#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?"

👁️👄👁️

[forlinx]

Introduction to RK3588

What is RK3588?

RK3588 is a universal SoC with ARM architecture, which integrates quad-core Cortex-A76 (large core) and quad-core Cortex-A55(small core). Equipped with G610 MP4 GPU, which can run complex graphics processing smoothly. Embedded 3D GPU makes RK3588 fully compatible with OpenGLES 1.1, 2.0 and 3.2, OpenCL up to 2.2 and Vulkan1.2. A special 2D hardware engine with MMU will maximize display performance and provide smooth operation. And a 6 TOPs NPU empowers various AI scenarios, providing possibilities for local offline AI computing in complex scenarios, complex video stream analysis, and other applications. Built-in a variety of powerful embedded hardware engines, support 8K@60fps H.265 and VP9 decoders, 8K@30fps H.264 decoders and 4K@60fps AV1 decoders; support 8K@30fps H.264 and H.265 encoder, high-quality JPEG encoder/decoder, dedicated image pre-processor and post-processor.

RK3588 also introduces a new generation of fully hardware-based ISP (Image Signal Processor) with a maximum of 48 million pixels, implementing many algorithm accelerators, such as HDR, 3A, LSC, 3DNR, 2DNR, sharpening, dehaze, fisheye correction, gamma Correction, etc., have a wide range of applications in graphics post-processing. RK3588 integrates Rockchip's new generation NPU, which can support INT4/INT8/INT16/FP16 hybrid computing. Its strong compatibility can easily convert network models based on a series of frameworks such as TensorFlow / MXNet / PyTorch / Caffe. RK3588 has a high-performance 4-channel external memory interface (LPDDR4/LPDDR4X/LPDDR5), capable of supporting demanding memory bandwidth.

RK3588 Block Diagram

Advantages of RK3588?

Computing: RK3588 integrates quad-core Cortex-A76 and quad-core Cortex-A55, G610 MP4 graphics processor, and a separate NEON coprocessor. Integrating the third-generation NPU self-developed by Rockchip, computing power 6TOPS, which can meet the computing power requirements of most artificial intelligence models.

Vision: support multi-camera input, ISP3.0, high-quality audio;

Display: support multi-screen display, 8K high-quality, 3D display, etc.;

Video processing: support 8k video and multiple 4k codecs;

Communication: support multiple high-speed interfaces such as PCIe2.0 and PCIe3.0, USB3.0, and Gigabit Ethernet;

Operating system: Android 12 is supported. Linux and Ubuntu will be developed in succession;

FET3588-C SoM based on Rockchip RK3588

Forlinx FET3588-C SoM inherits all advantages of RK3588. The following introduces it from structure and hardware design.

1. Structure:

The SoM size is 50mm x 68mm, smaller than most RK3588 SoMs on market;

100pin ultra-thin connector is used to connect SoM and carrier board. The combined height of connectors is 1.5mm, which greatly reduces the thickness of SoM; four mounting holes with a diameter of 2.2mm are reserved at the four corners of SoM. The product is used in a vibration environment can install fixing screws to improve the reliability of product connections.

2. Hardware Design:

FET3568-C SoM uses 12V power supply. A higher power supply voltage can increase the upper limit of power supply and reduce line loss. Ensure that the Forlinx’s SoM can run stably for a long time at full load. The power supply adopts RK single PMIC solution, which supports dynamic frequency modulation.

FET3568-C SoM uses 4 pieces of 100pin connectors, with a total of 400 pins; all the functions that can be extracted from processor are all extracted, and ground loop pins of high-speed signal are sufficient, and power supply and loop pins are sufficient to ensure signal integrity and power integrity.

The default memory configuration of FET3568-C SoM supports 4GB/8GB (up to 32GB) LPDDR4/LPDDR4X-4266; default storage configuration supports 32GB/64GB (larger storage is optional) eMMC; Each interface signal and power supply of SoM and carrier board have been strictly tested to ensure that the signal quality is good and the power wave is within specified range.

PCB layout: Forlinx uses top layer-GND-POWER-bottom layer to ensure the continuity and stability of signals.

RK3588 SoM hardware design Guide

FET3588-C SoM has integrated power supply and storage circuit in a small module. The required external circuit is very simple. A minimal system only needs power supply and startup configuration to run, as shown in the figure below:

The minimum system includes SoM power supply, system flashing circuit, and debugging serial port circuit. The minimum system schematic diagram can be found in "OK3588-C_Hardware Manual". However, in general, it is recommended to connect some external devices, such as debugging serial port, otherwise user cannot judge whether system is started. After completing these, on this basis, add the functions required by user according to default interface definition of RK3588 SoM provided by Forlinx.

RK3588 Carrier Board Hardware Design Guide

The interface resources derived from Forlinx embedded OK3588-C development board are very rich, which provides great convenience for customers' development and testing. Moreover, OK3588-C development board has passed rigorous tests and can provide stable performance support for customers' high-end applications.

In order to facilitate user's secondary development, Forlinx provides RK3588 hardware design guidelines to annotate the problems that may be encountered during design process of RK3588. We want to help users make the research and development process simpler and more efficient, and make customers' products smarter and more stable. Due to the large amount of content, only a few guidelines for interface design are listed here. For details, you can contact us online to obtain "OK3588-C_Hardware Manual" (Click to Inquiry)

[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メリットがあります。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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ジェレミー・ハワード：自ら学習するコンピュータの素晴らしくも物恐ろしい可能性？

フェイフェイ・リー：コンピュータが写真を理解するようになるまで

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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.…

[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

[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.

[the-lazy-girl]

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[ujjinatd]

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[vietngocbs]

Như bạn đã thấy. Vì thiết kế vi mạch đòi hỏi sự rõ ràng trong cách thức lập luận về điều kiện để luồng dữ liệu chảy ở mức cao hơn rất nhiều so với công nghệ thông tin (thử nhằn cấu trúc dữ liệu và giải thuật đi ạ!) nên thách thức lớn nhất cho việc chuyển một triệu nhân sự từ ICT (Information and Communications Technology) sang ICD (Integrated Circuit Design) có lẽ là môi trường cho họ sinh sống và nghỉ ngơi sau giờ làm việc ạ. Và đây rõ ràng đây là điều bất khả trong tương lai gần bởi đó là số dân của một tỉnh trung bình nha. Haha :)))

Yepp. Có thể bạn cảm thấy buồn cười khi nghe việc nhiều bác sĩ thấy rất khó chịu vì ở đâu cũng có vi trùng nhưng đây là một triệu chứng trong hội chứng Rối loạn ám ảnh cưỡng chế (Obsessive-compulsive disorder - OCD). Có mã ICD10 là F42, ICD11 là 6B20, đây là một bệnh tâm căn được cho là nảy sinh từ sự lo lắng quá mức về sự thiếu vắng của một thao tác nào đó, như ở trường hợp bác sĩ là nguy cơ nhiễm trùng bệnh viện, nên rất có khả năng đây là một vấn đề sức khỏe chủ yếu ở những kỹ sư thiết kế vi mạch làm việc trong nước bởi sự vênh nhau chả hề nhẹ giữa cách họ nghĩ để làm với cách họ nghĩ để sống, ví dụ như một cái cần định nghĩa chi tiết còn một cái chả hề có luôn.

*Ngáp* Nếu thấy ngứa mắt với từ Apps trong Setting của Windows 11 thì bạn bị OCD rồi đấy. Nếu sửa thành Applications thì sẽ đỡ và bạn chả thể nào sửa được hay than phiền về điều này với Satya Nadella!!!

Ừ hứ. Kĩ sư cầu nối (Bridge System Engineer - BrSE) là người sẽ làm việc trực tiếp với khách hàng, kết nối và chịu trách nhiệm truyền đạt yêu cầu từ nội bộ khách hàng tới nhóm phát triển ở bên ngoài, đảm bảo hai bên hiểu nhau và hợp tác suôn sẻ, thuận lợi. Câu hỏi ở đây là nghiên cứu thị trường (cụ thể là khách hàng) và nghiên cứu khoa học giống và khác như thế nào? Có lấy cái này thay cái kia được chăng???

Yepp. Một dự án ICT hoàn chỉnh sẽ phải có các mục về phần cứng. Có điều đó hoặc là phần được thầu riêng hoặc là sử dụng tiếp các thiết bị có sẵn nên Software mới là phần trọng tâm khi làm kỹ sư cầu nối cho thị trường Nhật. Nó cũng đã gây khó khăn không ít cho đội vận hành khi họ vẫn đang xài đĩa mềm và chả muốn thay đổi. Huhu!

*Ngáp* "Trồng cây gì, nuôi con gì" là ví dụ minh họa cho Governance. Và để ra được đáp án thì cần cả nghiên cứu khoa học để biết cái gì thì sẽ thế nào lẫn nghiên cứu thị trường để làm rõ thế nào thì khách hàng chốt đơn. Nhưng theo mình, đây chỉ là điểm dễ thấy nhất thôi!

***

Ừ hứ. Lý thuyết mà nói thì GPU của Ivy Bridge đã đủ khả năng tăng tốc giải mã media dùng chuẩn nén VP8 (Tức là tệp ảnh đuôi webp và nhiều tệp phim đuôi webm, nói là nhiều vì thiết kế của định dạng tệp tin WebM còn cho phép nó chứa chuỗi hình được mã hóa theo kiểu VP9 và AV1. Trong đó VP9 được tối ưu cho nội dung có độ phân giải đến Full HD còn AV1 thì tốt hơn cho nội dung từ 2K trở lên) bởi S.o.C Atom Z3770 có tính năng này và có GPU thực ra là phiên bản cắt bớt đơn vị thực thi (Execution Units - EU) và xung nhịp của GPU có trong các chíp thế hệ ba cho máy tính để bàn và xách tay nên sự thiếu sót tính năng này chắc bởi vì chưa tích hợp chương trình điều khiển. Và do Intel đã ngừng việc phát triển chương trình điều khiển cho thế hệ chíp này nên ta sẽ cần đốt kha khá tiền để chứng minh năng lực giải quyết vấn đề thiếu điện (giảm tải CPU) ở quy mô toàn cầu này nha :D

Yepp. Ivy Bridge có hỗ trợ OpenCl (Open Computing Language) còn Sandy Bridge liền trước thì không và dù nhiều người đã viết chương trình giải mã VP8/VP9 bằng OpenCl nhưng hiện tại nó rất chậm khi chạy trên x86 nên chả còn ai quan tâm đến phương án này làm chi.

Ừ hứ. Intel có giới thiệu cách dùng Register-Transfer Level (RTL hay Cấp độ thanh ghi - chuyển đổi) để tối ưu mã lệnh OpenCl chạy trên FPGA và Google đã cung cấp miễn phí RTL của vi mạch giải mã VP8/VP9 cho bất kì công ty nào có nhu cầu thêm tính năng này vào sản phẩm thực tế. Nhưng hiệu năng của cách này trên GPU đến từ Intel hiện chưa đủ tốt nên việc phát minh lại cái bánh xe vẫn cần diễn ra :D

*Cho bạn nào chưa biết* Về cơ bản, RTL là sự thể hiện của mạch số dưới dạng luồng dữ liệu giữa các thanh ghi và các phép toán luận lý trên các tín hiệu mang dữ liệu đó. Tạo ra RTL hiện là bước đầu tiên để thiết kế vi mạch thuần số trước khi tái tạo nó bởi ngôn ngữ mô tả phần cứng như Verilog hay VHDL để máy tính tạo ra thiết kế mẫu ít trừu tượng hơn, tức làm hiện ra mối liên kết cụ thể giữa các linh kiện để tối ưu hoá bằng tay. Việc tối ưu hoá này rất mất thời gian nên đây là cơ hội cho các đơn vị bên ngoài khi giúp công ty thiết kế vi mạch có thể cắt giảm các chi phí kém ưu tiên như tiền cho cơ sở hạ tầng: Giá thuê văn phòng trung bình ở Âu Mỹ hiện đang gấp đôi giá thuê văn phòng hạng A ở Việt Nam (Và có nhiều nhân sự dưới chuẩn hơn)

***

Chả biết. Vì dùng nhân Linux tiêu chuẩn nên chắc không cần sửa đổi nhiều để chạy Tizen trên WSL 2 như Windows Subsystem for Android đã bị ngưng do thu chả bù chi. (Tencent đã xác nhận tiếp tục chi tiền cho dự án nhưng hên xui vì hiện chỉ dành cho mỗi thị trường Đại lục) Tức là cản trở lớn nhất hiện giờ để Microsoft Store có mặt trên các thiết bị di động chỉ là các bên liên quan có muốn triển khai hay không

Nhân tiện thì rất nhiều chuyên gia đã kêu gọi ngưng dùng QtWebkit vì quá nhiều lỗ hổng bảo mật không được vá. Và lý do để vẫn còn sử dụng thư viện này là vì lõi Chromium chả tối ưu bộ nhớ như Webkit2.

[codeshive]

ME 766 : HW 3 solved

1. Create two matrices, A and B, each of size (N N). Write a CUDA or OpenCL (choice is yours) for computing C = AB. Report the times taken for the codes. Vary the size of the problem from N = 100 . . . 10000. Also report the specifications of the computer you are running this on. Also specs of the GPU (if any). 2. Choose A and B to be same as HW 2. 3. Submit your code and a report.