Alternatives to CUDA

Mojo - a new language for AI developers. Mojo combines Python's usability with C's performance, unlocking AI hardware programmability and extensibility that is unmatched. Scale Python down to the metal. Programming low-level AI hardware. No C++ or Cuda required. With the most advanced compilers and runtimes, you can harness the full power of your hardware, including the multiple cores, vectors and exotic accelerators. You can achieve performance comparable to C++ and CUDA, without the complexity.

The NVIDIA HPC Software Developer Kit (SDK), includes the proven compilers and libraries, as well as software tools that maximize developer productivity and improve the portability and performance of HPC applications. NVIDIA HPC SDK C and C++, and Fortran compilers allow GPU acceleration of HPC simulation and modeling applications using standard C++ and Fortran, OpenACC® directives and CUDA®. GPU-accelerated math libraries maximize performance for common HPC algorithms. Optimized communications libraries allow standards-based multi-GPU programming and scalable systems programming. Debugging and performance profiling tools make porting and optimizing HPC applications easier. Containerization tools allow for easy deployment on-premises and in the cloud. The HPC SDK supports NVIDIA GPUs, Arm, OpenPOWER or x86 64 CPUs running Linux.

Cloud GPU Service provides GPU computing power and high-performance parallel computing. It is a powerful tool for the IaaS layer that delivers high computing power to deep learning training, scientific computation, graphics and image processors, video encoding/decoding, and other intensive workloads. Improve your business efficiency with high-performance parallel processing. Install your deployment environment quickly using preinstalled driver and GPU images, CUDA and cuDNN, and auto-installed GPU and CUDA drivers. TACO Kit is a computing acceleration engine that Tencent Cloud provides to accelerate distributed training and inference.

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

Software is what transforms a vehicle into a smart machine. Open source software stack NVIDIA DRIVE™, enables developers to quickly build and deploy a variety state-of the-art AV applications. This includes perception, localization, mapping, driver monitoring, planning and control, driver monitoring and natural language processing. DRIVE OS, the foundation of the DRIVE SoftwareStack, is the first secure operating system for accelerated computation. It includes NvMedia to process sensor input, NVIDIACUDA®, libraries for parallel computing implementations that are efficient, NVIDIA TensorRT™ for real time AI inference, as well as other tools and modules for accessing hardware engines. NVIDIA DriveWorks®, a SDK that provides middleware functions over DRIVE OS, is essential for autonomous vehicle development. These include the sensor abstraction layer (SAL), sensor plugins, data recorder and vehicle I/O support.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

NVIDIA®, Parabricks®, is the only GPU accelerated suite of genomic analysis apps that delivers fast, accurate analysis of exomes and genomes for sequencing centres, clinical teams and high-throughput instrument developers. NVIDIA Parabricks provides GPU-accelerated versions of tools used every day by computational biologists and bioinformaticians--enabling significantly faster runtimes, workflow scalability, and lower compute costs. NVIDIA Parabricks can accelerate runtimes in a variety of hardware configurations, from FastQ to Variant Call format (VCF). This is done with NVIDIA Tensor Core GPUs. Genomic researchers will experience acceleration at every step of their analysis workflows - from alignment to sorting and variant calling. The compute time can be accelerated up to 107X when more GPUs are added.

NVIDIA®, Iray®, is an intuitive, physically-based rendering technology that produces photorealistic imagery for interactive or batch rendering workflows. Iray uses AI denoising, CUDA®, NVIDIA OptiX™, and Material Definition Language to generate stunning visuals. It can be paired with the latest NVIDIA RTX™-based hardware. The latest version of Iray adds support for RTX, which includes dedicated ray-tracing-acceleration hardware support (RT Cores) and an advanced acceleration structure to enable real-time ray tracing in your graphics applications. All render modes in the 2019 Iray SDK use NVIDIA's RTX technology. This, combined with AI denoising allows you to create photorealistic renderings in seconds rather than minutes. Tensor Cores, the latest NVIDIA hardware, brings deep learning to final-frame as well as interactive photorealistic renderings.

Multifunctional software allows you to convert, edit, and play video, audio, and DVD files. You can also create your own videos and GIF images with it. You can convert one file at a time, or multiple files simultaneously. It can encode and decode videos on a CUDA enabled graphics card. This allows for fast and high-quality HD and SD video conversion. Quality will not be affected by your video. NVIDIA's CUDA acceleration technology and AMD APP acceleration technology allow you to convert 6X faster and support multi-core processors completely. It is compatible with NVIDIA®, CUDA™, and AMD®. FonePaw Video Converter Ultimate supports CUDA™, AMD® and NVIDIA®. This allows for fast and high-quality HD and SD video conversion. This all-in one video converter can convert video, audio, and DVD files quickly and efficiently. It even allows you to edit them with better results.

NVIDIA Base command manager offers end-to-end management and fast deployment for heterogeneous AI clusters and high-performance computing at the edge, data center and in multi-cloud and hybrid environments. It automates provisioning and management of clusters from a few nodes up to hundreds of thousands of nodes, supports NVIDIA GPU accelerated systems and other systems and enables orchestration using Kubernetes. The platform integrates Kubernetes to orchestrate workloads and provides tools for infrastructure monitoring and workload management. Base Command Manager has been optimized for accelerated computing environments and is suitable for diverse HPC workloads. It is available on NVIDIA DGX Systems and as part the NVIDIA AI enterprise software suite. NVIDIA Base Command Manager allows you to quickly build and manage high-performance Linux clusters for HPC, machine learning and analytics applications.

MATLAB®, a combination of a desktop environment for iterative analysis, design processes, and a programming language that expresses matrix or array mathematics directly, is MATLAB®. It also includes the Live Editor, which allows you to create scripts that combine output, code, and formatted text in an executable notebook. MATLAB toolboxes have been professionally developed, tested and documented. MATLAB apps allow you to see how different algorithms interact with your data. You can repeat the process until you get the results you desire. Then, MATLAB will automatically generate a program to replicate or automate your work. With minor code changes, you can scale your analyses to run on GPUs, clusters, and clouds. You don't need to rewrite any code or learn big-data programming and other out-of-memory methods. Convert MATLAB algorithms automatically to C/C++ and HDL to run on your embedded processor/FPGA/ASIC. Simulink works with MATLAB to support Model-Based Design.

Fortran was designed from the ground up to support computationally intensive applications in engineering and science. You can write code that runs fast and close to the metal with mature and battle-tested libraries and compilers. Fortran is statically and heavily typed. This allows the compiler to catch programming errors early. This allows the compiler generate efficient binary code. Fortran is a small language that is easy to learn and use. It is easy to express most mathematical and arithmetic operations on large arrays by simply writing them out on a whiteboard. Fortran is a natively parallel programming languages that uses intuitive array-like syntax to exchange data between CPUs. It is possible to run almost identical code on one CPU, on a shared memory multicore system or on a distributed-memory HPC system or cloud-based system.

You can build reliable and optimized code to achieve the best results on multiple Server or HPC architectures. This includes the latest compilers and C++ standard, as well as Intel, 64-bit Arm and AMD, OpenPOWER and Nvidia GPU hardware. Arm Forge combines Arm DDT (the leading debugger for efficient, high-performance application debugging), Arm MAP (the trusted performance profiler that provides invaluable optimization advice across native, Python, and HPC codes), and Arm Performance Reports, which provide advanced reporting capabilities. Arm DDT/Arm MAP can also be purchased as standalone products. Arm experts provide full technical support for efficient application development on Linux Server and HPC. Arm DDT is the best debugger for C++, C, and Fortran parallel applications. Arm DDT's intuitive graphical interface makes it easy to detect memory bugs at all scales and divergent behavior. This makes it the most popular debugger in academia, industry, research, and academia.

Arm DDT is the most widely used server and HPC debugger in academia, research, and industry for software engineers and scientists who develop C++, C, Fortran parallel, and threaded programs on CPUs and GPUs, Intel and Arm. Arm DDT is trusted for its ability to detect memory bugs and divergent behavior, enabling it to deliver lightning-fast performance on all scales. Cross-platform support for multiple servers and HPC architectures. Native parallel debugging for Python applications. Market-leading memory debugging. Outstanding C++ debugging support. Complete Fortran debugging support. Offline mode allows you to debug non-interactively. Large data sets can be visualized and handled. Arm DDT is a powerful parallel tool that can be used as a standalone debugger or as part the Arm Forge profile and debug suite. Its intuitive interface graphically allows for automatic detection of memory bugs at all scales and divergent behavior.

Mitsuba 2 is a research-oriented, retargetable rendering system. It is written in portable C++17 and built on top of Enoki. It was developed by EPFL's Realistic Graphics Lab. It can be compiled to many variations, including color handling (RGB and spectral), vectorization (scalar SIMD, CUDA), and differentiable rendering. Mitsuba 2 is composed of a limited number of core libraries and a variety of plugins that implement functionality from materials and light sources to complete rendering algorithm. It aims to maintain scene compatibility with Mitsuba 0.6. It includes a large automated Python test suite. Its development is dependent on continuous integration servers that compile new commits on different operating system using different compilation settings (e.g. debug/release builds, single/double precision etc.

Bright Cluster Manager offers a variety of machine learning frameworks including Torch, Tensorflow and Tensorflow to simplify your deep-learning projects. Bright offers a selection the most popular Machine Learning libraries that can be used to access datasets. These include MLPython and NVIDIA CUDA Deep Neural Network Library (cuDNN), Deep Learning GPU Trainer System (DIGITS), CaffeOnSpark (a Spark package that allows deep learning), and MLPython. Bright makes it easy to find, configure, and deploy all the necessary components to run these deep learning libraries and frameworks. There are over 400MB of Python modules to support machine learning packages. We also include the NVIDIA hardware drivers and CUDA (parallel computer platform API) drivers, CUB(CUDA building blocks), NCCL (library standard collective communication routines).

MediaCoder is a universal media-transcoding software that has been actively developed and maintained since 2005. It combines the most advanced audio/video technologies into a single transcoding solution. You can adjust many parameters to give you full control over your transcoding. Updates and new features are constantly added. MediaCoder may not be the most intuitive tool, but it is a powerful tool that delivers quality and performance. Once you get it, it will be your go-to tool for media transcoding. Convert between most popular audio- and video formats. H.264/H.265 GPU accelerated encoding. (QuickSync. NVENC. CUDA). Ripping BD/DVD/VCD/CD, and capturing from video camera. Various filters can be used to enhance audio and video content. A rich array of transcoding parameters are available for tuning and adjustment. Multi-threaded design with parallel filtering unleashing multicore power Segmental Video Encoding technology allows for better parallelization.

NVIDIA GPU Cloud is a GPU-accelerated cloud platform that is optimized for scientific computing and deep learning. NGC is responsible for a catalogue of fully integrated and optimized deep-learning framework containers that take full benefit of NVIDIA GPUs in single and multi-GPU configurations.

We have all the infrastructure (computers, Frameworks, Cuda) and software (Cuda) you need to train and deploy deep-learning models. You can launch GPU/CPU instances directly from your web browser or automate the process through our Python API.

DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers.

CUDA compatible GPUs (nVidia) can be found in ccminer, an open-source project. It is compatible with both Linux platforms and Windows platforms. This site is designed to share trusted cryptocurrency mining tools. We will compile and sign open-source binaries. These projects are mostly open-source, but may require technical skills to be correctly compiled.

Elastic computing instances with GPU computing accelerations suitable for scenarios such as artificial intelligence (specifically, deep learning and machine-learning), high-performance computing and professional graphics processing. Elastic GPU Service is a complete service that combines both software and hardware. It helps you to flexibly allocate your resources, elastically scale up your system, increase computing power, and reduce the cost of your AI business. It is applicable to scenarios (such a deep learning, video decoding and encoding, video processing and scientific computing, graphical visualisation, and cloud gaming). Elastic GPU Service offers GPU-accelerated computing and ready-to use, scalable GPU computing resource. GPUs are unique in their ability to perform mathematical and geometric computations, particularly floating-point computing and parallel computing. GPUs have 100 times more computing power than their CPU counterparts.

Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend.

A powerful, flexible, intuitive framework for neural networks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be used on multiple GPUs without much effort. Chainer supports a variety of network architectures, including convnets, feed-forward nets, and recurrent nets. It also supports per batch architectures. Forward computation can include any control flow statement of Python without sacrificing the ability to backpropagate. It makes code easy to understand and debug. ChainerRLA is a library that implements several state-of-the art deep reinforcement algorithms. ChainerCVA is a collection that allows you to train and run neural network for computer vision tasks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be run on multiple GPUs without much effort.

NVIDIA's Morpheus AI framework is GPU-accelerated and allows developers to create applications that are optimized for filtering, classifying, and processing large volumes of cybersecurity data. Morpheus uses AI to reduce time and costs associated with identifying and capturing threats and taking action. This brings a new level to security to data centers, clouds, and the edge. Morpheus extends the capabilities of human analysts with generative AI, automating real-time analyses and responses. It produces synthetic data for AI models to train that accurately identify risks and run what-if scenario. Morpheus can be downloaded as open-source software from GitHub by developers who are interested in the latest prerelease features and want to build their own. NVIDIA AI enterprise offers unlimited usage across all clouds, access NVIDIA AI experts and long-term support.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

NVIDIA GRID®, Virtual PC (GRID PC vPC), and Virtual Apps, (GRID Apps vApps), are virtualization solutions that provide a user experience almost identical to a native PC. GRID provides future-proofing for your VDI environment with server-side graphics, comprehensive management and monitoring capabilities. GPU-acceleration delivers the power of GPU acceleration to every virtual machine (VM) in your company, creating an exceptional user experience that allows your IT team to focus on business goals and strategy.

TrinityX, an open-source cluster management system created by ClusterVision to provide 24/7 oversight of High-Performance Computing and Artificial Intelligence environments. It provides a reliable, SLA-compliant system of support, allowing users the freedom to focus on their research, while still managing complex technologies like Linux, SLURM CUDA, InfiniBand Lustre and Open OnDemand. TrinityX simplifies cluster deployment with an intuitive interface that guides users step-bystep in configuring clusters for diverse purposes such as container orchestration, HPC and InfiniBand/RDMA. The BitTorrent protocol enables rapid deployment and setup of AI/HPC Nodes. The platform offers a dashboard that provides real-time insights on cluster metrics, resource usage, and workload distribution. This allows for the identification of bottlenecks, and optimizes resource allocation.

QikkDB is an GPU-accelerated columnar database that delivers outstanding performance for complex polygon operations as well as big data analytics. qikkDB is the best choice if you want to count your data in billions, and see real-time results. We are compatible with both Windows and Linux operating systems. Google Tests is our testing framework. The project contains hundreds of unit and tens integration tests. Microsoft Visual Studio 2019 is recommended for Windows development. Its dependencies include CUDA version 10.2 minimum, CMake 3.15 and newer, vcpkg., boost. The dependencies for Linux development are CUDA version 10.2 minimum, CMake 3.15 and newer, boost, and vcpkg. This project is licensed under Version 2.0 of the Apache License. To install qikkDB, you can use an installation script (or dockerfile).

The New Era of Data Storage has Arrived. Nyriad is combining the power of CPUs and GPUs to achieve unprecedented capacity reliability and security. This disrupts conventional storage architectures. A compression platform that provides advanced data storage services for big-data and high-performance computing. The company's platform uses GPU-accelerated block storage to provide highly resilient data storage. Clients can meet the security, efficiency, and performance requirements of any type or computing project. Nyriad's concept for 'liquid data' is a flow of data through storage, networking, and processing bottlenecks in order to achieve speed, efficiency, and performance. This allows Nyriad to provide cloud support. Nyriad is busy finishing Ambigraph, which will become a significant operating system in exascale computing.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

High-performance computing is the heart of AI, machine learning and deep learning applications. The Intel® oneAPI HPC Toolkit is a toolkit that allows developers to create, analyze, optimize and scale HPC applications using the most recent techniques in vectorization and multithreading, multi-node paralelization, memory optimization, and multi-node parallelization. This toolkit is an extension to the Intel(r] oneAPI Base Toolkit. It is required for full functionality. Access to the Intel(r?) Distribution for Python*, Intel(r] oneAPI DPC++/C++ C compiler, powerful data-centric library and advanced analysis tools are all included. You get everything you need to optimize, test, and build your oneAPI projects. An Intel(r] Developer Cloud account gives you 120 days access to the latest Intel®, hardware, CPUs and GPUs as well as Intel oneAPI tools, frameworks and frameworks. No software downloads. No configuration steps and no installations

Hyperstack, the ultimate self-service GPUaaS Platform, offers the H100 and A100 as well as the L40, and delivers its services to the most promising AI start ups in the world. Hyperstack was built for enterprise-grade GPU acceleration and optimised for AI workloads. NexGen Cloud offers enterprise-grade infrastructure for a wide range of users from SMEs, Blue-Chip corporations to Managed Service Providers and tech enthusiasts. Hyperstack, powered by NVIDIA architecture and running on 100% renewable energy, offers its services up to 75% cheaper than Legacy Cloud Providers. The platform supports diverse high-intensity workloads such as Generative AI and Large Language Modeling, machine learning and rendering.

Your projects will fly with the brand-new NVIDIA RTX 4000, which has 16GB of VRAM, 6144 CUDA Cores, 48RT Cores, and 192 tensor cores. You can get two units for only EUR99 per week and render unlimited clouds. The Polargrid RTX Flat achieved an Octanebench2020.1 result of 855. This free program is designed for Blender Artists with great ideas, but no rendering resources. Polargrid supports the Blender Community by offering this program. This is an investment in the Blender Community. The only restriction is the output resolution. The free service is limited by a frame size 1920 x1080 pixels. Your projects will be rendered on the incredibly fast AMD EPYC 7642 48Core blade systems. Much faster and more reliable that any other Blender cloud service, free or paid. Our new data center, located in Boden, Sweden, runs on green energy.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

Enterprise-class management is available to run distributed compute-intensive and data-intensive applications on a scalable, shared network. IBM Spectrum Symphony®, software provides powerful enterprise-class management to run distributed, compute-intensive and data-intensive applications on a scalable shared grid. It can accelerate dozens of parallel applications to achieve faster results and better utilization. IBM Spectrum Symphony can help you improve IT performance, reduce costs and expenses, and meet your business needs quickly. Accelerate time-to-results by achieving faster throughput and performance in data-intensive and compute-intensive analytics applications. Optimize and control the huge compute power in your technical computing systems to achieve higher resource utilization. You can reduce infrastructure, application development, deployment, and management costs by taking control of large-scale jobs.

TotalView debugging software gives you the specialized tools to quickly analyze, scale, and debug high-performance computing applications (HPC). This includes multicore, parallel, and highly dynamic applications that run on a variety of hardware, from desktops to supercomputers. TotalView's powerful tools allow for faster fault isolation, better memory optimization, and dynamic visualisation to improve HPC development efficiency and time-to market. You can simultaneously debug thousands upon thousands of threads and processes. TotalView is a tool that was specifically designed for parallel and multicore computing. It provides unprecedented control over thread execution and processes, as well as deep insight into program data and program states.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

The NVIDIA®, EGX™, Platform for professional visualization accelerates multiple workloads, from rendering and virtualization to engineering analyses and data science. This flexible reference design combines NVIDIA GPUs with NVIDIA virtual CPU (vGPU), high-performance networking, and high-end graphics. It delivers exceptional graphics and compute power that allows artists and engineers to do their best work from anywhere, at a fraction of the cost, space and power of CPU-based solutions. The EGX Platform and NVIDIA RTX Virtual Workstation software (vWS), can be combined to simplify the deployment of a high performance, cost-effective infrastructure. This solution is certified by industry-leading partners and ISVs on trusted OEM servers. It allows professionals to work remotely, increasing productivity, increasing data center utilization, and reducing IT maintenance and costs.

Cloud GPU servers are a type cloud computing service which provides access to remote servers equipped with Graphics Processing Units. These GPUs are designed for complex, high-speed parallel computations. They perform at a rate much faster than conventional central processor units (CPUs). NVIDIA K40 and K80 GPU models are available. The GPUs offer a variety of computing options to meet your business needs. Nvidia GPU cloud server allows designers to quickly iterate as rendering time is reduced. Your team's productivity will increase significantly if you invest your time in innovation instead of rendering or computing. Data security is ensured by fully isolating the resources allocated to each user. GPU Mart protects from DDoS at the edge while ensuring that legitimate traffic to Nvidia GPU cloud servers is not compromised.

The Intel Distribution of OpenVINO makes it easy to adopt and maintain your code. Open Model Zoo offers optimized, pre-trained models. Model Optimizer API parameters make conversions easier and prepare them for inferencing. The runtime (inference engines) allows you tune for performance by compiling an optimized network and managing inference operations across specific devices. It auto-optimizes by device discovery, load balancencing, inferencing parallelism across CPU and GPU, and many other functions. You can deploy the same application to multiple host processors and accelerators (CPUs. GPUs. VPUs.) and environments (on-premise or in the browser).

Elastic Cloud Server (ECS), which provides secure, scalable and on-demand computing resources, allows you to deploy applications and workloads in a flexible way. Comprehensive security protection that is worry-free. General computing ECSs provide a balanced mix of memory, computing, and network resources. This ECS type is ideal to handle light- and moderate-load applications. Memory-optimized ECSs have large amounts of memory, flexible bandwidths, and support high I/O EVS disks. This ECS type is best suited for large data volumes. Disk-intensive ECSs are for applications that require sequential read/write of large datasets in local storage (such a distributed Hadoop computing), as well as large-scale parallel processing and log processing. Disk-intensive ECSs can be used with HDDs, have a default bandwidth of 10GE and offer high PPS and low latency.

Each GPU contains 16896 CUDA Cores and 528 Tensor cores. This is the current flagship chip from NVidia®, which is unmatched in terms of raw performance for AI operations. We use the SXM5 module of NVLINK, which has a memory bandwidth up to 2.6 Gbps. It also offers 900GB/s bandwidth P2P. Fourth generation AMD Genoa with up to 384 Threads and a boost clock 3.7GHz. We only use the SXM4 "for NVLINK" module, which has a memory bandwidth exceeding 2TB/s as well as a P2P bandwidth up to 600GB/s. Second generation AMD EPYC Rome with up to 192 Threads and a boost clock 3.3GHz. The name 8A100.176V consists of 8x RTX, 176 CPU cores threads and virtualized. It is faster at processing tensor operations than the V100 despite having fewer tensors. This is due to its different architecture. Second generation AMD EPYC Rome with up to 96 threads and a boost clock speed of 3.35GHz.

Metariver Technology Co., Ltd. develops innovative and creative computer-aided engineering (CAE) analysis S/W based upon the most recent HPC technology and S/W technologies including CUDA technology. We are changing the paradigm in CAE technology by using particle-based CAE technology, high-speed computation technology with GPUs, and CAE analysis software. Here is an introduction to our products. 1. Samadii-DEM: works with discrete element method and solid particles. 2. Samadii-SCIV (Statistical Contact In Vacuum): working with high vacuum system gas-flow simulation. 3. Samadii-EM (Electromagnetics) : For full-field interpretation 4. Samadii-Plasma: For Analysis of ion and electron behavior in an electromagnetic field. 5. Vampire (Virtual Additive Manufacturing System): Specializes in transient heat transfer analysis.

High-end accelerators and processors such as Graphic Processing Units (GPU) are best for data-intensive AI, IoT, and HPC workloads that require multiple parallel processes. Businesses and research organizations have had the to make compromises when running compute-intensive workloads using cloud-based solutions. Cloud environments can be incompatible with new applications, or require high energy consumption levels. This can raise concerns about the environment. Other times, some aspects of cloud solutions are just too difficult to use. This makes it difficult to create custom cloud environments that meet business needs.

Aimersoft Video Converter is the fastest video converter for Mac or Windows. It has been tested with more than 10,000 files and shows a remarkable 90X faster conversion speed. The fast file converter supports many media formats and preserves original quality in HD or Ultra HD. Aimersoft Video Converter is optimized with APEXTRANS™, NVIDIA® CUDA, Intel(r] Core™ and AMD®. This technology speeds up conversion up to 90X faster that regular quick video converters. The ultra-fast video converter ensures high quality output. Aimersoft Video Converter supports many video and audio formats. These include MP4, MOV and WMV, MKV and FLV. This super video converter allows you to convert video to fit into your portable media players for easy playing or further editing.

Bodo's powerful parallel computing engine and powerful compute engine provide efficient execution and effective scaling, even for 10,000+ cores or PBs of data. Bodo makes it easier to develop and maintain data science, data engineering, and ML workloads using standard Python APIs such as Pandas. End-to-end compilation prevents frequent failures and catches errors before they reach production. With Python's simplicity, you can experiment faster with large datasets from your laptop. Produce production-ready code without having to refactor for scaling large infrastructure.

Clara's domain specific tools, AI pretrained models, accelerated applications, and accelerated AI applications are enabling AI advances in many fields, including medical device, imaging, drug discovery and genomics. Holoscan allows you to explore the entire pipeline of medical device deployment and development. With the NVIDIA IGX Developer Kits, you can build containerized AI apps using the Holoscan SDK. The NVIDIA IGX SDK includes pre-trained AI model, healthcare-specific acceleration libraries and reference applications for medical devices.

The Microsoft Cognitive Toolkit is an open-source toolkit that allows commercial-grade distributed deep-learning. It describes neural networks using a directed graph, which is a series of computational steps. CNTK makes it easy to combine popular models such as feed-forward DNNs (CNNs), convolutional neural network (CNNs), and recurrent neural network (RNNs/LSTMs) with ease. CNTK implements stochastic grade descent (SGD, error-backpropagation) learning with automatic differentiation/parallelization across multiple GPUs or servers. CNTK can be used in your Python, C# or C++ programs or as a standalone machine learning tool via its own model description language (BrainScript). You can also use the CNTK model assessment functionality in your Java programs. CNTK is compatible with 64-bit Linux and 64-bit Windows operating system. You have two options to install CNTK: you can choose pre-compiled binary packages or you can compile the toolkit using the source available in GitHub.