#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.

[datasv]

В полном видео всё же они дали отпор. Охранник все таки хорошо был подготовлен... Не просто так берут в охрану, особенно молодых, после армии, когда там дисциплина, и еще не расслабился, некоторые не знают... Жаль что opencl с автоматическим огнестрелом в магазинах не стоит 😀, который по ногам целится 😄, агрессорам, ситцация была бы очень смешной. Когда сотрудники мвд просто пришли и никого не задержали 😀, и еще главное дааали ходу побойке. А почему? А потому что они сами и скорей всего через какооо нибудь петушка и привели бригаду, а тут разбойное нападение, хорошие сроки... Ну если будут ставить такие автоматические устройства, можно и без уголовной ответственности обходиться. На кнопочку нажал, под кассой, ввзов оперативной группы, скорой помощи(ноги забинтовывать агрессорам), и моментальная карма. Регистрация случая. Который ложится в характеристику. И все довольны - без судебных разрешений, и желание отпадает на такие приключения.

[howzitsa]

Intel 12th Gen Core i3-12100 LGA1700 3.3GHZ 4-Core Take the next evolutionary leap with the performance hybrid architecture of 12th Generation Intel® Core™ processors. Get the performance you need, where you need it, whether you’re a gamer, creator, streamer, or everyday user. Whatever you’re into, do more of it, whenever you want with Intel® 12 Generation processors. With unprecedented new performance hybrid architecture, 12th Gen Intel® Core™ processors offer a unique combination of Performance and Efficient-cores (P-core and E-core). That means real-world performance, intuitively scaled to match whatever you’re doing. The Performance-core is Intel’s highest-performing CPU core ever. And it’s designed to maximize single-thread performance and responsiveness for compute-intensive workloads like gaming and 3D design. The Efficient-core delivers multithreaded performance for tasks that can run in parallel (like image rendering), along with efficient offload of background tasks for modern multitasking. So that Performance-cores and Efficient-cores can work seamlessly with the operating system, Intel built Intel® Thread Director right into the hardware. Automatically monitoring and analyzing on-the-fly, Intel® Thread Director guides the operating system, helping it place the right thread on the right core, at the right time. And it does it all dynamically, adapting scheduling guidance based on actual computing needs, not on static rules. 12th Gen Intel® Core™ processors also support the next wave of discrete graphics cards and storage devices. These devices take advantage of increased throughput coming with PCIe 5.0 as well the higher speeds and bandwidth of DDR5 memory. With 12th Gen Intel® Core™ processors, standard, built-in features enable capabilities like noise suppression, auto-framing, and optimization for bandwidth and video resolution while gaming. That saves you time and lets you multitask in ways you’ve only ever dreamed of, until now. From epic gaming to browsing to streaming to creating your next masterpiece, 12th Gen Intel® Core™ processors make it all entirely doable. FEATURES: 4 Cores 8 Threads Max Turbo Frequency of up to 4.30GHz 12MB Intel® Smart Cache Support for DDR4 and DDR5 memory Built for serious gaming Smart solutions for enthusiasts and creators Nearly 3x faster connectivity SPECIFICATIONS: Essentials: Product Collection: 12th Generation Intel® Core™ i3 Processors Code Name: Products formerly Alder Lake Vertical Segment: Desktop Processor Number: i3-12100 CPU Specifications: Total Cores: 4 # of Performance-cores: 4 # of Efficient-cores: 0 Total Threads: 8 Max Turbo Frequency: 4.30 GHz Performance-core Max Turbo Frequency: 4.30 GHz Performance-core Base Frequency: 3.30 GHz Cache: 12 MB Intel® Smart Cache Total L2 Cache: 5 MB Processor Base Power: 60 W Maximum Turbo Power: 89 W Memory Specifications: Max Memory Size (dependent on memory type): 128 GB Memory Types: Up to DDR5 4800 MT/s Up to DDR4 3200 MT/s Max # of Memory Channels: 2 Max Memory Bandwidth: 76.8 GB/s Processor Graphics: Processor Graphics: Intel® UHD Graphics 730 Graphics Base Frequency: 300 MHz Graphics Max Dynamic Frequency: 1.40 GHz Graphics Output: eDP 1.4b, DP 1.4a, HDMI 2.1 Execution Units: 24 Max Resolution (HDMI): 4096 x 2160 @ 60Hz Max Resolution (DP): 7680 x 4320 @ 60Hz Max Resolution (eDP – Integrated Flat Panel): 5120 x 3200 @ 120Hz DirectX Support: 12 OpenGL Support: 4.5 Multi-Format Codec Engines: 1 Intel® Quick Sync Video: Yes Intel® Clear Video HD Technology: Yes # of Displays Supported: 4 Device ID: 0x4692 OpenCL Support: 2.1 Expansion Options: Direct Media Interface (DMI) Revision: 4.0 Max # of DMI Lanes: 8 Scalability: 1S Only PCI Express Revision: 5.0 and 4.0 PCI Express Configurations: Up to 1×16+4, 2×8+4 Max # of PCI Express Lanes: 20 Advanced Features: Intel® Gaussian & Neural Accelerator: 3.0 Intel® Thread Director: No Intel® Deep Learning Boost (Intel® DL Boost): Yes Intel® Optane™ Memory Supported: Yes Intel® Speed Shift Technology: Yes Intel® Turbo Boost Max Technology 3.0: No Intel® Turbo Boost Technology: 2.0 Intel® Hyper-Threading Technology: Yes Intel® 64: Yes Instruction Set: 64-bit Instruction Set Extensions: Intel® SSE4.1, Intel® SSE4.2, Intel® AVX2 Idle States: Yes Enhanced Intel SpeedStep® Technology: Yes Thermal Monitoring Technologies: Yes Intel® Volume Management Device (VMD): Yes Security & Reliability Intel® Threat Detection Technology (TDT): Yes Intel® Standard Manageability (ISM): Yes Intel® AES New Instructions: Yes Secure Key: Yes Intel® OS Guard: Yes Execute Disable Bit: Yes Intel® Boot Guard: Yes Mode-based Execute Control (MBEC): Yes Intel® Control-Flow Enforcement Technology: Yes Intel® Virtualization Technology (VT-x): Yes Intel® Virtualization Technology for Directed I/O (VT-d): Yes Intel® VT-x with Extended Page Tables (EPT): Yes WHAT’S IN THE BOX: Intel 12th Gen Core i3-12100 LGA1700 3.3GHz 4-Core CPU x1

[australiajobstoday]

Software Development Engineer - HIP/OpenCL Runtime

_ Responsibilities: THE ROLE: AMD is looking for an influential software engineer who is passionate about improving the performance… and will work with the very latest hardware and software technology. THE PERSON: The ideal candidate should be passionate… Apply Now

[aitoolswhitehattoolbox]

Software Development Engineer - HIP/OpenCL Runtime

_ Responsibilities: THE ROLE: AMD is looking for an influential software engineer who is passionate about improving the performance… and will work with the very latest hardware and software technology. THE PERSON: The ideal candidate should be passionate… Apply Now

[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]

Teste mostra Intel Arc B570 12% mais lenta do que a Intel Arc B580

Por Vinicius Torres Oliveira

Nova placa da Intel, a B570, deve chegar ao mercado 12% mais barata que a B580 e com um desempenho 12% menor.

Ao que tudo indica, a Intel calculou com precisão a relação desempenho-preço da sua nova placa B570, que chega ao mercado 12% mais barata que a B580 e com um desempenho 12% menor. As informações foram publicadas no site Geekbench.

E embora os testes do Geekbench não sejam totalmente comparáveis aos benchmarks de jogos, especialmente no que diz respeito ao desempenho da memória, eles mostram a capacidade de desempenho bruto dos componentes testados.

A nova B570 seguirá modelo da irmã, a B580, lançada em dezembro. Porém, é mais barata, possui menos núcleos e vem com um preço mais acessível. Ela também marca o primeiro lançamento de uma placa de vídeo em 2025.

Sua antecessora, a B580, foi um sucesso para a Intel, com suas placas esgotadas em quase todos os varejistas populares, então a B570 tem a oportunidade de preencher uma lacuna no segmento de entrada.

A B570 alcança 86.716 pontos no teste OpenCL, enquanto a B580 se aproxima de 100.000. O site usou 98.343 para comparação, mostrando que a B570 é aproximadamente 11,8% mais lenta. No entanto, é possível encontrar pontuações mais altas para comparação.

Sem conhecer as especificações exatas da placa, é difícil fazer uma comparação adequada com um modelo com configuração de hardwre aproximado. A B580 tem três níveis de overclocking de até 210W, então não são placas com a mesma linha de desempenho.

Com base nas especificações, a B570 possui 10% menos núcleos, uma velocidade de clock 7% menor, largura de banda 17% menor e um preço 12% mais baixo. Nesse teste, ela entregou um desempenho 12% inferior.

Mesmo com sacrifícios, o sucesso da B580 sugere que a B570 pode alcançar popularidade semelhante. Caso contrário, veremos outro Arc buscando rapidamente um novo e mais baixo MSRP.

Para a Intel, a principal vantagem agora é que nem a AMD nem a NVIDIA têm um SKU de entrada com previsão para lançamento em breve, o que proporciona uma janela de oportunidade para capturar participação de mercado antes das atualizações RDNA4 e Blackwell chegarem para os consumidores.

Com seus novos modelos, a AMD está trazendo o FSR4, compatível com AMD Ryzen 9000 Series e com GPUs Radeon Graphics. A tecnologia usa inteligência artificial para fazer upscalling. Em sistemas iguais, o desempenho é comparável ao FSR3, mas o drive deverá ser aprimorado.

A NVIDIA, por sua vez, está apresentando o DLSS4, compatível com as GPUs Blackwell e que também usa IA para aumentar o desempenho e a qualidade dos gráficos dos jogos. No lançamento, a empresa já confirmou suporte para 75 jogos e a lista deverá aumentar conforme avançar o desenvolvimento da tecnologia.

[telodogratis]

NVIDIA GeForce RTX 5090 appare nei primi benchmark e si prende facilmente la prima posizione su Geekbench

NVIDIA GeForce RTX 5090 appare nei primi benchmark e si prende facilmente la prima posizione su Geekbench I primi benchmark trapelati della NVIDIA RTX 5090 mostrano prestazioni in linea con le attese: ecco i punteggi nei test Vulkan e OpenCL. Powered by WPeMatico I primi benchmark trapelati della NVIDIA RTX 5090 mostrano prestazioni in linea con le attese: ecco i punteggi nei test Vulkan e…

[codezup]

C++ and 3D Graphics: A Tutorial on Using OpenGL and OpenCL

Introduction C++ and 3D Graphics: A Tutorial on Using OpenGL and OpenCL is a comprehensive guide to creating 3D graphics applications using C++ and the OpenGL and OpenCL APIs. This tutorial is designed for developers who want to learn how to create high-performance 3D graphics applications using C++ and the OpenGL and OpenCL APIs. In this tutorial, we will cover the core concepts and…

[bizonmark]

Projector 300Ansi With Video Game Consoles 36000+ Allwinner H713 Android11 Smart Portable Home Cinema

P30MAX Game Projector: SOCH713 CPU Quad-core 64 bit ARM Cortex™-A53 GPU ARM Mali-G31 Supports OpenGL ES 3.2, Vulkan 1.1, and OpenCL 2.0 Light source type ：LED Resolving power1280*720P Brightness 300 ANSI Contrast1000：1 Projective ratio1.25 : 1 Optimum projection size30~100 inch Aspect ratio4:3、16:9、16:10 Focus modeManual focus   VPU Supports： – 10-bit HEVC MP@L6.1, up to 4K@30fps – 10-bit VP9, up…