How NVIDIA Uses C++

The article discusses the significant updates in CUDA Toolkit 13.

This article explores profit and loss modeling on GPUs using ISO C++ language parallelism, building on previous work with Black-Scholes simulations.

NVIDIA has released CUDA Toolkit 12. 0, marking its first major update in years, focusing on enhanced programming models and application acceleration through new hardware capabilities.

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

The article discusses MatX, a GPU-accelerated numerical computing C++ library that allows developers to write high-performance code with a Python-like syntax.

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++, ...

This article discusses the implementation and usage of sstable-to-arrow, a tool designed to convert SSTable data from Cassandra into Arrow format for GPU-based analytics.

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

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.

libcu++, NVIDIA's open-source C++ Standard Library, facilitates seamless integration between CPU and GPU code. The latest version, 1. 4.

The article discusses the challenges of memory allocation in NVIDIA CUDA and introduces the RAPIDS Memory Manager (RMM) as a solution.

The article discusses the NVIDIA Ampere architecture and the new data movement controls introduced in CUDA 11.

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 how NVIDIA's NVC++ compiler enables GPU acceleration of Standard C++ code without the need for language extensions or non-standard libraries.

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.

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.

CUDA 8 introduces significant enhancements to the CUDA compiler toolchain, focusing on compile time improvements, extended lambda support, and runtime compilation features.

CUDA 8 introduces significant advancements in NVIDIA's parallel computing platform, including support for the Pascal GPU architecture, enhanced Unified Memory capabilities, and new profiling tools.

The article discusses the integration of modern C++ features into CUDA 7. 5, particularly focusing on the Hemi 2 library, which simplifies portable parallel programming for GPUs.

The article introduces the NVIDIA OpenACC Toolkit, designed to facilitate GPU computing for scientists and researchers who may lack extensive programming experience.

The article discusses the new features introduced in CUDA Toolkit 7.