#oneapi

[govindhtech]

OneAPI Construction Kit For Intel RISC V Processor Interface

With the oneAPI Construction Kit, you may integrate the oneAPI Ecosystem into your Intel RISC V Processor.

Intel RISC-V

Recently, Codeplay, an Intel business, revealed that their oneAPI Construction Kit supports RISC-V. Rapidly expanding, Intel RISC V is an open standard instruction set architecture (ISA) available under royalty-free open-source licenses for processors of all kinds.

Through direct programming in C++ with SYCL, along with a set of libraries aimed at common functions like math, threading, and neural networks, and a hardware abstraction layer that allows programming in one language to target different devices, the oneAPI programming model enables a single codebase to be deployed across multiple computing architectures including CPUs, GPUs, FPGAs, and other accelerators.

In order to promote open source cooperation and the creation of a cohesive, cross-architecture programming paradigm free from proprietary software lock-in, the oneAPI standard is now overseen by the UXL Foundation.

A framework that may be used to expand the oneAPI ecosystem to bespoke AI and HPC architectures is Codeplay’s oneAPI Construction Kit. For both native on-host and cross-compilation, the most recent 4.0 version brings RISC-V native host for the first time.

Because of this capability, programs may be executed on a CPU and benefit from the acceleration that SYCL offers via data parallelism. With the oneAPI Construction Kit, Intel RISC V processor designers can now effortlessly connect SYCL and the oneAPI ecosystem with their hardware, marking a key step toward realizing the goal of a completely open hardware and software stack. It is completely free to use and open-source.

OneAPI Construction Kit

Your processor has access to an open environment with the oneAPI Construction Kit. It is a framework that opens up SYCL and other open standards to hardware platforms, and it can be used to expand the oneAPI ecosystem to include unique AI and HPC architectures.

Give Developers Access to a Dynamic, Open-Ecosystem

With the oneAPI Construction Kit, new and customized accelerators may benefit from the oneAPI ecosystem and an abundance of SYCL libraries. Contributors from many sectors of the industry support and maintain this open environment, so you may build with the knowledge that features and libraries will be preserved. Additionally, it frees up developers’ time to innovate more quickly by reducing the amount of time spent rewriting code and managing disparate codebases.

The oneAPI Construction Kit is useful for anybody who designs hardware. To get you started, the Kit includes a reference implementation for Intel RISC V vector processors, although it is not confined to RISC-V and may be modified for a variety of processors.

Codeplay Enhances the oneAPI Construction Kit with RISC-V Support

The rapidly expanding open standard instruction set architecture (ISA) known as RISC-V is compatible with all sorts of processors, including accelerators and CPUs. Axelera, Codasip, and others make Intel RISC V processors for a variety of applications. RISC-V-powered microprocessors are also being developed by the EU as part of the European Processor Initiative.

At Codeplay, has been long been pioneers in open ecosystems, and as a part of RISC-V International, its’ve worked on the project for a number of years, leading working groups that have helped to shape the standard. Nous realize that building a genuinely open environment starts with open, standards-based hardware. But in order to do that, must also need open hardware, open software, and open source from top to bottom.

This is where oneAPI and SYCL come in, offering an ecosystem of open-source, standards-based software libraries for applications of various kinds, such oneMKL or oneDNN, combined with a well-developed programming architecture. Both SYCL and oneAPI are heterogeneous, which means that you may create code once and use it on any GPU AMD, Intel, NVIDIA, or, as of late, RISC-V without being restricted by the manufacturer.

Intel initially implemented RISC-V native host for both native on-host and cross-compilation with the most recent 4.0 version of the oneAPI Construction Kit. Because of this capability, programs may be executed on a CPU and benefit from the acceleration that SYCL offers via data parallelism. With the oneAPI Construction Kit, Intel RISC V processor designers can now effortlessly connect SYCL and the oneAPI ecosystem with their hardware, marking a major step toward realizing the vision of a completely open hardware and software stack.

Read more on govindhtech.com

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[ithardware-info]

CPU Requirements for Machine Learning and AI Workstations

CPU Requirements for Machine Learning and AI Workstations From conventional regression models, non-neural network classifiers, and statistical models that are represented by capabilities in Python SciKitLearn and the R language to Deep Learning models using frameworks like PyTorch and TensorFlow, there are many different kinds of machine learning and artificial intelligence applications. There may also be a great deal of variation among these various ML/AI model types. Although the "best" hardware will generally follow certain patterns, the ideal specifications for a particular application may differ. We will base our suggestions on generalizations from common workflows. Please be aware that this is more about programming model "training" than "inference" using AI machine learning workstation hardware. CPU (processor) Performance in the ML/AI space is typically dominated by GPU acceleration. But the platform to support it is defined by the motherboard and CPU. The fact that preparing for GPU training requires a substantial amount of work in data analysis and cleanup, which is typically accomplished on the CPU, is another truth. When GPU constraints like onboard memory (VRAM) availability necessitate it, the CPU can also serve as the primary computing engine. Which CPU is ideal for AI and machine learning workstations? AMD Threadripper Pro and Intel Xeon W are the two suggested CPU systems. This is due to the fact that both of these provide outstanding memory performance in CPU space, exceptional stability, and the ability to provide the necessary PCI-Express lanes for multiple video cards (GPUs). In order to reduce memory mapping problems over multi-CPU interconnects, which can result in issues mapping memory to GPUs, we often advise single-socket CPU workstations. Does machine learning and AI speed up with more CPU cores? The anticipated load for non-GPU operations will determine how many cores are selected. It is generally advised that each GPU accelerator have a minimum of four cores. However, 32 or even 64 cores can be perfect if there is a sizable CPU computation component to your task. In any event, a 16-core CPU is typically regarded as the bare minimum for this kind of workstation. Does AI and machine learning perform better on AMD or Intel CPUs? Choosing a brand in this market is primarily a personal decision, at least if GPU acceleration is the primary factor in your workload. However, if some of the technologies in the Intel oneAPI AI Analytics Toolkit may improve your workflow, the Intel platform would be better. Why are Threadripper Pro or Xeon CPUs suggested over more "consumer" level CPUs? When it comes to ML and AI workloads, the main justification for this advice is the number of PCI-Express lanes that these CPUs support, which will determine how many GPUs can be used. Depending on motherboard design, chassis size, and power consumption, the AMD Threadripper PRO 7000 Series and Intel Xeon W-3500 both have enough PCIe lanes to accommodate three or four GPUs. Additionally, this processor class offers eight memory channels, which can significantly affect performance for tasks that are CPU-bound. The fact that these processors are enterprise grade and that the platform as a whole is probably resilient to high, continuous compute loads is another factor to take into account.

[bird4669]

https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html?cid=em&source=elo&campid=smggmo_WW_gmocoma_EMPU_EN_2024_2025.0%20tools%20release_C-MKA-40744_T-MKA-44324&content=smggmo_WW_gmocoma_EMPU_EN_2024_2025.0%20tools%20release_C-MKA-40744_T-MKA-44324-Developer-Generic&elq_cid=1517609&em_id=107177&elqrid=d8f3192c72ef44ea826b4383dc4a1166&elqcampid=65092&erpm_id=3833742#gs.iqonpv

[likejazz]

지난번 라마3 모델의 순수 NumPy 구현에 이어 이번에는 라마3 모델을 순수 C/CUDA로 구현해봤습니다.

GitHub: https://github.com/likejazz/llama3.cuda

Reddit 소개글: https://www.reddit.com/r/LocalLLaMA/comments/1d6q7qh/llama3cuda_pure_ccuda_implementation_for_llama_3/

NumPy 구현과 달리 CUDA 구현은 고성능을 목표로 했기 때문에 M2 맥북 에어에서 33 tokens/s로 처리되던 것에 비해 CUDA 버전은 4080 SUPER에서 2,823 tokens/s로 처리되어 약 85배 더 빠른 속도를 보입니다. 왜 GPU를 사용해야 하는지를 몸소 체감할 수 있도록 했습니다.

무엇보다 이 모든 구현에 오픈소스 커뮤니티의 많은 도움을 받았습니다. 앞으로도 몇 차례 추가 개선을 통해 다양한 실험을 이어나갈 생각입니다. 피드백과 기여 또한 환영합니다.

상세한 기술적인 내용은 다음과 같습니다.

No dependency 코드는 간결함과 가독성을 유지하도록 구성했으며, 의존성이 없는 구조로 개발하여 어디서든 간단하게 컴파일이 가능하게 했습니다. 아울러 Makefile과 CMake를 모두 지원합니다.

No C++ C++를 사용하지 않은 순수 C 구현이며, 모든 값은 포인터와 더블 포인터로 CPU에서 GPU의 값을 가리키는 형태로 구현되어 있습니다.

One single file UTF-8 바이트 시퀀스 처리 같은 상용구 코드를 포함하고 있지만 전체 코드를 단일 파일에서 900줄 미만으로 유지했습니다.

Same result NumPy 구현과 동일한 결과를 얻기 위해 logit 값을 일일이 디버깅하여 부동 소수점 연산 오차를 0.5% 미만으로 줄였습니다.

이외에도 추후 개선 계획은 다음과 같습니다.

NumPy 버전과 동일한 결과를 보장하기 위해 토크나이저를 비롯한 일부 패치를 진행하였는데 재밌게도 토크나이저 코드에 안드레이 카파시가 “I don't have the energy to read more of the sentencepiece code to figure out what it's doing”라고 주석을 달아둔 걸 볼 수 있었습니다. 이 부분을 깔끔하게 마무리 하기 위해 많은 시간을 투자해 봤으나 아쉽게도 좋은 결과를 얻진 못했고 지저분하게 몽키 패치로 처리할 수밖에 없었습니다. 추후에 추가 개선을 진행하면서 다시 한번 도전해 볼 예정입니다.

이후에는 AMD의 ROCm 구현, Intel의 oneAPI 구현을 통해 CUDA 이외에 다른 플랫폼에서도 각각 검증을 진행해 볼 예정입니다. 또한 현재도 단일 커널로 Multi-Head Attention을 처리하여 Flash Attention과 비슷한 효과를 내고 있으나 GEMM 연산이 아닌 GEMV 연산을 수행하여 다소 비효율적으로 동작하는 문제가 있습니다. 추후에는 이 부분을 개선하여 Flash Attention을 정확하게 구현해 볼 예정입니다.

모델 구조와 UTF-8 토크나이저 구현은 안드레이 카파시가 지난번 llama2.c에 구현한 것을 대부분 사용했으며 CUDA 구현은 포틀랜드의 CUDA 전문 프로그래머인 rogerallen이 구현한 커널을 활용했습니다. 이외에 초창기에 인도의 ankan-ban이 구현한 CUDA 커널도 많이 참고하였습니다.

전 세계 오픈소스 커뮤니티의 도움으로 저 또한 간결하게 CUDA 구현을 마무리할 수 있었습니다. 많은 도움을 받았던 만큼 저 또한 코드를 공개하여 오픈소스 혁신이 전 세계에 이어질 수 있도록 보탬을 더해봅니다. 많은 분들에게 도움이 되길 바랍니다.

[tomreview]

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Access familiar Python* tools and frameworks for AI development.

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[hackernewsrobot]

FreeBSD has a(nother) new C compiler: Intel oneAPI DPC++/C++

https://briancallahan.net/blog/20240306.html

[lanshengic]

Intel continues to innovate edge AI technology and joins hands with ecological partners to promote urban digital transformation

【Lansheng Technology News】On October 24, 2023, the 2023 Intel Digital Intelligence Park and Community Ecology Conference with the theme of "Initiating Digital Intelligence to Create a Smart City" was held in Shenzhen.

At this conference, Intel comprehensively demonstrated its vision and strategic layout in the field of smart cities, and talked about the future of smart cities with many technical experts and industry elites from smart cities, smart parks, smart communities and other professional fields. Trends and development directions, and jointly explore the latest technologies and products in the field of smart cities.

Intel said that in order to solve the security issues faced by smart cities during the development process, as well as the challenges caused by road congestion and insufficient infrastructure, the company has built a strong edge artificial intelligence software and hardware product portfolio and vertical industry solutions, and cooperated with a wide range of Cooperate with ecological partners to accelerate the digital and intelligent transformation of cities.

In terms of product layout, Intel has built a diversified hardware product portfolio for the edge, including Intel® Xeon® processors, Intel® Core® processors, and ARC series and Flex series independent graphics cards; on the other hand, Intel can provide tools, software , reference solutions and tools, including OpenVINO, FlexRAN, and oneAPI.

At the same time, Intel also provides critical value to a variety of business types and end customers by creating innovative solutions. For example, the VPPSDK solution provided by Intel can enable customers to quickly develop application software for NVR/VPP products on Intel CPU platforms, allowing developers to directly use the interfaces provided by VPPSDK to complete corresponding functional development without further knowledge of Intel. Chip-related underlying video processing software. The Intel® Edge AI Computing Box integrates Intel's advanced software and hardware technology and commercial AI algorithms that have been deployed in thousands of industries, helping industry partners accelerate the launch and deployment of solutions.

Intel cooperates with a wide range of ecological partners to promote the large-scale implementation of innovative technologies in cities and create advanced solutions for future smart cities for many scenarios. For example, based on Intel processors, Gosuncn Technology Group has built a holographic smart video application solution to help realize holographic processing of smart videos and multi-dimensional data; Intel and Entropy Technology have jointly created the Entropy-based intelligent edge server BioCVBox, which is targeted at small and medium-sized enterprises. Micro-projects integrate storage, computing, management and other applications. They have the characteristics of low construction cost, simple deployment and operation and maintenance, powerful performance, safety, stability and high reliability.

In addition, Intel is working with the National Intelligent Building and Residential Area Digital Standardization Technical Committee to develop reference standards for the digital campus industry. This specification can ensure that the construction of the park follows unified standards, enables interconnection between various systems, and avoids the formation of information islands. It can also help the park create unified workflow, collaboration, scheduling and sharing mechanisms, thereby promoting the park to improve operational efficiency and service quality. , improve the overall operational level.

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[govindhtech]

Roofline AI: Unlocking The Potential Of Variable Hardware

What is Roofline AI?

Edge AI is implemented with the help of a software development kit (SDK) called Roofline AI. It was developed by Roofline AI GmbH, a spin-off from RWTH Aachen University.

The following is made easier with RooflineAI’s SDK:

Flexibility: Models from any AI framework, including ONNX, PyTorch, and TensorFlow, may be imported.

Roofline AI provides excellent performance.

Usability: RooflineAI is simple to use.

RooflineAI makes it possible to deploy on a variety of hardware, such as CPUs, MPUs, MCUs, GPUs, and specialized AI hardware accelerators.

RooflineAI’s retargetable AI compiler technology fosters collaborations with chip suppliers and the open-source community.

A computer science technique called the Roofline model aids programmers in figuring out a computation’s compute-memory ratio. It is employed to evaluate AI architectures’ memory bandwidth and computational efficiency.

To redefine edge AI deployment

Edge AI is developing quickly. Rapidly emerging novel models, like LLMs, make it difficult to foresee technological advancements. Simultaneously, hardware solutions are becoming more complicated and diverse.

Conventional deployment techniques are unable to keep up with this rate and have turned into significant obstacles to edge AI adoption. They are uncomfortable to use, have limited performance, and are not very adaptable.

With a software solution that provides unparalleled flexibility, superior performance, and user-friendliness, Roofline transforms this procedure. With a single Python line, import models from any framework and distribute them across various devices.

Benefits

Flexible

Install any model from any framework on various target devices. Innovative applications may be deployed on the most efficient hardware with to the retargetable compiler.

Efficient

Unlock your system’s full potential. Without sacrificing accuracy, it provide definite performance benefits, including up to 4x reduced memory consumption and 1.5x lower latency.

EASY

Deployment is as simple as a Python call with us. All of the necessary tools are included in to SDK. Unfold them if you’d want, or let us handle the magic from quantization to debugging.

How RooflineAI works

Roofline AI showed how their compiler converts machine learning models from well-known frameworks like PyTorch and TensorFlow into SPIR-V code, a specific language for carrying out parallel computation operations, during the presentation.

As a consequence, developers may more easily get optimal performance without requiring unique setups for every kind of hardware with to a simplified procedure that permits quick, optimized AI model deployment across several platforms.

OneAPI’s ability to enable next-generation AI is demonstrated by Roofline AI’s dedication to improving compiler technology. Roofline AI is not only enhancing AI deployment but also establishing a new benchmark for AI scalability and efficiency with to its unified support for many devices and seamless connectivity with the UXL ecosystem.

Roofline AI is establishing itself as a major force in the development of scalable, high-performance AI applications by pushing the limits of AI compiler technology.

The Contribution of Roofline AI to the Development of Compiler Technology with oneAPI

The oneAPI DevSummit is an event centered around the oneAPI specification, an open programming paradigm that spans industries and was created by Intel to accommodate a variety of hardware architectures.

The DevSummit series, which are held all around the world and are frequently organized by groups like the UXL Foundation, bring together developers, researchers, and business executives to discuss the real-world uses of oneAPI in fields including artificial intelligence (AI), high-performance computing (HPC), edge computing, and more.

Roofline AI took center stage at the recent oneAPI DevSummit, which was organized by the UXL Foundation and Intel Liftoff member, to showcase its creative strategy for improving AI and high-performance HPC performance.

Through RooflineAI’s integration with the UXL framework, they were able to fulfill a key demand in the AI and HPC ecosystem: effective and flexible AI compiler support that can blend in with a variety of devices.

In order to connect AI models and the hardware that runs them, AI compilers are essential. The team from Roofline AI stressed in their pitch that they have developed a strong compiler that facilitates end-to-end model execution for the UXL ecosystem by utilizing the open-source Multi-Level Intermediate Representation (MLIR). With this architecture, developers can map and run AI models on many devices with unmatched flexibility and efficiency.

It’s a clear advancement in device-agnostic AI processing, especially for sectors with a range of hardware requirements. A lightweight runtime based on the Level Zero API, which makes kernel calls and efficiently manages memory, is the foundation of their approach.

In addition to optimizing performance, Roofline AI‘s runtime guarantees compatibility with a variety of Level Zero-compatible hardware, such as Intel GPUs. Because of this interoperability, developers may use their software to control devices outside of the box, reducing the requirement for configuration and increasing the range of hardware alternatives.

Read more on govindhtech.com

[tamarovjo4]

The Linux Foundation launches the Unified Acceleration Foundation to create an open standard for accelerator programming, an evolution of the oneAPI initiative (Frederic Lardinois/TechCrunch)

http://dlvr.it/SwMjZg

[jacquelineknowle]

Professional Marketplace API Integration - DigitalAPICraft

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[rrkrishna3031-blog]

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[incredefy]