutorials or classroom setup. How To Crack Passwords - Beginners Tutorials - YouTube How To Use Hashcat - YouTube Howto: Hashcat Cracking Password Hashes - YouTube How To Crack Password Hashes Using HashCat In Kali Linux - Flawless Programming - YouTube Password Cracking with Hashcat Tutorials - YouTube Crack Encrypted iOS backups with Hashcat - YouTube How to crack hashes using Hashcat -Tamilbotnet-Kali Linux - YouTube How To Crack Password Hashes Using HashCat In Kali Linux by rj tech - YouTube Ubuntu: How To Crack Password Using Hashcat : Tutorials - YouTube Mac OSX: How To Crack Password Using Hashcat : Tutorials - YouTube Hashcat eBooks, PDF and Cheat Sheets for Reference These are downloadable resources about hashcat. You can download the PDF and eBook versions to learn anywhere. Hashcat User Manual - The official user manual of Hashcat that contains all features in a well documented format. This may be handy once you start feel little comfortable with basic hashcat usage. Owaspbristol 2018 02 19 Practical Password Cracking - OWASP is the place for security experts to get most authentic information. This is a simple eBook about password cracking encourage stronger passwords. Bslv17 Ground1234 Passwords 201 Beyond The Basics Royce Williams 2017 07 26 - A simple presentation that covers hassed password cracking tips and techniques using hashcat.

Hashcat 4.10 Cheat Sheet v 1.2018.1 - Black Hills Information SecurityBlack Hills Information Security Hashcat-Cheatsheet/README.md at master · frizb/Hashcat-Cheatsheet · GitHub KALI – How to crack passwords using Hashcat – The Visual Guide | University of South Wales: Information Security & Privacy Hashcat Websites, Blogs and Forums To Get Help Learning Below mentioned websites can be a good source for getting help on Hashcat and related topics. Official Website of hashcat - advanced password recovery - The official Hashcat website with all details about the tool and its supported versions to download. This is the best place to start your hashcat research and learning. hashcat Forum - Best place to get help as a beginner about hashcat. I will recommend do a search before asking a question, since most questions may have been asked in past. Your Hacking Tutorial by ZempiriansHotHot - Subreddit about hacking where you may get some help and direction on using hashcat. HashCat Online - Password Recovery in the cloud WPA MD5 PDF DOC - Hashcat online, can be a good place to experiment with your hashcat skills without installing hashcat on your own computer. Newest 'hashcat' Questions - Stack Overflow - Stackoverflow is my favorite place for many things, however, for hashcat it can be a little dull since I do not notice a lot of participation from the community. You may still have some luck if you ask your question the right way and give some bounty. Summary This is a very big list of tutorials. Hashcat is just a simple software and you may need to use very few options from it. Try to experiment with it and you will start learning. Please share this with friends and add your suggestion and feedback in the comments section.

[takahashicleaning]

ＴＥＤにて

デニス・ホン：視覚障害者が運転できる車を作る！

（詳しくご覧になりたい場合は上記リンクからどうぞ）

注意！！現在、基本的人権を侵害するストーカーアルゴリズムしか能力のない人工知能です。

注意！！現在、基本的人権を侵害するストーカーアルゴリズムしか能力のない人工知能です。

注意！！現在、基本的人権を侵害するストーカーアルゴリズムしか能力のない人工知能です。

IT産業長者は、乱世の奸雄。テロ抑止にもなる現代では、競争時代の奸雄。

競争時代の奸雄によって本質が歪められていますが・・・

本来、完全な自動運転車は、視覚障害者が運転できるようになるということで開発されています！！

DARPA(米国防高等研究計画局)のアーバンチャレンジというイベントでロボティクス、レーザーレンジファインダー、GPS、フィードバック装置などのセンサーテクノロジーを使い、デニス・ホンは視覚障害者が運転できる車を作ろうとしています。

これは「自動運転車」ではないことに注意してください。

目の不自由なドライバーが、コンピュータープログラムのシステムの支援を受けて、速度、障害物との距離、ルートをリアルタイムで把握し、教えてくれるため自分はハンドルを動かすだけで運転することのできる車なのです。

簡単だと思い込んでいました。自動運転車は既に作っているので、あとは視覚障害者を乗せるだけでしょ？(笑)

大間違いでした。NFB（全米視覚障害者連合）が望んでいたのは、視覚障害者を運べる車ではなく、視覚障害者が自ら判断し運転できる車だったのです。だから、私たちはすべてを捨てて一から作り直す必要がありました。

映画のように人間のような複雑な思考をする機械とか、そのようなことは不可能であることがすでに、2000年代初頭で証明されていますので、ルーティンワークのような機械学習です。

このようなシステムに、ルーティンワークのような機械学習を取り入れていくことで、オープンデータのメリットとクラウドコンピューティングの大規模解析を融合していくことは

匿名性と高レベルのセキュリティーの前提ですが革新的なイノベーションに可能性を観ることが出来ます。

どういう仕組みなのでしょう？

3つのステップがあります。認識、計算、それに、非視覚的インタフェースです。

ドライバーは目が見えないのでシステムがドライバーに代わって環境を把握し、情報を集める必要があります。ちょうど人の内耳のように加速度や角加速度を把握します。その情報をGPS情報と合わせて車の位置を割り出します。

それから2台のカメラで車線を検出し3台のレーザーレンジファインダーで環境中の障害物をスキャンします。前後から近づく車や道路に飛び出してくるもの車の周囲の障害物などです。

そういった膨大な情報をコンピュータに取り込んで2つのことをします。1つはその情報を処理して周りの環境を理解すること。ここに車線があり、あそこに障害物があると把握し、それをドライバーに伝えます。

このシステムは賢くてどう運転すると一番安全か判断でき運転のための操作指示を生成します。

問題は、素早く正確に見ることのできない人にそういった情報や指示をどう伝えるかということです。

そのために様々な種類の非視覚的インタフェース技術を開発しました。3次元通知音システムに始まり、振動するベストボイスコマンド付きクリックホイールやレッグストリップ。足を圧迫して合図する靴まであります。

センサーのデータがコンピューターを通してドライバーに伝えられています。

なお、ビックデータは教育や医療に限定してなら、多少は有効かもしれません。それ以外は、日本の場合、プライバシーの侵害です。

通信の秘匿性とプライバシーの侵害対策として、匿名化処理の強化と強力な暗号化は絶対必要です！

さらに、オープンデータは、特定のデータが、一切の著作権、特許などの制御メカニズムの制限なしで、全ての人が

望むように再利用・再配布できるような形で、商用・非商用問わず、二次利用の形で入手できるべきであるというもの。

主な種類では、地図、遺伝子、さまざまな化合物、数学の数式や自然科学の数式、医療のデータやバイオテクノロジー

サイエンスや生物などのテキスト以外の素材が考えられます。

情報技術の発展とインターネットで大企業の何十万、何百万単位から、facebook、Apple、Amazom、Google、Microsoftなどで数億単位で共同作業ができるようになりました。

現在、プラットフォーマー企業と呼ばれる法人は先進国の国家単位レベルに近づき欧米、日本、アジア、インドが協調すれば、中国の人口をも超越するかもしれません。

法人は潰れることを前提にした有限責任！ 慈愛や基本的人権を根本とした社会システムの中の保護されなければならない小企業や個人レベルでは、違いますが・・・

こういう新産業でイノベーションが起きるとゲーム理論でいうところのプラスサムになるから既存の産業との

戦争に発展しないため共存関係を構築できる���リットがあります。デフレスパイラルも予防できる？人間の限界を超えてることが前提だけど

しかし、独占禁止法を軽視してるわけではありませんので、既存産業の戦争を避けるため新産業だけの限定で限界を超えてください！

（個人的なアイデア）

イーロンマスクが実用化している自動運転車は、２０２０年時点で、約140テラフロップスの処理速度を達成している。

これは、一昔前の地球シュミレーター第二世代2009年並の処理速度のスーパーコンピューターが搭載されていることと同じです。

つまり、走るスーパーコンピューターが搭載されていることに相当します。未来の最新技術を実用的に活用できて、また低価格でも実現している。

一台数十億円が、たった十年くらいで庶民の手の届く数百万円に！デフレスパイラルにもならないプラスサムになる真のイノベーションです。素晴らしい。

参考として、2002年の地球シミュレータ第一世代は、35.86 TFLOPS（テラフロップス)

2004年のIBM Blue Gene/Lは、136.8 TFLOPS（テラフロップス）

この処理能力をコンピューターの外部CPU、外部GPUとして機能させることが可能ならば、Thunderbolt3（USB-C）経由のeGPUという形で実現できる。

そして、現在では、活用する機会の少ない車とは、別の使いみちが広がる素晴らしい世の中になるかもしれません。

eGPUとは、External GPU（外付けGPU）の略称で、外付けGPU（グラフィックプロセッサ）を外付けHDDなどと同じようにノートPCなどにケーブルで接続出来るようにして処理能力を増加させること。

Appleのコンピューター、Thunderbolt 3端子が必要です。

MacOS High Sierra 10.13.4 以降の eGPUサポートは、パワフルなeGPUの恩恵を受けられるMetal、OpenGL、OpenCL Appの高速化が狙いです。

しかし、Appによっては、eGPUによる高速化にソフトが対応していない場合もあります。推奨GPU以外は現在、使用できません。

2015年の時点では、影響力が少ないので問題にならなかった。しかし、現在、2020年では・・・

処理速度を補う方法にクラウドコンピューターで処理すれば良さそうですが、以外とプロバイダ経由でデータが読み取られて、知らない間に無断で広告に使われている！

インターネット黎明期から警告されていた基本的人権、プライバシーの侵害などの危険性が高まる傾向が増加し、現実のものとなってきている。

これは、過去にBIGなIBMのデータセンターに対してAppleスティーブジョブズがパーソナルコンピューターを創造したことに似ています。

現在では、走るパーソナルスーパーコンピューターだけど！！

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MediaTek Dimensity 9400: Un nuevo campeón en el rendimiento gráfico móvil

La llegada del Dimensity 9400 de MediaTek, que cuenta con la GPU Arm Immortalis-G925, promete elevar los estándares de rendimiento en el procesamiento gráfico móvil. Este nuevo GPU ha hecho su debut en Geekbench, donde obtuvo una puntuación de OpenCL de 16,257 puntos, lo que representa un incremento del 10% en comparación con su predecesor, el Arm Immortalis-G720, que alcanzó los 14,679 puntos.…

[theclubhero-blog]

GPU AMD Radeon RX 8000 "RDNA 4" tem especificações vazadas

Por Vinicius Torres Oliveira

A AMD submeteu uma de suas novas GPUs Radeon RX 8000 "RDNA 4" ao Geekbench, mostrando o que esperar das próximas placas de vídeo

Uma das GPUs AMD Radeon RX 8000 “RDNA 4” teve suas informações divulgadas no Geekbench, mostrando algumas de suas especificações e como ela se posicionará diante dos demais hardwares da linha.

Ela é descrita como “GFX1201”, o que confirma que o modelo específico usará o SKU Navi 48 – o maior dos dois dies Navi 4X. A placa de vídeo é listada com 28 unidades computacionais (UC) e, levando em conta que o RDNA 3 trazia um motor de sombreamento com unidades duplas, é possível que isso signifique que traga 56 UC no total.

Essa contagem de unidades computacionais da GPU AMD Radeon RX 8000 “RDNA 4” se mostraria entre dois modelos da fabricante – a RX 7700 XT, que foi lançada com 54 UC e a RX 7800 XT, modelo lançado com 60 UC.

Além disso, é listado que a placa de vídeo terá uma velocidade de clock configurada em 2,1 GHz – o que parece baixo em comparação às GPUs RDNA 3 (que atingem entre 2,5 a 2,6 GHz com facilidade), mas é importante ressaltar que esta pode ser apenas uma amostra, uma versão de testes que não representa o estado final do modelo.

Por fim, também é visto que a GPU AMD Radeon RX 8000 “RDNA 4” (GFX1201) é listada com 16 GB de VRAM – similar ao que é visto nas RX 7800 XT e RX 7900 GRE. Isto confirma que usará a interface de bus de 256-bit, mas apesar da informação não foi revelado o tipo de memória que foi utilizado – apesar de vazamentos sugerirem que usarão o GDDR6 em 18 Gbps.

O desempenho da GPU AMD Radeon RX 8000 “RDNA 4” não teve resultados tão positivos assim no benchmark OpenCL, mas como citamos anteriormente, como é uma versão prévia e o chip deve estar passando por testes apenas, isto provavelmente não representa o que será visto em seu lançamento em 2025.

É importante notar que, se os testes já estão ocorrendo com as amostras, a fabricante deve estar analisando os dados para os ajustes necessários – algo que sempre ocorre antes da chegada dos principais hardwares ao mercado e aos consumidores. Isso significa que seu lançamento – de fato – não está tão distante assim.

Possivelmente a AMD mostrará mais detalhes das suas GPUs Radeon RX 8000 “RDNA 4” durante a CES 2025. No entanto, a concorrência também está de olho no evento e as placas de vídeo da NVIDIA “GeForce RTX 50” também estão previstas para marcarem presença por lá.