![]() Suddenly, you hear a terrible and monstrous scream scattering the peace of the countryside. You are in the middle of the road leading to your village, where your friends, your family, and you favorite dog are living. Wa_cq_url: "/content/www/us/en/high-performance-computing/hpc-software-and-programming., updated Twitter #Fundamentals #Complexity What is Software Entropy And How To Manage It? Wa_english_title: "High Performance Computing (HPC) Software and Tools", Wa_industry_type: "emtindustry:softwareandservices", Wa_emtsubject: "emtsubject:itinformationtechnology", ![]() Wa_emtindustry: "emtindustry:softwareandservices", Wa_emtcontenttype: "emtcontenttype:salesandmarketingmaterials/industryarticles", Gain direct access to analytics and AI optimizations from Intel to ensure that your software works together seamlessly.Achieve drop-in acceleration for data preprocessing and machine learning workflows with compute-intensive Python packages, Modin, scikit-learn, and XGBoost, optimized for Intel.Deliver high-performance, deep-learning training on Intel® CPUs and GPUs and integrate fast inference into your AI development workflow with Intel-optimized frameworks for TensorFlow and PyTorch, pretrained models, and low-precision tools.Like the HPC Toolkit, the AI Analytics Toolkit components are built using oneAPI libraries for low-level compute optimizations. This toolkit maximizes performance end to end-from preprocessing through machine learning-and provides interoperability for efficient model development. This comprehensive package provides data scientists, AI developers, and researchers with familiar Python tools and AI frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architectures. To help accelerate AI and analytics, Intel offers the Intel® oneAPI AI Analytics Toolkit. While AI and big data applications have typically run on traditional single-node systems, organizations are increasingly moving to HPC technology to accelerate workflows and improve results. These applications require massive amounts of compute to perform their task. (Note: The HPC Toolkit is an add-on to the Intel® oneAPI Base Toolkit, which is required for full functionality.)ĪI and analytics workloads are a primary use case for HPC systems. Intel® Trace Analyzer and Collector: Understand MPI application behavior across its full runtime.Intel® MPI Library: Deliver flexible, efficient, scalable cluster messaging on Intel® architecture.Intel® Inspector: Locate and debug threading, memory, and persistent memory errors early in the design cycle to avoid costly errors later.Intel® Fortran Compiler Classic: This standards-based Fortran compiler includes support for OpenMP that provides continuity with existing CPU-focused workflows.Intel® Fortran Compiler: Use this standards-based Fortran Compiler with OpenMP support for CPU and GPU offload.Intel® Cluster Checker: Verify that cluster components work together seamlessly for optimal performance, improved uptime, and lower total cost of ownership.Intel® C++ Compiler Classic: Use this standards-based C++ compiler with support for OpenMP to take advantage of more cores and built-in technologies in platforms based on Intel® Xeon® Scalable processors and Intel® Core™ processors. ![]() Intel® oneAPI DPC++/C++ Compiler: Use this standards-based C++ compiler with support for OpenMP to take advantage of more cores and built-in technologies in Intel® CPU, GPU, and FPGA platforms (Intel® Xeon®, Intel® Core™ processors with Intel® Processor Graphics, Intel® Xe architecture GPUs). ![]() They’re all designed on the foundation of oneAPI, an open, cross-architecture, standards-based programming model. To help solve these challenges, Intel offers several HPC tools and resources that help developers build high-performance, parallel-computing-optimized, cross-architecture applications. Here, they must also deal with a number of time-consuming and costly hurdles as they seek to ensure their software works with as many hardware types and computing models as possible. However, this process can be significantly shortened using the right software tools.Īt the same time, developers face a growing need to accelerate specialized workloads through a variety of architectures-CPUs alongside accelerators such as GPUs and FPGAs. Transitioning software to function on HPC clusters and efficiently programming high-performance parallel computing can be complex, requiring significant time investment for developers. Many businesses are supercharging big data and analytics use cases with HPC systems that distribute the computing process across a number of nodes-running workloads in parallel to accelerate results. Accelerate HPC Workloads Across Multiple Architectures ![]()
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