Best HPC Software in Brazil - Page 2

The Nimbix Supercomputing Suite offers a diverse and secure range of high-performance computing (HPC) solutions available as a service. This innovative model enables users to tap into a comprehensive array of HPC and supercomputing resources, spanning from hardware options to bare metal-as-a-service, facilitating the widespread availability of advanced computing capabilities across both public and private data centers. Through the Nimbix Supercomputing Suite, users gain access to the HyperHub Application Marketplace, which features an extensive selection of over 1,000 applications and workflows designed for high performance. By utilizing dedicated BullSequana HPC servers as bare metal-as-a-service, clients can enjoy superior infrastructure along with the flexibility of on-demand scalability, convenience, and agility. Additionally, the federated supercomputing-as-a-service provides a centralized service console, enabling efficient management of all computing zones and regions within a public or private HPC, AI, and supercomputing federation, thereby streamlining operations and enhancing productivity. This comprehensive suite empowers organizations to drive innovation and optimize performance across various computational tasks.

The NVIDIA DGX Cloud provides an AI infrastructure as a service that simplifies the deployment of large-scale AI models and accelerates innovation. By offering a comprehensive suite of tools for machine learning, deep learning, and HPC, this platform enables organizations to run their AI workloads efficiently on the cloud. With seamless integration into major cloud services, it offers the scalability, performance, and flexibility necessary for tackling complex AI challenges, all while eliminating the need for managing on-premise hardware.

Kao Data stands at the forefront of the industry, innovating in the creation and management of data centres specifically designed for artificial intelligence and cutting-edge computing. Our platform, inspired by hyperscale models and tailored for industrial use, offers clients a secure, scalable, and environmentally friendly environment for their computing needs. Based at our Harlow campus, we support a diverse range of mission-critical high-performance computing projects, establishing ourselves as the UK's top choice for demanding, high-density, GPU-driven computing solutions. Additionally, with swift integration options available for all leading cloud providers, we enable the realization of your hybrid AI and HPC aspirations seamlessly. By prioritizing sustainability and performance, we are not just meeting current demands but also shaping the future of computing infrastructure.

Azure's high-performance computing (HPC) capabilities enable groundbreaking innovations, tackle intricate challenges, and enhance your compute-heavy tasks. You can create and manage your most resource-demanding applications in the cloud using a comprehensive solution specifically designed for HPC needs. With Azure Virtual Machines, you gain access to supercomputing capabilities, seamless interoperability, and nearly limitless scalability for intensive computing requirements. Additionally, you can enhance decision-making processes and realize advanced AI potential through top-tier Azure AI and analytics services. Furthermore, Azure ensures the protection of your data and applications by implementing robust security measures and confidential computing techniques, all while facilitating regulatory compliance. This comprehensive approach allows organizations to focus on innovation while maintaining a secure and efficient cloud environment.

Fuzzball propels innovation among researchers and scientists by removing the complexities associated with infrastructure setup and management. It enhances the design and execution of high-performance computing (HPC) workloads, making the process more efficient. Featuring an intuitive graphical user interface, users can easily design, modify, and run HPC jobs. Additionally, it offers extensive control and automation of all HPC operations through a command-line interface. With automated data handling and comprehensive compliance logs, users can ensure secure data management. Fuzzball seamlessly integrates with GPUs and offers storage solutions both on-premises and in the cloud. Its human-readable, portable workflow files can be executed across various environments. CIQ’s Fuzzball redefines traditional HPC by implementing an API-first, container-optimized architecture. Operating on Kubernetes, it guarantees the security, performance, stability, and convenience that modern software and infrastructure demand. Furthermore, Fuzzball not only abstracts the underlying infrastructure but also automates the orchestration of intricate workflows, fostering improved efficiency and collaboration among teams. This innovative approach ultimately transforms how researchers and scientists tackle computational challenges.

Amazon's EC2 P4d instances offer exceptional capabilities for machine learning training and high-performance computing tasks within the cloud environment. Equipped with NVIDIA A100 Tensor Core GPUs, these instances achieve remarkable throughput and feature low-latency networking, supporting an impressive 400 Gbps instance networking speed. P4d instances present a cost-effective solution, providing up to 60% savings in the training of ML models, along with an average performance increase of 2.5 times for deep learning applications when compared to earlier P3 and P3dn models. They are utilized in expansive clusters known as Amazon EC2 UltraClusters, which seamlessly integrate high-performance computing, networking, and storage. This allows users the flexibility to scale from a handful to thousands of NVIDIA A100 GPUs, depending on their specific project requirements. A wide array of professionals, including researchers, data scientists, and developers, can leverage P4d instances for various machine learning applications such as natural language processing, object detection and classification, and recommendation systems, in addition to executing high-performance computing tasks like drug discovery and other complex analyses. The combination of performance and scalability makes P4d instances a powerful choice for tackling diverse computational challenges.

Amazon S3 Express One Zone is designed for high-performance storage within a single Availability Zone, specifically tailored to provide rapid access to frequently used data and support latency-sensitive applications with single-digit millisecond response times. This storage class enhances data retrieval speeds by up to ten times while reducing request costs by as much as 50% compared to the standard S3 tier. By allowing users to choose a particular AWS Availability Zone in a region for data storage, S3 Express One Zone enables the co-location of storage and compute resources, which can lead to improved performance and reduced computing expenses, ultimately facilitating faster workload execution. The data is organized in a unique bucket type known as an S3 directory bucket, capable of handling hundreds of thousands of requests each second. Moreover, S3 Express One Zone seamlessly integrates with various services, including Amazon SageMaker Model Training, Amazon Athena, Amazon EMR, and AWS Glue Data Catalog, thus optimizing machine learning and analytical processes. As a result, this storage solution not only meets the demands of high-performance applications but also enhances overall operational efficiency by streamlining data access and processing.

AWS Parallel Computing Service (AWS PCS) provides a streamlined managed service designed for executing and scaling high-performance computing tasks while also facilitating the development of scientific and engineering models on the AWS platform using Slurm. This service allows users to create fully elastic environments that seamlessly incorporate computing, storage, networking, and visualization tools, liberating them from the complexities of infrastructure management and enabling a stronger focus on research and innovation. In addition, AWS PCS includes features for managed updates and built-in observability, which greatly improve the efficiency of cluster operations and maintenance. Users have the flexibility to construct and launch scalable, reliable, and secure HPC clusters via the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDK. The service accommodates a wide range of applications, from tightly coupled workloads such as computer-aided engineering to high-throughput computing tasks like genomics analysis, as well as accelerated computing utilizing GPUs and specialized silicon like AWS Trainium and AWS Inferentia. Furthermore, by leveraging AWS PCS, organizations can ensure that they remain at the forefront of technological advancements in high-performance computing.

In the last twenty years, there have been significant advancements in software technologies, and high-performance computing (HPC) has progressed exponentially. However, our capacity to interpret simulation results has not experienced a similar evolution. Traditional methods of visualizing data, such as creating plots and animations, fail to keep pace when faced with extremely large multi-billion cell meshes or extensive simulations involving tens of thousands of timesteps. The process of evaluating solutions can be greatly expedited by generating features and quantitative metrics through techniques like eigen analysis or machine learning. Furthermore, the user-friendly FieldView desktop software is seamlessly integrated with the robust capabilities of the VisIt Prime backend, enhancing the overall analysis experience. This integration allows for a more efficient workflow, enabling researchers to focus on interpreting results rather than being bogged down by outdated visualization methods.

NVIDIA GPU Cloud (NGC) serves as a cloud platform that harnesses GPU acceleration for deep learning and scientific computations. It offers a comprehensive catalog of fully integrated containers for deep learning frameworks designed to optimize performance on NVIDIA GPUs, whether in single or multi-GPU setups. Additionally, the NVIDIA train, adapt, and optimize (TAO) platform streamlines the process of developing enterprise AI applications by facilitating quick model adaptation and refinement. Through a user-friendly guided workflow, organizations can fine-tune pre-trained models with their unique datasets, enabling them to create precise AI models in mere hours instead of the traditional months, thereby reducing the necessity for extensive training periods and specialized AI knowledge. If you're eager to dive into the world of containers and models on NGC, you’ve found the ideal starting point. Furthermore, NGC's Private Registries empower users to securely manage and deploy their proprietary assets, enhancing their AI development journey.

Develop a hybrid storage solution that seamlessly integrates with your current network-attached storage (NAS) and Azure Blob Storage. This local caching device enhances data access within your data center, in Azure, or throughout a wide-area network (WAN). Comprising both software and hardware, the Microsoft Azure FXT Edge Filer offers exceptional throughput and low latency, making it ideal for hybrid storage systems that cater to high-performance computing (HPC) demands. The scale-out clustering feature allows for uninterrupted NAS performance enhancements. You can connect up to 24 FXT nodes within a single cluster, enabling you to achieve millions of IOPS and hundreds of GB/s of performance. When high performance and scalability are crucial for file-based workloads, Azure FXT Edge Filer ensures your data remains on the quickest route to processing resources. Managing your storage infrastructure becomes straightforward with Azure FXT Edge Filer. It allows you to migrate older data to Azure Blob Storage, maintaining easy access with minimal delay, while facilitating a balanced approach between on-premises and cloud storage solutions. This hybrid model not only streamlines data management but also enhances operational efficiency across your entire storage architecture.

Arm Allinea Studio is a comprehensive collection of tools designed for the development of server and high-performance computing (HPC) applications specifically on Arm architecture. It includes specialized compilers and libraries tailored for Arm, along with debugging and optimization functionalities. The Arm Performance Libraries offer optimized core mathematical libraries that enhance the performance of high-performance computing applications running on Arm processors. These libraries provide routines accessible through both Fortran and C interfaces, ensuring versatility for developers. Furthermore, the Arm Performance Libraries leverage OpenMP across various routines, including BLAS, LAPACK, FFT, and sparse operations, to fully exploit the capabilities of multi-processor systems, thereby significantly boosting application performance. In addition to this, the suite also facilitates seamless integration and improved workflow, making it an essential toolset for developers in the HPC landscape.

Amazon EC2's P5 instances, which utilize NVIDIA H100 Tensor Core GPUs, along with the P5e and P5en instances that feature NVIDIA H200 Tensor Core GPUs, offer unparalleled performance for deep learning and high-performance computing tasks. They can significantly enhance your solution development speed by as much as four times when compared to prior GPU-based EC2 instances, while simultaneously lowering the costs associated with training machine learning models by up to 40%. This efficiency allows for quicker iterations on solutions, resulting in faster time-to-market. The P5, P5e, and P5en instances are particularly well-suited for training and deploying advanced large language models and diffusion models, which are essential for the most challenging generative AI applications. These applications encompass a wide range of functions, including question-answering, code generation, image and video synthesis, and speech recognition. Moreover, these instances are also capable of scaling to support demanding HPC applications, such as those used in pharmaceutical research and discovery, thus expanding their utility across various industries. In essence, Amazon EC2's P5 series not only enhances computational power but also drives innovation across multiple sectors.

Amazon EC2 UltraClusters allow for the expansion to thousands of GPUs or specialized machine learning accelerators like AWS Trainium, granting immediate access to supercomputing-level performance. They make advanced computing accessible to developers in machine learning, generative AI, and high-performance computing through an easy pay-as-you-go system, eliminating the need for setup or maintenance expenses. UltraClusters are comprised of thousands of accelerated EC2 instances that are strategically placed within a specific AWS Availability Zone and are connected via Elastic Fabric Adapter (EFA) networking within a petabit-scale nonblocking network. This innovative setup delivers superior networking capabilities and access to Amazon FSx for Lustre, a fully managed shared storage solution built on a high-performance parallel file system, which facilitates the swift processing of large datasets with latencies measured in sub-milliseconds. Furthermore, EC2 UltraClusters enhance scale-out opportunities for distributed machine learning training and tightly integrated high-performance computing tasks, significantly minimizing training durations. Overall, this state-of-the-art infrastructure is designed to meet the demands of the most intensive computational projects.

AWS's High Performance Computing (HPC) offerings enable users to run extensive simulations and deep learning tasks in a cloud environment, delivering nearly limitless computational power, advanced file storage solutions, and high-speed networking capabilities. By providing a comprehensive suite of cloud tools, including capabilities for machine learning and data analysis, this service enhances innovation and facilitates the swift development and testing of new products. The efficiency of operations is significantly improved through the availability of on-demand computing resources, which allows users to concentrate on solving intricate challenges without being hindered by conventional infrastructure limitations. Among the various solutions offered by AWS HPC are the Elastic Fabric Adapter (EFA) for optimized networking with low latency and high bandwidth, AWS Batch for efficiently managing job scaling, AWS ParallelCluster for easy cluster setup, and Amazon FSx for robust file storage systems. Collectively, these offerings create a nimble and scalable framework designed to meet a wide array of HPC demands, ensuring that users can adapt swiftly to changing project requirements. This flexibility is crucial in a landscape where rapid technological advancement and competitive pressures are ever-present.

The Elastic Fabric Adapter (EFA) serves as a specialized network interface for Amazon EC2 instances, designed to support applications that necessitate significant inter-node communication when deployed at scale on AWS. Its unique operating system (OS) effectively circumvents traditional hardware interfaces, significantly improving the efficiency of communications between instances, which is essential for the scalability of these applications. EFA allows High-Performance Computing (HPC) applications utilizing the Message Passing Interface (MPI) and Machine Learning (ML) applications leveraging the NVIDIA Collective Communications Library (NCCL) to seamlessly expand to thousands of CPUs or GPUs. Consequently, users can experience the performance levels of traditional on-premises HPC clusters while benefiting from the flexible and on-demand nature of the AWS cloud environment. This feature is available as an optional enhancement for EC2 networking, and can be activated on any compatible EC2 instance without incurring extra charges. Additionally, EFA integrates effortlessly with most widely-used interfaces, APIs, and libraries for facilitating inter-node communications, making it a versatile choice for developers. The ability to scale applications while maintaining high performance is crucial in today’s data-driven landscape.

AWS ParallelCluster is a free, open-source tool designed for managing clusters that streamlines the deployment and oversight of High-Performance Computing (HPC) clusters within the AWS environment. It takes care of setting up necessary components such as compute nodes, shared filesystems, and job schedulers, while accommodating various instance types and job submission queues. Users can engage with ParallelCluster through different interfaces, including a graphical user interface, command-line interface, or API, which allows for adaptable configuration and management of clusters. Additionally, it works seamlessly with job schedulers like AWS Batch and Slurm, making it easier to transition existing HPC workloads to the cloud with minimal changes. Without any extra charges for the tool itself, users are only billed for the AWS resources their applications utilize. AWS ParallelCluster empowers users to model, provision, and dynamically adjust the resources required for their applications through a straightforward text file, enhancing automation and security in the process. This flexibility not only saves time but also optimizes resource management, making it an invaluable asset for researchers and organizations seeking to leverage cloud computing for their HPC needs.

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).

Moab®, HPC Suite automates the management, monitoring, reporting, and scheduling of large-scale HPC workloads. Its intelligence engine, which is patent-pending, uses multi-dimensional policies to optimize workload start times and run time on different resources. These policies balance high utilization goals and throughput with competing workload priorities, SLA requirements, and thus accomplish more work in less time and in a better priority order. Moab HPC Suite maximizes the value and use of HPC systems, while reducing complexity and management costs.