Alternatives to RunMat

MATLAB® offers a desktop environment specifically optimized for iterative design and analysis, paired with a programming language that allows for straightforward expression of matrix and array mathematics. It features the Live Editor, which enables users to create scripts that merge code, output, and formatted text within an interactive notebook. The toolboxes provided by MATLAB are meticulously developed, thoroughly tested, and comprehensively documented. Additionally, MATLAB applications allow users to visualize how various algorithms interact with their data. You can refine your results through repeated iterations and then easily generate a MATLAB program to replicate or automate your processes. The platform also allows for scaling analyses across clusters, GPUs, and cloud environments with minimal modifications to your existing code. There is no need to overhaul your programming practices or master complex big data techniques. You can automatically convert MATLAB algorithms into C/C++, HDL, and CUDA code, enabling execution on embedded processors or FPGA/ASIC systems. Furthermore, when used in conjunction with Simulink, MATLAB enhances the support for Model-Based Design methodologies, making it a versatile tool for engineers and researchers alike. This adaptability makes MATLAB an essential resource for tackling a wide range of computational challenges.

Simr (formerly UberCloud) is revolutionizing the world of simulation operations with our flagship solution, Simulation Operations Automation (SimOps). Designed to streamline and automate complex simulation workflows, Simr enhances productivity, collaboration, and efficiency for engineers and scientists across various industries, including automotive, aerospace, biomedical engineering, defense, and consumer electronics. Our cloud-based infrastructure provides scalable and cost-effective solutions, eliminating the need for significant upfront investments in hardware. This ensures that our clients have access to the computational power they need, exactly when they need it, leading to reduced costs and improved operational efficiency. Simr is trusted by some of the world's leading companies, including three of the seven most successful companies globally. One of our notable success stories is BorgWarner, a Tier 1 automotive supplier that leverages Simr to automate its simulation environments, significantly enhancing their efficiency and driving innovation.

YAKINDU Model Viewer (YMV) is a specialized tool for displaying models made with MATLAB Simulink, presenting block diagrams that closely resemble those in Simulink. This viewer offers users the ability to efficiently explore, navigate, and search through extensive and intricate models. With its browser-like navigation capabilities, users can swiftly delve into the system hierarchy. Additionally, YMV boasts advanced visualization options, signal tracing, requirements tracking, and gesture-based interactions, among other features. The tool includes multiple perspectives to display a model's structure and the characteristics of its components, enhancing the user experience. Overall, YAKINDU Model Viewer simplifies the process of understanding complex systems through its intuitive design and comprehensive functionality.

Quickly set up a virtual machine on Google Cloud for your deep learning project using the Deep Learning VM Image, which simplifies the process of launching a VM with essential AI frameworks on Google Compute Engine. This solution allows you to initiate Compute Engine instances that come equipped with popular libraries such as TensorFlow, PyTorch, and scikit-learn, eliminating concerns over software compatibility. Additionally, you have the flexibility to incorporate Cloud GPU and Cloud TPU support effortlessly. The Deep Learning VM Image is designed to support both the latest and most widely used machine learning frameworks, ensuring you have access to cutting-edge tools like TensorFlow and PyTorch. To enhance the speed of your model training and deployment, these images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers, as well as the Intel® Math Kernel Library. By using this service, you can hit the ground running with all necessary frameworks, libraries, and drivers pre-installed and validated for compatibility. Furthermore, the Deep Learning VM Image provides a smooth notebook experience through its integrated support for JupyterLab, facilitating an efficient workflow for your data science tasks. This combination of features makes it an ideal solution for both beginners and experienced practitioners in the field of machine learning.

Effortlessly combine COMSOL Multiphysics® with MATLAB® to broaden your modeling capabilities through scripting within the MATLAB framework. The LiveLink™ for MATLAB® feature empowers you to access the comprehensive functionalities of MATLAB and its various toolboxes for tasks such as preprocessing, model adjustments, and postprocessing. Elevate your custom MATLAB scripts by integrating robust multiphysics simulations. You can base your geometric modeling on either probabilistic elements or image data. Furthermore, leverage multiphysics models alongside Monte Carlo simulations and genetic algorithms for enhanced analysis. Exporting COMSOL models in a state-space matrix format allows for their integration into control systems seamlessly. The COMSOL Desktop® interface facilitates the utilization of MATLAB® functions during your modeling processes. You can also manipulate your models via command line or scripts, enabling you to parameterize aspects such as geometry, physics, and the solution approach, thus boosting the efficiency and flexibility of your simulations. This integration ultimately provides a powerful platform for conducting complex analyses and generating insightful results.

Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.

Develop virtualization solutions utilizing a minimalistic hypervisor that operates without the need for any external kernel extensions. The hypervisor offers C APIs, allowing for interaction with virtualization technologies directly in user space, eliminating the necessity of writing kernel extensions (KEXTs). Consequently, the applications designed with this framework can be distributed through the Mac App Store. Leverage this framework to create and manage hardware-accelerated virtual machines and virtual processors (VMs and vCPUs) from your authorized, sandboxed user-space application. The Hypervisor simplifies the concept of virtual machines as processes and treats virtual processors as threads. It is important to note that the Hypervisor framework relies on hardware capabilities to virtualize resources efficiently. For Apple silicon, this entails support for the Virtualization Extensions, while for Intel-based Macs, it necessitates systems equipped with an Intel VT-x feature set that includes Extended Page Tables (EPT) and Unrestricted Mode. This ensures the framework is optimized for performance and security across various hardware configurations.

At the core of every embedded operating system lies a kernel, which plays a crucial role in task scheduling and multitasking to guarantee that the timing demands of your application code are fulfilled, even as you frequently enhance and modify that code with new functionalities. However, Micrium OS offers more than just a kernel; it includes a variety of supplementary modules designed to assist you in addressing the specific requirements of your project. Furthermore, Micrium OS is available completely free for use on Silicon Labs EFM32 and EFR32 devices, allowing you to integrate Micrium’s high-quality components into your projects today without incurring any licensing costs. This accessibility encourages innovation and experimentation, ensuring that developers can focus on creating robust applications without the worry of financial constraints.

Unsloth is an innovative open-source platform specifically crafted to enhance and expedite the fine-tuning and training process of Large Language Models (LLMs). This platform empowers users to develop customized models, such as ChatGPT, in just a single day, a remarkable reduction from the usual training time of 30 days, achieving speeds that can be up to 30 times faster than Flash Attention 2 (FA2) while significantly utilizing 90% less memory. It supports advanced fine-tuning methods like LoRA and QLoRA, facilitating effective customization for models including Mistral, Gemma, and Llama across its various versions. The impressive efficiency of Unsloth arises from the meticulous derivation of computationally demanding mathematical processes and the hand-coding of GPU kernels, which leads to substantial performance enhancements without necessitating any hardware upgrades. On a single GPU, Unsloth provides a tenfold increase in processing speed and can achieve up to 32 times improvement on multi-GPU setups compared to FA2, with its functionality extending to a range of NVIDIA GPUs from Tesla T4 to H100, while also being portable to AMD and Intel graphics cards. This versatility ensures that a wide array of users can take full advantage of Unsloth's capabilities, making it a compelling choice for those looking to push the boundaries of model training efficiency.

LXC serves as a user-space interface that harnesses the Linux kernel's containment capabilities. It provides a robust API along with straightforward tools, enabling Linux users to effortlessly create and oversee both system and application containers. Often viewed as a hybrid between a chroot environment and a complete virtual machine, LXC aims to deliver an experience closely resembling a typical Linux installation without necessitating an independent kernel. This makes it an appealing option for developers needing lightweight isolation. As a free software project, the majority of LXC's code is distributed under the GNU LGPLv2.1+ license, while certain components for Android compatibility are available under a standard 2-clause BSD license, and some binaries and templates fall under the GNU GPLv2 license. The stability of LXC's releases is dependent on the various Linux distributions and their dedication to implementing timely fixes and security patches. Consequently, users can rely on the continuous improvement and security of their container environments through active community support.

Homebrew Cask provides an elegant command-line interface (CLI) workflow for managing macOS applications that are distributed as binaries. By extending the capabilities of Homebrew, it offers a straightforward and efficient way to install and manage GUI applications like Atom and Google Chrome. To get started with Homebrew Cask, you only need to have Homebrew installed on your system. It facilitates the installation of macOS applications, fonts, plugins, and other proprietary software. Homebrew Cask functions as an integral component of Homebrew itself, with all commands beginning with "brew," which is applicable to both Casks and Formulae. You can use the command "brew install" to add one or more Cask tokens at once. Additionally, Homebrew Cask supports bash and zsh completion for the brew command, enhancing its usability. Since the Homebrew Cask repository operates as a Homebrew Tap, users can quickly download the latest Casks by running the standard "brew update" command, ensuring that they always have access to the most current applications available. This streamlined process not only saves time but also makes application management much more efficient for macOS users.

Minoca OS is a versatile, open-source operating system tailored for advanced embedded devices. It combines the expected high-level features of an OS while significantly reducing the memory usage. By utilizing a driver API that decouples device drivers from the kernel, it ensures that driver binaries remain compatible across kernel updates. This separation of drivers facilitates dynamic loading and unloading based on demand. The hardware layer API creates a cohesive kernel, eliminating the need for a separate kernel fork, even on ARM architecture. Additionally, a unified power management system enables more intelligent energy-saving decisions, ultimately enhancing battery longevity. With fewer background processes and reduced wake-ups from idle states, devices can enter deeper power-saving modes, thereby optimizing energy consumption further. The availability of both proprietary and non-GPL source licenses provides flexibility for customers and end-users, ensuring a broad range of options for deployment. This adaptability makes Minoca OS an appealing choice for developers seeking efficiency and performance in embedded systems.

TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.

QEMU serves as a versatile and open-source machine emulator and virtualizer, allowing users to operate various operating systems across different architectures. It enables execution of applications designed for other Linux or BSD systems on any supported architecture. Moreover, it supports running KVM and Xen virtual machines with performance that closely resembles native execution. Recently, features like complete guest memory dumps, pre-copy/post-copy migration, and background guest snapshots have been introduced. Additionally, there is new support for the DEVICE_UNPLUG_GUEST_ERROR to identify hotplug failures reported by guests. For macOS users with Apple Silicon CPUs, the ‘hvf’ accelerator is now available for AArch64 guest support. The M-profile MVE extension is also now integrated for the Cortex-M55 processor. Furthermore, AMD SEV guests can now measure the kernel binary during direct kernel boot without utilizing a bootloader. Enhanced compatibility has been added for vhost-user and NUMA memory options, which are now available across all supported boards. This expansion of features reflects QEMU's commitment to providing robust virtualization solutions that cater to a wide range of user needs.

Collimator is a simulation and modeling platform for hybrid dynamical system. Engineers can design and test complex, mission-critical systems in a reliable, secure, fast, and intuitive way with Collimator. Our customers are control system engineers from the electrical, mechanical, and control sectors. They use Collimator to improve productivity, performance, and collaborate more effectively. Our out-of-the-box features include an intuitive block diagram editor, Python blocks for developing custom algorithms, Jupyter notebooks for optimizing their systems, high performance computing in cloud, and role-based access controls.

CUDA® is a powerful parallel computing platform and programming framework created by NVIDIA, designed for executing general computing tasks on graphics processing units (GPUs). By utilizing CUDA, developers can significantly enhance the performance of their computing applications by leveraging the immense capabilities of GPUs. In applications that are GPU-accelerated, the sequential components of the workload are handled by the CPU, which excels in single-threaded tasks, while the more compute-heavy segments are processed simultaneously across thousands of GPU cores. When working with CUDA, programmers can use familiar languages such as C, C++, Fortran, Python, and MATLAB, incorporating parallelism through a concise set of specialized keywords. NVIDIA’s CUDA Toolkit equips developers with all the essential tools needed to create GPU-accelerated applications. This comprehensive toolkit encompasses GPU-accelerated libraries, an efficient compiler, various development tools, and the CUDA runtime, making it easier to optimize and deploy high-performance computing solutions. Additionally, the versatility of the toolkit allows for a wide range of applications, from scientific computing to graphics rendering, showcasing its adaptability in diverse fields.

WebAssembly, commonly referred to as Wasm, is a binary instruction format intended for a stack-based virtual machine. It serves as a portable compilation target for various programming languages, which facilitates the deployment of applications on the web for both client-side and server-side use. The design of the Wasm stack machine emphasizes efficiency in size and load time, utilizing a binary format that promotes quick execution. By leveraging prevalent hardware capabilities, WebAssembly aims to achieve performance that is comparable to native speed across numerous platforms. WebAssembly also establishes a memory-safe and sandboxed execution environment that can be integrated into existing JavaScript virtual machines, thus expanding its versatility. When utilized within web environments, WebAssembly adheres to the browser's same-origin and permissions security protocols, ensuring a safe execution context. Additionally, WebAssembly provides a pretty-printed textual format that is beneficial for debugging, testing, and learning, allowing developers to experiment and optimize their code easily. This textual representation will also be accessible when examining the source of Wasm modules on the web, making it easier for programmers to engage directly with their code. By fostering such accessibility, WebAssembly encourages a deeper understanding of how web applications function at a fundamental level.

CodeMetal is an advanced platform that leverages AI for code translation and deployment, enabling engineering teams to seamlessly transform high-level reference code into optimized implementations suited for edge and embedded systems. Developers can utilize familiar programming languages like Python, MATLAB, or Julia, and the platform automatically produces low-level code adapted to the specific runtime environment, which may include embedded C/C++, Rust, CUDA, or FPGA languages. Its intelligent workflow assesses module dependencies, identifies architectural equivalents, and generates a comprehensive transpilation and deployment strategy that developers can either review or implement immediately. By focusing on verifiable AI, CodeMetal integrates generative methods with formal verification processes to ensure the translated code is rigorously tested, compliant with standards, and ready for production use, thereby addressing reliability issues often faced in safety-critical sectors. This commitment to quality and safety makes CodeMetal an invaluable tool for developers working in demanding environments.

The VLFeat open source library offers a range of well-known algorithms focused on computer vision, particularly for tasks such as image comprehension and the extraction and matching of local features. Among its various algorithms are Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, the agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, and large scale SVM training, among many others. Developed in C to ensure high performance and broad compatibility, it also has MATLAB interfaces that enhance user accessibility, complemented by thorough documentation. This library is compatible with operating systems including Windows, Mac OS X, and Linux, making it widely usable across different platforms. Additionally, MatConvNet serves as a MATLAB toolbox designed specifically for implementing Convolutional Neural Networks (CNNs) tailored for various computer vision applications. Known for its simplicity and efficiency, MatConvNet is capable of running and training cutting-edge CNNs, with numerous pre-trained models available for tasks such as image classification, segmentation, face detection, and text recognition. The combination of these tools provides a robust framework for researchers and developers in the field of computer vision.

KVM, which stands for Kernel-based Virtual Machine, serves as a comprehensive virtualization solution for Linux systems operating on x86 hardware equipped with virtualization capabilities (such as Intel VT or AMD-V). It comprises a loadable kernel module, known as kvm.ko, that underpins the essential virtualization framework, along with a processor-specific module, either kvm-intel.ko or kvm-amd.ko. By utilizing KVM, users can operate several virtual machines that run unaltered Linux or Windows operating systems. Each virtual machine is allocated its own set of virtualized hardware components, including a network interface card, storage, graphics adapter, and more. KVM is an open-source project, with its kernel component integrated into the mainline Linux kernel since version 2.6.20, while the userspace aspect has been incorporated into the mainline QEMU project starting from version 1.3. This integration enables widespread deployment and support for various virtualization applications and services.

NVIDIA TensorRT is a comprehensive suite of APIs designed for efficient deep learning inference, which includes a runtime for inference and model optimization tools that ensure minimal latency and maximum throughput in production scenarios. Leveraging the CUDA parallel programming architecture, TensorRT enhances neural network models from all leading frameworks, adjusting them for reduced precision while maintaining high accuracy, and facilitating their deployment across a variety of platforms including hyperscale data centers, workstations, laptops, and edge devices. It utilizes advanced techniques like quantization, fusion of layers and tensors, and precise kernel tuning applicable to all NVIDIA GPU types, ranging from edge devices to powerful data centers. Additionally, the TensorRT ecosystem features TensorRT-LLM, an open-source library designed to accelerate and refine the inference capabilities of contemporary large language models on the NVIDIA AI platform, allowing developers to test and modify new LLMs efficiently through a user-friendly Python API. This innovative approach not only enhances performance but also encourages rapid experimentation and adaptation in the evolving landscape of AI applications.

The Intel® Tiber™ AI Cloud serves as a robust platform tailored to efficiently scale artificial intelligence workloads through cutting-edge computing capabilities. Featuring specialized AI hardware, including the Intel Gaudi AI Processor and Max Series GPUs, it enhances the processes of model training, inference, and deployment. Aimed at enterprise-level applications, this cloud offering allows developers to create and refine models using well-known libraries such as PyTorch. Additionally, with a variety of deployment choices, secure private cloud options, and dedicated expert assistance, Intel Tiber™ guarantees smooth integration and rapid deployment while boosting model performance significantly. This comprehensive solution is ideal for organizations looking to harness the full potential of AI technologies.

CoppeliaSim, created by Coppelia Robotics, stands out as a dynamic and robust platform for robot simulation, effectively serving various purposes such as rapid algorithm development, factory automation modeling, quick prototyping, verification processes, educational applications in robotics, remote monitoring capabilities, safety checks, and the creation of digital twins. Its architecture supports distributed control, allowing for individual management of objects and models through embedded scripts in Python or Lua, plugins written in C/C++, and remote API clients that support multiple programming languages including Java, MATLAB, Octave, C, C++, and Rust, as well as tailored solutions. The simulator is compatible with five different physics engines—MuJoCo, Bullet Physics, ODE, Newton, and Vortex Dynamics—enabling swift and customizable dynamics calculations that facilitate highly realistic simulations of physical phenomena and interactions, such as collision responses, grasping mechanisms, and the behavior of soft bodies, strings, ropes, and fabrics. Additionally, CoppeliaSim offers both forward and inverse kinematics computations for a diverse range of mechanical systems, enhancing its utility in various robotics applications. This flexibility and capability make CoppeliaSim an essential tool for researchers and professionals in the field of robotics.

Homebrew serves as the missing package manager for macOS and Linux, providing a script that outlines its intended actions before executing them. It effectively installs software that Apple or your Linux distribution may not provide by default, placing packages in dedicated directories and creating symlinks in /usr/local for macOS Intel systems. This package manager ensures that installations remain within its designated prefix, allowing for flexible placement of Homebrew installations. Users can easily create their own Homebrew packages, as the underlying technology involves Git and Ruby, which facilitates simple reversion of changes and merging of updates. Homebrew formulas are straightforward Ruby scripts that enhance the functionality of macOS or Linux systems. Furthermore, RubyGems can be installed using the gem command, while Homebrew manages their dependencies through the brew command. For macOS users, Homebrew Cask enables the installation of applications, fonts, and plugins, including proprietary software, with the process of creating a cask being as easy as writing a formula. This simplicity encourages users to explore and customize their software environment further.

Bayesforge™ is a specialized Linux machine image designed to assemble top-tier open source applications tailored for data scientists in need of sophisticated analytical tools, as well as for professionals in quantum computing and computational mathematics who wish to engage with key quantum computing frameworks. This image integrates well-known machine learning libraries like PyTorch and TensorFlow alongside open source tools from D-Wave, Rigetti, and platforms like IBM Quantum Experience and Google’s innovative quantum language Cirq, in addition to other leading quantum computing frameworks. For example, it features our quantum fog modeling framework and the versatile quantum compiler Qubiter, which supports cross-compilation across all significant architectures. Users can conveniently access all software through the Jupyter WebUI, which features a modular design that enables coding in Python, R, and Octave, enhancing flexibility in project development. Moreover, this comprehensive environment empowers researchers and developers to seamlessly blend classical and quantum computing techniques in their workflows.

Lguest enables the operation of several instances of a 32-bit kernel simultaneously; by using the command modprobe lg, you can initiate it by running Documentation/lguest/lguest to establish a new guest. I encourage you to experiment with it as lguest is exceedingly straightforward to set up. Its utility is significant: I can boot kernels for testing purposes in less than a second, which is approximately ten times quicker than standard qemu and a hundred times faster than a traditional boot process. Moreover, since it employs a pty for the console, you're able to perform actions such as piping the output through grep. Lguest comprises a comprehensive kernel patch, which includes the launcher and is available in versions 2.6.23-git13 and later. The primary goal of lguest is to keep the guest isolated, preventing it from accessing the host directly, aside from virtual devices provided by the host, even if the guest is acting maliciously. Nevertheless, a potentially harmful guest kernel has the capability to pin host memory, limited to the volume allocated to the guest. While most images are configured to create virtual consoles like (/dev/tty0, etc.), the console for lguest is designated as /dev/hvc0, which adds a layer of distinction to its functionality. Additionally, this makes lguest a practical tool for developers who want to test kernel changes in a rapid and efficient manner without the overhead of a full virtualization solution.

Ansys Sherlock stands out as the sole reliability physics-based tool for electronics design that delivers quick and precise life expectancy assessments for electronic components, boards, and systems during the initial design phases. By automating the design analysis process, Ansys Sherlock enables the rapid generation of life predictions, thus eliminating the "test-fail-fix-repeat" cycle that often hampers development. Designers can effectively model the interactions between silicon–metal layers, semiconductor packaging, printed circuit boards (PCBs), and assemblies, allowing for accurate predictions of potential failure risks stemming from thermal, mechanical, and manufacturing stresses, all prior to creating prototypes. Additionally, Sherlock's extensive libraries, which house over 500,000 components, facilitate the seamless transformation of electronic computer-aided design (ECAD) files into computational fluid dynamics (CFD) and finite element analysis (FEA) models. Each of these models is equipped with precise geometries and material properties, ensuring that stress information is accurately conveyed for reliable predictions. This capability not only enhances design efficiency but also significantly reduces the risk of costly errors in the later stages of product development.

Dive CAE is a cloud-based software platform designed for computational fluid dynamics that empowers engineers to model intricate fluid dynamics phenomena, including free-surface flows, multiphase interactions, heat transfer, and the dynamics of moving machinery, all through a mesh-free Smoothed Particle Hydrodynamics approach. Accessible directly from a web browser and optimized for high-performance computing systems, it eliminates the need for local hardware or installation processes. This innovative mesh-free method facilitates the modeling of complex geometries, accounts for surface tension, handles non-Newtonian fluids, and addresses transient flow scenarios without the cumbersome meshing and adjustments typical of traditional CFD methods. Users can quickly onboard, usually within a single day, while the software is designed to support parallel design-of-experiment workflows, allowing for numerous iterations to be completed in just hours rather than days. Dive CAE prioritizes collaboration among users, offers a straightforward licensing model (a single license for all), ensures transparent cost management, adheres to data usage governance, and provides scalability through its cloud-based architecture, making it an attractive choice for engineering teams looking to enhance their fluid dynamics simulations. This combination of features not only streamlines the simulation process but also fosters efficient teamwork and innovation in project development.

Arm Mbed OS is an open-source operating system tailored for IoT applications, providing all the essential tools for creating IoT devices. This robust OS is equipped to support smart and connected products built on Arm Cortex-M architecture, offering features such as machine learning, secure connectivity stacks, an RTOS kernel, and drivers for various sensors and I/O devices. Specifically designed for the Internet of Things, Arm Mbed OS integrates capabilities in connectivity, machine learning, networking, and security, complemented by a wealth of software libraries, development boards, tutorials, and practical examples. It fosters collaboration across a vast ecosystem, supporting over 70 partners in silicon, modules, cloud services, and OEMs, thereby enhancing choices for developers. By leveraging the Mbed OS API, developers can maintain clean, portable, and straightforward application code while benefiting from advanced security, communication, and machine learning functionalities. This cohesive solution ultimately streamlines the development process, significantly lowering costs, minimizing time investment, and reducing associated risks. Furthermore, Mbed OS empowers innovation, enabling developers to rapidly prototype and deploy IoT solutions with confidence.

NVIDIA PhysicsNeMo is a publicly available Python-based deep-learning framework designed for the creation, training, fine-tuning, and inference of physics-AI models that integrate physical principles with data, thereby enhancing simulations, developing accurate surrogate models, and facilitating near-real-time predictions in various fields such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate studies, and digital twin technologies. This framework offers powerful, GPU-accelerated capabilities along with Python APIs that are built on the PyTorch platform and distributed under the Apache 2.0 license, featuring a selection of curated model architectures that include physics-informed neural networks, neural operators, graph neural networks, and generative AI techniques, enabling developers to effectively leverage physics-based causal relationships together with empirical data for high-quality engineering modeling. Additionally, PhysicsNeMo provides comprehensive training pipelines that encompass everything from geometry ingestion to the application of differential equations, along with reference application recipes that help users quickly initiate their development workflows. This combination of features makes PhysicsNeMo an essential tool for engineers and researchers seeking to advance their work in physics-driven AI applications.

Inventor® Nastran® is a finite element analysis (FEA) tool integrated within CAD software, enabling engineers and analysts to perform a diverse range of studies using various materials. This software provides comprehensive simulation capabilities that encompass both linear and nonlinear stress analysis, dynamic simulations, and heat transfer assessments. It is exclusively accessible through the Product Design & Manufacturing Collection, which includes a suite of powerful tools designed to enhance workflows within Inventor. In addition to advanced simulation features, this collection also offers 5-axis CAM, nesting tools, and access to software like AutoCAD and Fusion 360, ensuring a holistic approach to product design and manufacturing processes. By utilizing Inventor Nastran, professionals can streamline their analysis and improve their design outcomes significantly.

NVIDIA FLARE, which stands for Federated Learning Application Runtime Environment, is a versatile, open-source SDK designed to enhance federated learning across various sectors, such as healthcare, finance, and the automotive industry. This platform enables secure and privacy-focused AI model training by allowing different parties to collaboratively develop models without the need to share sensitive raw data. Supporting a range of machine learning frameworks—including PyTorch, TensorFlow, RAPIDS, and XGBoost—FLARE seamlessly integrates into existing processes. Its modular architecture not only fosters customization but also ensures scalability, accommodating both horizontal and vertical federated learning methods. This SDK is particularly well-suited for applications that demand data privacy and adherence to regulations, including fields like medical imaging and financial analytics. Users can conveniently access and download FLARE through the NVIDIA NVFlare repository on GitHub and PyPi, making it readily available for implementation in diverse projects. Overall, FLARE represents a significant advancement in the pursuit of privacy-preserving AI solutions.

Refraction serves as a powerful code-generation tool tailored for developers, employing AI to assist in writing code. This innovative platform enables users to produce unit tests, documentation, refactor existing code, and much more. It supports code generation in 34 programming languages, including Assembly, C#, C++, CoffeeScript, CSS, Dart, Elixir, Erlang, Go, GraphQL, Groovy, Haskell, HTML, Java, JavaScript, Kotlin, LaTeX, Less, Lua, MatLab, Objective-C, OCaml, Perl, PHP, Python, R Lang, Ruby, Rust, Sass/SCSS, Scala, Shell, SQL, Swift, and TypeScript. With Refraction, thousands of developers globally are streamlining their workflows, utilizing AI to automate tasks such as documentation creation, unit testing, and code refactoring. This tool not only enhances efficiency but also allows programmers to concentrate on more critical aspects of software development. By leveraging AI, you can refactor, optimize, fix, and style-check your code effortlessly. Additionally, it facilitates the generation of unit tests compatible with various testing frameworks and helps clarify the intent of your code, making it more accessible for others. Embrace the capabilities of Refraction and transform your coding experience today.

RightNow AI is an innovative platform that leverages artificial intelligence to automatically analyze, identify inefficiencies, and enhance CUDA kernels for optimal performance. It is compatible with all leading NVIDIA architectures, such as Ampere, Hopper, Ada Lovelace, and Blackwell GPUs. Users can swiftly create optimized CUDA kernels by simply using natural language prompts, which negates the necessity for extensive knowledge of GPU intricacies. Additionally, its serverless GPU profiling feature allows users to uncover performance bottlenecks without the requirement of local hardware resources. By replacing outdated optimization tools with a more efficient solution, RightNow AI provides functionalities like inference-time scaling and comprehensive performance benchmarking. Renowned AI and high-performance computing teams globally, including Nvidia, Adobe, and Samsung, trust RightNow AI, which has showcased remarkable performance enhancements ranging from 2x to 20x compared to conventional implementations. The platform's ability to simplify complex processes makes it a game-changer in the realm of GPU optimization.

DeepSpeed is an open-source library focused on optimizing deep learning processes for PyTorch. Its primary goal is to enhance efficiency by minimizing computational power and memory requirements while facilitating the training of large-scale distributed models with improved parallel processing capabilities on available hardware. By leveraging advanced techniques, DeepSpeed achieves low latency and high throughput during model training. This tool can handle deep learning models with parameter counts exceeding one hundred billion on contemporary GPU clusters, and it is capable of training models with up to 13 billion parameters on a single graphics processing unit. Developed by Microsoft, DeepSpeed is specifically tailored to support distributed training for extensive models, and it is constructed upon the PyTorch framework, which excels in data parallelism. Additionally, the library continuously evolves to incorporate cutting-edge advancements in deep learning, ensuring it remains at the forefront of AI technology.

It enables efficient training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances powered by AWS Trainium. Additionally, for model deployment, it facilitates both high-performance and low-latency inference utilizing AWS Inferentia-based Amazon EC2 Inf1 instances along with AWS Inferentia2-based Amazon EC2 Inf2 instances. With the Neuron SDK, users can leverage widely-used frameworks like TensorFlow and PyTorch to effectively train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal alterations to their code and no reliance on vendor-specific tools. The integration of the AWS Neuron SDK with these frameworks allows for seamless continuation of existing workflows, requiring only minor code adjustments to get started. For those involved in distributed model training, the Neuron SDK also accommodates libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and scalability for various ML tasks. By providing robust support for these frameworks and libraries, it significantly streamlines the process of developing and deploying advanced machine learning solutions.

Developers experience significantly higher productivity levels when using Ubuntu compared to custom embedded Linux systems. By utilizing a shared platform, costs can be reduced, as licensing becomes more affordable, updates are more thoroughly tested, and maintenance responsibilities are distributed. The widespread familiarity and usage of Ubuntu facilitate seamless CI/CD processes, access to superior tools, quicker updates, and more reliable kernels. In this context, Linux itself does not provide a competitive edge; instead, leveraging pre-configured boards allows teams to concentrate on software that is distinctively aligned with their objectives. Managing a well-known environment and platform proves to be both easier and more cost-effective than operating a specialized operating system. Unsurprisingly, a larger number of Linux developers prefer Ubuntu, resulting in a richer and more diverse talent pool. By tapping into this expansive talent reservoir, organizations can benefit from Ubuntu's clear advantages across various metrics. Ultimately, productivity thrives on the principle of reuse, and developers can be empowered by accessing the widest selection of packages available. This strategy not only streamlines processes but also accelerates project timelines, leading to enhanced outcomes.

Fusion 360 seamlessly integrates design, engineering, electronics, and manufacturing into one cohesive software environment. It offers a comprehensive suite that combines CAD, CAM, CAE, and PCB capabilities within a single development platform. Additionally, users benefit from features like EAGLE Premium, HSMWorks, Team Participant, and various cloud-based services, including generative design and cloud simulation. With an extensive range of modeling tools, engineers can effectively design products while ensuring their form, fit, and function through multiple analysis techniques. Users can create and modify sketches using constraints, dimensions, and advanced sketching tools. It also allows for editing or fixing imported geometry from other file formats with ease. Design modifications can be made without concern for time-dependent features, enabling flexibility in the workflow. Furthermore, the software supports the creation of intricate parametric surfaces for tasks such as repairing or designing geometry, while history-based features like extrude, revolve, loft, and sweep dynamically adapt to any design alterations made. This versatility makes Fusion 360 an essential tool for modern engineering practices.

Void is an operating system designed for general use, built on the monolithic Linux kernel. Its package management system facilitates the swift installation, updating, and removal of software; users can choose from binary packages or compile directly from source using the XBPS source packages collection. Void is compatible with numerous platforms, providing flexibility for various hardware environments. Additionally, software can be built natively or cross-compiled through the XBPS source packages collection, enhancing its versatility. In contrast to countless other distributions, Void is an original creation and not a derivative of any existing system. The package manager and build system of Void have been developed entirely from the ground up, ensuring a unique approach. Furthermore, Void Linux accommodates both musl and GNU libc implementations, addressing compatibility issues with patches and collaborating with upstream developers to enhance the accuracy and adaptability of their software projects. This commitment to innovation and quality makes Void Linux a distinct choice for users seeking an alternative operating system.

Customize models, refine designs, and export with precision. PlantFCE Model Builder offers 3D modeling for process plants. Model Builder can be used to estimate costs for engineering projects. View real-time updates as you edit a scene in the rendering window. Get low lock-in with PlantFCE Model Builder* PlantFCE Model Builder can export and import 3D models to and from industry-standard 3D model formats like GLB, OBJ, or STL files. Reduce errors and save time with automated clash check built right in.** PlantFCE Model Builder's automated clash check** reduces the time spent on clash check sessions and gives you more time to work on your project. Download for Windows (from the Microsoft Store) and Mac (available on Intel and Apple silicon Macs) on our website Training Contact us through the contact page on the PlantFCE website if you would like training for your organization. Documentation You can find documentation for Model Builder on the PlantFCE website. NOTES *Exporting through Model Builder does not include Model Builder specific functionality like properties set on objects or features specific to Model Builder. **Clash check will be released as part of version 2

IREN’s AI Cloud is a cutting-edge GPU cloud infrastructure that utilizes NVIDIA's reference architecture along with a high-speed, non-blocking InfiniBand network capable of 3.2 TB/s, specifically engineered for demanding AI training and inference tasks through its bare-metal GPU clusters. This platform accommodates a variety of NVIDIA GPU models, providing ample RAM, vCPUs, and NVMe storage to meet diverse computational needs. Fully managed and vertically integrated by IREN, the service ensures clients benefit from operational flexibility, robust reliability, and comprehensive 24/7 in-house support. Users gain access to performance metrics monitoring, enabling them to optimize their GPU expenditures while maintaining secure and isolated environments through private networking and tenant separation. The platform empowers users to deploy their own data, models, and frameworks such as TensorFlow, PyTorch, and JAX, alongside container technologies like Docker and Apptainer, all while granting root access without any limitations. Additionally, it is finely tuned to accommodate the scaling requirements of complex applications, including the fine-tuning of extensive language models, ensuring efficient resource utilization and exceptional performance for sophisticated AI projects.

Developed in collaboration with top chip manufacturers over a span of 15 years, FreeRTOS is now downloaded approximately every 170 seconds and stands as a top-tier real-time operating system (RTOS) tailored for microcontrollers and small microprocessors. Available at no cost under the MIT open source license, FreeRTOS encompasses a kernel along with an expanding collection of IoT libraries that cater to various industries. Prioritizing reliability and user-friendliness, FreeRTOS is renowned for its proven durability, minimal footprint, and extensive device compatibility, making it the go-to standard for microcontroller and small microprocessor applications among leading global enterprises. With a wealth of pre-configured demos and IoT reference integrations readily available, users can easily set up their projects without any hassle. This streamlined process allows for rapid downloading, compiling, and quicker market entry. Furthermore, the ecosystem of partners offers a diverse range of options, including both community-driven contributions and professional support, ensuring that users have access to the resources they need for success. As technology continues to evolve, FreeRTOS remains committed to adapting and enhancing its offerings to meet the ever-changing demands of the industry.

LTE MAC Lab is a comprehensive simulation tool designed for system-level analysis, operating within the Matlab environment. This tool enables users to effectively model and evaluate the performance of wireless LTE network deployments while gaining insights into the dynamic aspects of radio interface mechanisms. It captures the fluctuating behavior of a modeled HetNet RAN, emphasizing essential Radio Resource Management functionalities, including scheduling, carrier aggregation, handover processes, and link adaptation strategies. Additionally, the tool incorporates various models for propagation effects such as path loss, shadowing, and multipath, as well as mobility scenarios to enhance simulation accuracy. By leveraging LTE MAC Lab, researchers and engineers can explore and optimize network performance in a controlled setting.

Introducing AI and Kubernetes that prioritize security from the ground up, regardless of your infrastructure's location. By establishing a robust security boundary around Kubernetes workloads, we eliminate the risks associated with container escapes. Our approach simplifies the execution of AI and machine learning tasks through advanced GPU device virtualization, driver isolation, and virtual GPUs (vGPUs). Edera Krata heralds a transformative shift in isolation technology, paving the way for a new era focused on security. Edera redefines both security and performance for AI and GPU applications, while ensuring seamless integration with Kubernetes environments. Each container operates with its own dedicated Linux kernel, thereby removing the vulnerabilities linked to shared kernel states among containers. This advancement effectively ends the prevalence of container escapes, reduces the need for costly security tools, and alleviates the burden of endlessly sifting through logs. With just a few lines of YAML, you can launch Edera Protect and get started effortlessly. Designed in Rust to enhance memory safety, this solution has no negative impact on performance. It represents a secure-by-design Kubernetes framework that effectively neutralizes threats before they can take action, transforming the landscape of cloud-native security.

Modular is an advanced AI infrastructure platform that unifies the entire inference stack, from hardware-level optimization to cloud deployment. It allows developers to run AI models seamlessly across multiple hardware types, including NVIDIA, AMD, and other architectures. The platform eliminates the need for fragmented tools by providing a single system for serving, optimization, and scaling. Modular delivers high-performance inference with improved efficiency and reduced costs through better hardware utilization. It supports flexible deployment options, including managed cloud services, private VPC environments, and self-hosted setups. Developers can deploy both open-source and custom models with ease while maintaining full control over performance. The platform’s compiler technology automatically optimizes workloads for different hardware targets. Modular also enables real-time scaling and efficient resource allocation for demanding AI applications. Its unified approach simplifies infrastructure management while improving reliability and performance. Overall, Modular empowers teams to build, deploy, and scale AI systems more effectively.