#GPUAcceleration

[govindhtech]

New AMD ROCm 6.3 Release Expands AI and HPC Horizons

Opening Up New Paths in AI and HPC with AMD’s Release ROCm 6.3. With the introduction of cutting-edge tools and optimizations to improve AI, ML, and HPC workloads on AMD Instinct GPU accelerators, ROCm 6.3 represents a major milestone for the AMD open-source platform. By increasing developer productivity, ROCm 6.3 is designed to enable a diverse spectrum of clients, from cutting-edge AI startups to HPC-driven businesses.

This blog explores the release’s key features, which include a redesigned FlashAttention-2 for better AI training and inference, the introduction of multi-node Fast Fourier Transform (FFT) to transform HPC workflows, a smooth integration of SGLang for faster AI inferencing, and more. Discover these fascinating developments and more as ROCm 6.3 propels industry innovation.

Super-Fast Inferencing of Generative AI (GenAI) Models with SGLang in ROCm 6.3

Industries are being revolutionized by GenAI, yet implementing huge models frequently involves overcoming latency, throughput, and resource usage issues. Presenting SGLang, a new runtime optimized for inferring state-of-the-art generative models like LLMs and VLMs on AMD Instinct GPUs and supported by ROCm 6.3.

Why It Is Important to You

6X Higher Throughput: According to research, you can outperform current systems on LLM inferencing by up to 6X, allowing your company to support AI applications on a large scale.

Usability: With Python integrated and pre-configured in the ROCm Docker containers, developers can quickly construct scalable cloud backends, multimodal processes, and interactive AI helpers with less setup time.

SGLang provides the performance and usability required to satisfy corporate objectives, whether you’re developing AI products that interact with customers or expanding AI workloads in the cloud.

Next-Level Transformer Optimization: Re-Engineered FlashAttention-2 on AMD Instinct

The foundation of contemporary AI is transformer models, although scalability has always been constrained by their large memory and processing requirements. AMD resolves these issues with FlashAttention-2 designed for ROCm 6.3, allowing for quicker, more effective training and inference.

Why It Will Be Favorite by Developers

3X Speedups: In comparison to FlashAttention-1, achieve up to 3X speedups on backward passes and a highly efficient forward pass. This will speed up model training and inference, lowering the time-to-market for corporate AI applications.

Extended Sequence Lengths: AMD Instinct GPUs handle longer sequences with ease with to their effective memory use and low I/O overhead.

With ROCm’s PyTorch container and Composable Kernel (CK) as the backend, you can easily add FlashAttention-2 on AMD Instinct GPU accelerators into your current workflows and optimize your AI pipelines.

AMD Fortran Compiler: Bridging Legacy Code to GPU Acceleration

With the release of the new AMD Fortran compiler in ROCm 6.3, businesses using AMD Instinct accelerators to run historical Fortran-based HPC applications may now fully utilize the potential of contemporary GPU acceleration.

Principal Advantages

Direct GPU Offloading: Use OpenMP offloading to take advantage of AMD Instinct GPUs and speed up important scientific applications.

Backward Compatibility: Utilize AMD’s next-generation GPU capabilities while building upon pre-existing Fortran code.

Streamlined Integrations: Connect to ROCm Libraries and HIP Kernels with ease, removing the need for intricate code rewrites.

Businesses in sectors like weather modeling, pharmaceuticals, and aerospace may now leverage the potential of GPU acceleration without requiring the kind of substantial code overhauls that were previously necessary to future-proof their older HPC systems. This comprehensive tutorial will help you get started with the AMD Fortran Compiler on AMD Instinct GPUs.

New Multi-Node FFT in rocFFT: Game changer for HPC Workflows

Distributed computing systems that scale well are necessary for industries that depend on HPC workloads, such as oil and gas and climate modeling. High-performance distributed FFT calculations are made possible by ROCm 6.3, which adds multi-node FFT functionality to rocFFT.

The Significance of It for HPC

The integration of the built-in Message Passing Interface (MPI) streamlines multi-node scalability, lowering developer complexity and hastening the deployment of distributed applications.

Scalability of Leadership: Optimize performance for crucial activities like climate modeling and seismic imaging by scaling fluidly over large datasets.

Larger datasets may now be processed more efficiently by organizations in sectors like scientific research and oil and gas, resulting in quicker and more accurate decision-making.

Enhanced Computer Vision Libraries: AV1, rocJPEG, and Beyond 

AI developers need effective preprocessing and augmentation tools when dealing with contemporary media and datasets. With improvements to its computer vision libraries, rocDecode, rocJPEG, and rocAL, ROCm 6.3 enables businesses to take on a variety of tasks, from dataset augmentation to video analytics.

Why It Is Important to You

Support for the AV1 Codec: rocDecode and rocPyDecode provide affordable, royalty-free decoding for contemporary media processing.

GPU-Accelerated JPEG Decoding: Use the rocJPEG library’s built-in fallback methods to perform image preparation at scale with ease.

Better Audio Augmentation: Using the rocAL package, preprocessing has been enhanced for reliable model training in noisy situations.

From entertainment and media to self-governing systems, these characteristics allow engineers to produce more complex AI solutions for practical uses.

It’s important to note that, in addition to these noteworthy improvements, Omnitrace and Omniperf which were first released in ROCm 6.2 have been renamed as ROCm System Profiler and ROCm Compute Profiler. Improved usability, reliability, and smooth integration into the existing ROCm profiling environment are all benefits of this rebranding.

Why ROCm 6.3?

AMD With each release, ROCm has advanced, and version 6.3 is no different. It offers state-of-the-art tools to streamline development and improve speed and scalability for workloads including AI and HPC. ROCm enables companies to innovate more quickly, grow more intelligently, and maintain an advantage in cutthroat markets by adopting the open-source philosophy and constantly changing to satisfy developer demands.

Are You Prepared to Jump? Examine ROCm 6.3‘s full potential and discover how AMD Instinct accelerators may support the next significant innovation in your company.

Read more on Govindhtech.com

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Octane Render 2018 (Free Download)

Octane Render is a very helpful and best GPU-accelerated and physical accurate renderer. Octanerender can be used for create image sof the maximum possible quality.

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Game changer for investment management: Alternative data and GPU-accelerated analytics

Game changer for investment management: Alternative data and GPU-accelerated analytics

Presented by OmniSci

Conventional financial data, in many respects, is a two-dimensional view of the investment world. It provides useful facts, but without perspective. What investment professionals increasingly seek is a third dimension that captures a business in all its dynamic reality — a process, in the analytics world, that only comes when multiple forms of data are connected together,…

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Download Sony Vegas Pro 14 Full Crack 32/64 bit – Google Drive

Sony VEGAS Pro 14 Full Crack: là chỉnh sửa video và âm thanh cốt lõi cùng với khả năng ghi và ghi đĩa linh ho��t, ổn định hình ảnh topnotch và các tùy chọn chuẩn độ bổ sung để cung cấp một sức mạnh sản xuất tuyệt vời. Các tính năng chuyên nghiệp nâng cao đặt thế giới của các khả năng sáng tạo hoàn toàn mở ra cho tầm nhìn của bạn. Các khả năng của HEVC và ProRes bản địa hỗ trợ các quy trình công việc hiện đại và các plugin video thông minh dễ dàng chuẩn bị các cảnh quay HD để sử dụng trong các dự án 4K. Cứu hộ các cảnh quay không thể sử dụng với ổn định hình ảnh và thêm các tiêu đề chuyên nghiệp để tạo ra kết quả chuyên nghiệp. Tác giả và ghi đĩa DVD và Bluray với các menu và hành động đầy đủ cũng như kiểm soát tốt tất cả các cài đặt mã hóa

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The post Download Sony Vegas Pro 14 Full Crack 32/64 bit – Google Drive appeared first on CongPhanMem.Com.

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[holytyphoonmusic-blog1]

GPU-Driven Applications Demand Optimized Cooling

Rapid advances in artificial intelligence (AI) are being enabled by the graphics processing units (GPUs) Nvidia originally designed for the video game market. Organizations seeking to capitalize on the capabilities of Nvidia GPUs must prepare their data centers for extreme power density and the heat that comes with it. 

Nvidia revolutionized computer gaming with its GPU. These specialized circuits are able to produce cleaner, faster and smoother motion in video games by performing multiple mathematical calculations simultaneously. Then, in 2007, Nvidia moved beyond the gaming market when it pioneered the concept of “GPU-accelerated computing.” GPUs are combined with traditional computer processing units (CPUs) in massively parallel processing environments that make compute-intensive programs run faster. That development provided the processing “oomph” required to enable essential AI functions such as deep learning. Deep learning is a computing model designed to loosely mimic the way the human brain works with neurons and synapses. Nvidia’s GPUs are used to create so-called “artificial neural networks” that use a large number of highly interconnected nodes working in unison to analyze large datasets. This gives a machine the ability to discover patterns or trends and learn from those discoveries. This the essence of artificial intelligence. Key architectural differences between a CPU and Nvidia’s GPU make this possible. A CPU has a few cores with lots of cache memory that can handle a few software threads at a time. CPUs are also optimized for sequential processing — the execution of processes in the order they are received. GPUs have hundreds of cores that can handle thousands of threads and execute multiple processes simultaneously. GPU-accelerated computing can run some software 100 times faster than with a CPU alone. That makes it perfect for the deep learning type of algorithms that are powering a range of AI applications. GPUs also bring significant challenges to the data center environment. While CPUs have steadily become more energy-efficient, GPUs consume a lot of power. The adoption of GPUaccelerated computing leads to higher power density in the data center — on the order of 30kW to 40kW per rack by some estimates. Many hyperscale data centers are only consuming about 10kW per rack. Power densities of that magnitude mean significantly greater heat loads, which few environments are prepared to handle. Hot-aisle containment is essential, along with in-row cooling systems that focus their capacity on nearby equipment. In-row cooling captures and neutralizes hot exhaust air before it can escape into the data center.

Chilled-water cooling systems are often recommended for GPU-accelerated computing because water has about four times the thermal capacity of air. However, in-row cooling provides greater efficiency by shortening the airflow path and reducing the volume of space to be cooled. Enconnex in-row cooling units give you the flexibility to choose the coolant of your choice. Available in condensate water, chilled water and DX air- and water-cooled configurations, Enconnex in-row cooling units deliver more than 100kW of cooling capacity yet fit comfortably in any data center environment. Nvidia GPUs are being used to accelerate hundreds of AI-driven applications for uses such as quantum chemistry, fluid dynamics, video editing and medical imaging. Organizations looking to take advantage of AI must ensure that their data center infrastructures can handle the heat generated by these powerful chips. Learn more https://goo.gl/9VHyxb

[govindhtech]

NVIDIA CuPyNumeric Allows Scientists To Use GPU Acceleration

NVIDIA cuPyNumeric

Introducing multi-GPU and multi-node (MGMN) accelerated computing with zero-code-change scalability.

Researchers and scientists utilize Python, a strong and intuitive programming language, extensively for data science, machine learning (ML), and efficient numerical computation. The de facto standard math and matrix library is NumPy, which offers a straightforward and user-friendly programming paradigm with interfaces that closely match the mathematical requirements of scientific applications.

CPU-based Python and NumPy applications require assistance in order to satisfy the speed and scalability requirements of cutting-edge research as data quantities and computational complexity increase.

An infrastructure for effectively addressing and testing theories in data-driven challenges is provided by distributed accelerated computing. Researchers are increasingly looking for ways to easily scale their programs, whether they are creating ML models, developing novel approaches to handle intricate computational fluid dynamics issues, or evaluating data produced by recording the scattering of high-energy electron beams.

The goal of NVIDIA cuPyNumeric is to provide distributed and accelerated computation on the NVIDIA platform for the Python community by serving as a drop-in replacement library for NumPy. Without worrying about distributed or parallel computing, it enables scientists and researchers to develop their research programs efficiently utilizing the native Python language and well-known tools. Then, without altering the code, cuPyNumeric and Legate may effortlessly scale their applications from single-CPU systems to MGMN supercomputers.

Advantages of NVIDIA cuPyNumeric

Legate’s NVIDIA cuPyNumeric library:

Supports the NumPy interface and the native Python language without limitations.

Scales and speeds up current NumPy workflows transparently offers NumPy a smooth drop-in substitute.

Offers automated acceleration and parallelism for several nodes spanning CPUs and GPUs.

Optimally scales from a single CPU to thousands of GPUs.

Requires few code modifications, enabling scientific activities to be completed more quickly. is openly accessible.

Start using Conda or GitHub.

NVIDIA cuPyNumeric GPU acceleration

Scientists may now utilize GPU acceleration at the cluster scale with to NVIDIA’s cuPyNumeric release

By enabling researchers to easily expand to powerful computing clusters without changing their Python code, the Accelerated Computing Library promotes scientific discovery.

Many scientists face the same problem: they must sift through petabytes of data to find insights that might further their studies, whether they are studying the behaviors of nanoscale electrons or bright galaxies merging millions of light years distant.

Researchers may now easily execute their data-crunching Python scripts on CPU-based laptops, GPU-accelerated workstations, cloud servers, or enormous supercomputers with to the NVIDIA cuPyNumeric accelerated computing library. They will be able to decide on intriguing data points, patterns worth looking into, and experiment modifications more quickly if they can process their data more quickly.

Researchers don’t need to be computer scientists to make the transition to accelerated computing. They may apply cuPyNumeric to pre-existing code or develop code using the well-known NumPy interface, according to performance and scalability best practices.

They may execute their programs on one or hundreds of GPUs with no code modifications after applying cuPyNumeric.

The most recent version of cuPyNumeric, which is now accessible on GitHub and Conda, has enhanced memory scalability, automated resource setting during runtime, and support for the NVIDIA GH200 Grace Hopper Superchip. Additionally, HDF5, a widely used file format in the scientific community that facilitates the effective management of massive, complicated data, is supported.

CuPyNumeric has been used by researchers at the National Payments Corporation of India, Australia National University, UMass Boston, the Center for Turbulence Research at Stanford University, Los Alamos National Laboratory, and the SLAC National Accelerator Laboratory to significantly improve their data analysis workflows.

Less Is More: Limitless GPU Scalability Without Code Changes

Millions of researchers in scientific domains such as astronomy, drug discovery, materials science, and nuclear physics utilize Python, the most popular programming language for data science, machine learning, and numerical computation. The NumPy arithmetic and matrix library, which was downloaded over 300 million times last month, is used by tens of thousands of packages on GitHub. Accelerated computing with cuPyNumeric might be advantageous for all of these applications.

In order to process ever-larger datasets gathered by devices such as electron microscopes, particle colliders, and radio telescopes, many of these scientists create programs that utilize NumPy and operate on a single CPU-only node. This limits the throughput of their methods.

By offering a drop-in substitute for NumPy that can expand to thousands of GPUs, cuPyNumeric assists researchers in keeping up with the increasing volume and complexity of their datasets. When cuPyNumeric scales from a single GPU to a whole supercomputer, no code modifications are needed. Because of this, researchers may easily conduct their studies on any size accelerated computer equipment.

Solving the Big Data Problem, Accelerating Scientific Discovery

Scientists at Stanford University’s SLAC National Accelerator Laboratory, a U.S. Department of Energy lab, have discovered that cuPyNumeric speeds up X-ray research at the Linac Coherent Light Source.

A semiconductor materials research discovery team at SLAC discovered that cuPyNumeric reduced run time from minutes to seconds and six times sped their data analysis application. When the team conducts tests at this highly specialized facility, this speedup enables them to perform critical analysis in simultaneously.

The team expects to find novel material characteristics, communicate discoveries, and publish work faster by making better use of experiment hours.

Other organizations that make use of cuPyNumeric include:

Researchers at Australia National University scaled the Levenberg-Marquardt optimization method to operate on multi-GPU systems at the nation’s National Computational Infrastructure using cuPyNumeric. Although there are several uses for the method, the researchers’ first focus is on large-scale weather and climate models.

Researchers at Los Alamos National Laboratory are using cuPyNumeric to speed up machine learning, computational science, and data science methods. With the help of cuPyNumeric, they will be able to utilize the newly released Venado supercomputer, which has more than 2,500 NVIDIA GH200 Grace Hopper Superchips, more efficiently.

Researchers at the Center for Turbulence Research at Stanford University are utilizing cuPyNumeric to create Python-based computational fluid dynamics solvers that can operate at scale on massive accelerated computer clusters. Complex applications like online training and reinforcement learning are made possible by these solvers’ ability to smoothly combine enormous sets of fluid simulations with well-known machine learning libraries like PyTorch.

A research team at UMass Boston is speeding up linear algebra computations to examine movies of microscopy and calculate the energy released by active materials. The group broke down a matrix with 4,000 columns and 16 million rows using cuPyNumeric.

About 250 million Indians utilize the National Payments Corporation of India’s real-time digital payment system every day, and it is growing internationally. NPCI tracks the transaction pathways between payers and payees using intricate matrix computations. Using existing techniques, processing data for a one-week transaction window on CPU systems takes around five hours.

According to an experiment, using cuPyNumeric to speed up calculations on multi-node NVIDIA DGX systems potentially 50x matrix multiplication. This would allow NPCI to evaluate bigger transaction windows in less than an hour and identify suspected money laundering almost instantly.

Read more on govindhtech.com

[govindhtech]

AMD Instinct MI300X GPU Accelerators With Meta’s Llama 3.2

AMD applauds Meta for their most recent Llama 3.2 release. Llama 3.2 is intended to increase developer productivity by assisting them in creating the experiences of the future and reducing development time, while placing a stronger emphasis on data protection and ethical AI innovation. The focus on flexibility and openness has resulted in a tenfold increase in Llama model downloads this year over last, positioning it as a top option for developers looking for effective, user-friendly AI solutions.

Llama 3.2 and AMD Instinct MI300X GPU Accelerators

The world of multimodal AI models is changing with AMD Instinct MI300X accelerators. One example is Llama 3.2, which has 11B and 90B parameter models. To analyze text and visual data, they need a tremendous amount of processing power and memory capa

AMD and Meta have a long-standing cooperative relationship. Its is still working to improve AI performance for Meta models on all of AMD platforms, including Llama 3.2. AMD partnership with Meta allows Llama 3.2 developers to create novel, highly performant, and power-efficient agentic apps and tailored AI experiences on AI PCs and from the cloud to the edge.

AMD Instinct accelerators offer unrivaled memory capability, as demonstrated by the launch of Llama 3.1 in previous demonstrations. This allows a single server with 8 MI300X GPUs to fit the largest open-source project currently available with 405B parameters in FP16 datatype something that no other 8x GPU platform can accomplish. AMD Instinct MI300X GPUs are now capable of supporting both the latest and next iterations of these multimodal models with exceptional memory economy with the release of Llama 3.2.

By lowering the complexity of distributing memory across multiple devices, this industry-leading memory capacity makes infrastructure management easier. It also allows for quick training, real-time inference, and the smooth handling of large datasets across modalities, such as text and images, without compromising performance or adding network overhead from distributing across multiple servers.

With the powerful memory capabilities of the AMD Instinct MI300X platform, this may result in considerable cost savings, improved performance efficiency, and simpler operations for enterprises.

Throughout crucial phases of the development of Llama 3.2, Meta has also made use of AMD ROCm software and AMD Instinct MI300X accelerators, enhancing their long-standing partnership with AMD and their dedication to an open software approach to AI. AMD’s scalable infrastructure offers open-model flexibility and performance to match closed models, allowing developers to create powerful visual reasoning and understanding applications.

Developers now have Day-0 support for the newest frontier models from Meta on the most recent generation of AMD Instinct MI300X GPUs, with the release of the Llama 3.2 generation of models. This gives developers access to a wider selection of GPU hardware and an open software stack ROCm for future application development.

CPUs from AMD EPYC and Llama 3.2

Nowadays, a lot of AI tasks are executed on CPUs, either alone or in conjunction with GPUs. AMD EPYC processors provide the power and economy needed to power the cutting-edge models created by Meta, such as the recently released Llama 3.2. The rise of SLMs (small language models) is noteworthy, even if the majority of recent attention has been on LLM (long language model) breakthroughs with massive data sets.

These smaller models need far less processing resources, assist reduce risks related to the security and privacy of sensitive data, and may be customized and tailored to particular company datasets. These models are appropriate and well-sized for a variety of corporate and sector-specific applications since they are made to be nimble, efficient, and performant.

The Llama 3.2 version includes new capabilities that are representative of many mass market corporate deployment situations, particularly for clients investigating CPU-based AI solutions. These features include multimodal models and smaller model alternatives.

When consolidating their data center infrastructure, businesses can use the Llama 3.2 models’ leading AMD EPYC processors to achieve compelling performance and efficiency. These processors can also be used to support GPU- or CPU-based deployments for larger AI models, as needed, by utilizing AMD EPYC CPUs and AMD Instinct GPUs.

AMD AI PCs with Radeon and Ryzen powered by Llama 3.2

AMD and Meta have collaborated extensively to optimize the most recent versions of Llama 3.2 for AMD Ryzen AI PCs and AMD Radeon graphics cards, for customers who choose to use it locally on their own PCs. Llama 3.2 may also be run locally on devices accelerated by DirectML AI frameworks built for AMD on AMD AI PCs with AMD GPUs that support DirectML. Through AMD partner LM Studio, Windows users will soon be able to enjoy multimodal Llama 3.2 in an approachable package.

Up to 192 AI accelerators are included in the newest AMD Radeon, graphics cards, the AMD Radeon PRO W7900 Series with up to 48GB and the AMD Radeon RX 7900 Series with up to 24GB. These accelerators can run state-of-the-art models such Llama 3.2-11B Vision. Utilizing the same AMD ROCm 6.2 optimized architecture from the joint venture between AMD and Meta, customers may test the newest models on PCs that have these cards installed right now3.

AMD and Meta: Progress via Partnership

To sum up, AMD is working with Meta to advance generative AI research and make sure developers have everything they need to handle every new release smoothly, including Day-0 support for entire AI portfolio. Llama 3.2’s integration with AMD Ryzen AI, AMD Radeon GPUs, AMD EPYC CPUs, AMD Instinct MI300X GPUs, and AMD ROCm software offers customers a wide range of solution options to power their innovations across cloud, edge, and AI PCs.

Read more on govindhtech.com