NVIDIA GPU Accelerator Development Tools

NVIDIA, The Portland Group, Accelereyes, Allinea and EMPhotonics bring you a comprehensive collection of development tools to enable you to take advantage of GPU acceleration. Leverage these tools to build blazing fast, NVIDIA-powered applications.

NVIDIA CUDA Toolkit and SDK

The CUDA Toolkit has all the development tools, libraries, and documentation you need to create applications for the CUDA architecture, including:

  • GPU Debugging & Profiling Tools
  • GPU-Accelerated Math Libraries
  • GPU-Accelerated Performance Primitives

NVIDIA Parallel Nsight 2.0

NVIDIA Parallel Nsight is the development environment for GPU computing integrated into Microsoft Visual Studio. Parallel Nsight enables developers to easily create GPU-accelerated applications for a range of desktop and supercomputing platforms.

PGI Accelerator Workstation

The Portland Group's high performance native parallelizing and optimizing OpenMP Fortran, C99 and ANSI C++ compilers. Includes support for PGI's three programming models for NVIDIA GPUs:

  • PGI Accelerator high level programming model for x64+GPU platforms by adding OpenMP-like compiler directives and pragmas to Fortran and C programs. Portable, incremental, and easy to use for application domain experts.
  • PGI CUDA C/C++ compiler for x86 platforms. CUDA developers can now compile and optimize their CUDA applications to run on x86-based workstations, servers and clusters with or without an NVIDIA GPU accelerator.
  • CUDA Fortran is a Fortran analog to NVIDIA CUDA C. Includes a Fortran 2003 compiler and tool chain for programming NVIDIA GPUs using Fortran.

PGI Accelerator Visual Fortran

PGI Accelerator Visual Fortran brings the PGI suite of high-performance 64-bit and 32-bit parallel Fortran compilers to Microsoft Windows developers using Microsoft Visual Studio. In addition to automatic and OpenMP parallelization support for optimizing performance on multi-core processors, PGI Accelerator Visual Fortran includes support for these PGI programming models for NVIDIA GPUs:

  • PGI Accelerator high level programming model for x64+GPU platforms by adding OpenMP-like compiler pragmas. Portable, incremental, and easy to use for application domain experts.
  • CUDA Fortran is a Fortran analog to NVIDIA CUDA C. Includes a Fortran 2003 compiler and tool chain for programming NVIDIA GPUs using Fortran.

Accelereyes LibJacket Library

Accelereyes LibJacket is the World's largest GPU computing software library. Thousands of function syntaxes in C, C++, Fortran, and Python. Includes the popular GFOR loop for running all iterations of a FOR loop simultaneously on the GPU.

The Accelereyes LibJacket Library:

  • Outperforms CPU libraries
  • Is optimized for any CUDA-enabled GPU. The same code will run on everything: laptops, desktops, servers.
  • Includes thousands of lines of optimized device code.

Accelereyes Jacket Library

The Accelereyes Jacket Library is the premier GPU solution for the M language found in Matlab. The Accelereyes Jacket Library enables transparent overloading of regular M functions to run on the GPU. Includes the popular GFOR loop for running all iterations of a FOR loop simultaneously on the GPU.

The Accelereyes Jacket Library:

  • Outperforms Matlab CPU version (see more) and it outperforms the Parallel Computing Toolbox
  • Optimized for any CUDA-enabled GPU. The same code will run on everything: laptops, desktops, servers.
  • Includes thousands of lines of optimized device code.

CULA™ by EM Photonics

CULA is a set of GPU-accelerated linear algebra libraries offering developers and engineers supercomputing performance. No GPU programming experience is required to use CULA. Programmers can easily call CULA from their C/C++, FORTRAN, MATLAB, or Python codes. CULA works with all GPUs supported by NVIDIA's CUDA and is built for all standard CUDA platforms, including Linux, Windows, and Mac OS X. Used by thousands of developers worldwide, CULA is offered as:

CULA Dense features the most popular and essential LAPACK and BLAS routines performing up to an order of magnitude faster than CPU-based linear algebra solvers. Contains routines for systems solvers, singular value decompositions, and eigenproblems.

CULA Sparse offers iterative solvers for sparse matrix systems with performance that is 10X faster than competing solutions. Multiple algorithms, preconditioners, and data storage formats are supported.

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