How NVIDIA Uses Fortran

The article discusses Autodesk Research's development of the Accelerated Lattice Boltzmann (XLB) library, which enhances computational fluid dynamics (CFD) performance using NVIDIA's Warp and GH200...

The article discusses the advancements in the NVIDIA HPC SDK v25. 7, focusing on how unified memory programming simplifies ocean modeling on GPUs.

The article discusses the advancements in AI-powered climate modeling, specifically focusing on the ClimSim-Online framework developed by NVIDIA.

The article discusses advanced optimization techniques for NVIDIA CUDA kernels, specifically focusing on handwritten Parallel Thread Execution (PTX) code.

The article discusses the process of porting CPU applications to NVIDIA GPUs to enhance performance, particularly in the context of Électricité de France's (EDF) fluid dynamics simulations using th...

This article provides a simplified introduction to CUDA, NVIDIA's parallel computing platform, and programming model.

The article discusses the NVIDIA GH200 NVL2 Enterprise Reference Architecture, which simplifies system memory management for AI infrastructure solutions.

The article discusses the NVIDIA Grace family of CPUs, designed to enhance data center efficiency amidst rising data processing demands.

The article discusses efficient debugging techniques for CUDA applications using NVIDIA Compute Sanitizer, highlighting its integration with NVIDIA Tools Extension (NVTX) and the creation of custom...

The article discusses the integration of AI with high-performance computing (HPC) to enhance data processing, simulation, and modeling. It highlights NVIDIA's HPC SDK 24.

cuTENSOR 2. 0 is an advanced CUDA math library designed to accelerate tensor computations, offering optimized implementations for dense, multi-dimensional arrays.

Dell Technologies and NVIDIA have collaborated to set new records in financial risk calculations using the NVIDIA H100 system for high-performance computing (HPC) and AI.

The article discusses the capabilities of NVIDIA Grace and Hopper architectures in high-performance computing (HPC), emphasizing the importance of a unified memory programming model and the tools a...

The article discusses the advancements in GPU programming facilitated by the NVIDIA Grace Hopper Superchip, emphasizing the benefits of a unified memory architecture that enhances developer product...

Heterogeneous Memory Management (HMM) enhances CUDA's Unified Memory model by allowing direct access to system allocated memory on PCIe-connected NVIDIA GPUs.

NVIDIA has released the HPC Software Development Kit (SDK) v22. 11, featuring the new stdexec library for asynchronous programming in C++.

The NVIDIA Arm HPC Developer Kit is a comprehensive platform for developing and benchmarking HPC, AI, and scientific computing applications on Arm-based systems.

The NVIDIA Grace Hopper Superchip Architecture represents a significant advancement in heterogeneous computing, combining NVIDIA Grace CPUs and Hopper GPUs to optimize performance for AI and high-p...

The article discusses the latest updates to the NVIDIA HPC SDK, focusing on its support for the Arm-based AWS Graviton3 processor and the Scalable Vector Extension (SVE) auto-vectorization.

The article discusses how to accelerate GPU applications using NVIDIA Math Libraries, highlighting three main approaches: compiler directives, programming languages, and preprogrammed libraries.

The article discusses the use of Fortran's standard parallel programming features, particularly the 'do concurrent' construct, for GPU acceleration.

This article discusses the optimization of multi-GPU programming using Standard Parallel C++, focusing on performance enhancement techniques and the integration of MPI for scaling applications.

This article discusses multi-GPU programming using Standard Parallel C++, focusing on the advantages of utilizing parallelism in C++ for accelerated computing.

The article provides a comprehensive overview of the latest software releases and resources from NVIDIA during GTC 2022, highlighting advancements in various SDKs including the HPC SDK, cuQuantum S...

NVIDIA has released cuFFTMp, a multi-node, multi-process extension to cuFFT, designed to enhance the performance of Fast Fourier Transforms (FFTs) across exascale platforms.

The article discusses the advantages of using standard language parallelism for accelerated computing on NVIDIA platforms, emphasizing the productivity and portability of programming with ISO C++, ...

The article discusses the release of NVIDIA HPC SDK 21. 11, which aims to enhance the performance and portability of high-performance computing applications.

NVIDIA has released cuTENSOR version 1. 4, which enhances support for up to 64-dimensional tensors and distributed multi-GPU tensor operations, while improving tensor contraction performance.

NVIDIA announced the upcoming release of HPC SDK 21. 11, which includes significant enhancements aimed at improving the performance and portability of high-performance computing applications.

The article provides insights on organizing a successful virtual hackathon, particularly focusing on the 2021 KISTI GPU Hackathon.

NVIDIA has announced the availability of the Arm HPC Developer Kit, which integrates hardware and software for high-performance computing, AI, and scientific applications on Arm server platforms.

NVIDIA has released cuTENSOR version 1. 3. 0, which offers significant performance improvements, including support for up to 40-dimensional tensors and enhanced mixed-precision capabilities.

This article provides an in-depth guide on utilizing Tensor Cores in CUDA Fortran, focusing on the WMMA (Warp Matrix Multiply and Accumulate) API.

NVIDIA has announced the release of the HPC SDK version 21. 3, which is now available for free download.

NASA and NVIDIA have collaborated to enhance scientific data science workflows by integrating RAPIDS with GPU-accelerated libraries.

The NVIDIA HPC SDK 20. 11 update introduces new features and enhancements for high-performance computing developers, including support for automatic GPU acceleration and new libraries.

The article discusses how to enhance high-performance computing (HPC) applications using NVIDIA Nsight Compute and the Roofline performance model.

The article discusses the Parallel Compiler Assisted Software Testing (PCAST) feature in NVIDIA's HPC compilers, focusing on its use cases for comparing GPU and CPU results.

The article discusses the complexities of setting up High-Performance Computing (HPC) applications and introduces the NVIDIA HPC SDK as a solution.

The article discusses how Fortran developers can accelerate their programs using the NVIDIA HPC SDK, specifically focusing on the DO CONCURRENT construct.

The article discusses how Dask, an open-source library, enhances Python's capabilities for data science and machine learning, making it suitable for enterprise-level applications.

The article discusses how to leverage NVIDIA's cuTENSOR library to accelerate standard Fortran array operations on GPUs using the nvfortran compiler.

The NVIDIA HPC SDK is a comprehensive suite of compilers, libraries, and tools designed for high-performance computing (HPC) developers, enabling them to program across the entire HPC platform.

The article discusses the release of VASP 6. 1. 0, which utilizes OpenACC to enhance performance on NVIDIA GPUs, achieving nearly 15x speed improvements over CPU-only simulations.

The PGI Community Edition 19. 10 introduces support for NVIDIA V100 Tensor Cores in CUDA Fortran, along with enhancements in C++17 language features and OpenACC 2. 6.

PGI Community Edition 19. 4 is now available for free download, providing scientists and engineers with tools for high-performance computing (HPC).

The article provides an overview of the NVIDIA GPU Technology Conference (GTC) 2019, highlighting the significance of CUDA in various computing domains.

The article discusses the latest release of PGI Compilers & Tools, version 19. 1, which enhances support for high-performance computing applications.

The article announces the release of the PGI Community Edition version 17. 10, highlighting its features and improvements for high-performance computing applications.

PGI 17. 7 introduces enhanced support for Tesla V100 GPUs, OpenACC directives, and CUDA Fortran, aimed at improving performance in high-performance computing (HPC) applications.