RAJA offers portable, parallel loop execution by providing building blocks that extend the generally-accepted parallel for idiom. RAJA relies on standard C++14 features.

This package provides several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case and univariate case. Linear interpolation of irregular gridded data is also covered. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids.

The rfacts package is an R interface to the Fixed and Adaptive Clinical Trial Simulator FACTS. It programmatically invokes FACTS to run clinical trial simulations. It aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility.

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This binary extracts information from standalone cubin files and presents them in human readable format. The output of nvdisasm includes CUDA assembly code for each kernel, listing of ELF data sections and other CUDA specific sections. Output style and options are controlled through nvdisasm command-line options. nvdisasm also does control flow analysis to annotate jump/branch targets and makes the output easier to read.

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides a binary that prunes host object files and libraries to only contain device code for the specified targets.

This package provides a GPU-accelerated library of primitives for deep neural networks, with highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.

CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN.

CUTLASS decomposes these ``moving parts'' into reusable, modular software components abstracted by C++ template classes. Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.

This package provides the CUDA Direct Sparse Solver library.

This package provides tooling to configure the NVSwitch memory fabrics to form one memory fabric among all participating GPUs, and monitors the NVLinks that support the fabric. See docs for more information.

This package provides a system-wide performance analysis tool designed to visualize an application’s algorithms, identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs,from large servers to small systems-on-a-chip.

The NVIDIA Management Library Headers (NVML) is a C-based API for monitoring and managing various states of the NVIDIA GPU devices. It provides a direct access to the queries and commands exposed via nvidia-smi.

This package enables the creation of profiling and tracing tools that target CUDA applications and give insight into the CPU and GPU behavior of CUDA applications. It provides the following APIs:

• the Activity API,

• the Callback API,

• the Event API,

• the Metric API,

• the Profiling API,

• the PC Sampling API,

• the Checkpoint API.

This package provides a library of functions for performing CUDA accelerated 2D image and signal processing.

The primary library focuses on image processing and is widely applicable for developers in these areas. NPP will evolve over time to encompass more of the compute heavy tasks in a variety of problem domains. The NPP library is written to maximize flexibility, while maintaining high performance.

This package provides a high-performance, GPU accelerated JPEG decoding functionality for image formats commonly used in deep learning and hyperscale multimedia applications. The library offers single and batched JPEG decoding capabilities which efficiently utilize the available GPU resources for optimum performance; and the flexibility for users to manage the memory allocation needed for decoding.

The nvJPEG library enables the following functions: use the JPEG image data stream as input; retrieve the width and height of the image from the data stream, and use this retrieved information to manage the GPU memory allocation and the decoding. A dedicated API is provided for retrieving the image information from the raw JPEG image data stream.

The encoding functions of the nvJPEG library perform GPU-accelerated compression of user’s image data to the JPEG bitstream. User can provide input data in a number of formats and colorspaces, and control the encoding process with parameters. Encoding functionality will allocate temporary buffers using user-provided memory allocator.

This package provides a an interactive profiler for CUDA and NVIDIA OptiX that provides detailed performance metrics and API debugging via a user interface and command-line tool. Users can run guided analysis and compare results with a customizable and data-driven user interface, as well as post-process and analyze results in their own workflows.

This package provides the proprietary cuTENSOR library for NVIDIA GPUs.

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides a C++ header-only library that wraps the NVIDIA CUDA Deep Neural Network library (cuDNN) C backend API. This entry point to the same API is less verbose (without loss of control), and adds functionality on top of the backend API, such as errata filters and autotuning.

This package provides the CUDA C++ developers with building blocks that make it easier to write safe and efficient code. It unifies three essential former CUDA C++ libraries into a single repository:

• Thrust (former repo)

• CUB (former repo)

• libcudacxx (former repo)

OpenCL (Open Computing Language) is a multi-vendor open standard for general-purpose parallel programming of heterogeneous systems that include CPUs, GPUs and other processors. This package provides the API to use OpenCL on NVIDIA GPUs.

This package provides a command-line tool to profile CUDA kernels. It enables the collection of a timeline of CUDA-related activities on both CPU and GPU, including kernel execution, memory transfers, memory set and CUDA API calls and events or metrics for CUDA kernels.