GPULib
 | This article's factual accuracy may be compromised due to out-of-date information. The reason given is: There are concerns this product and licenses for it are discontinued and may no longer be available. However this concern is not supported by a source acceptable to Wikipedia. Please update with reliable sources if applicable or add to the talk page. (September 2018) |
| Developer(s) | Tech-X Corporation |
|---|---|
| Stable release | 1.6.2
/ October 1, 2013 |
| Platform | Microsoft Windows, OS X, and Linux |
| Type | GPGPU |
| License | Proprietary commercial software |
| Website | www |
GPULib is discontinued and unsupported software library developed by Tech-X Corporation[1] for accelerating general-purpose scientific computations from within the Interactive Data Language (IDL) using Nvidia's CUDA platform for programming its graphics processing units (GPUs). GPULib provides basic arithmetic, array indexing, special functions, Fast Fourier Transforms (FFT), interpolation, BLAS matrix operations as well as LAPACK routines provided by MAGMA,[2] and some image processing operations. All numeric data types provided by IDL are supported. GPULib is used in medical imaging, optics,[3][4] astronomy, earth science,[5] remote sensing,[6][7] and other scientific areas.[8]
A CUDA enabled GPU is currently required[9] to use this library, although there is an OpenCL prototype available. GPULib provides more capabilities depending on the capability of the graphics processing unit (GPU) being used. For example, double-precision calculations and the ability to transfer data concurrently with computations are not provided by all GPUs, but GPULib supports these operations on GPUs which are capable of performing them.
GPULib is provided in the form of a Dynamically Loadable Module (DLM) along with IDL code. Using GPULib does not require knowledge of C or CUDA, though it can be extended if the user is knowledgeable with CUDA. GPULib previously provided bindings for other languages including Matlab, Python,[10] and Java.
The GPULib API documentation is available online.[11]
See also[edit]
- CUDA – a parallel computing platform and programming model created by Nvidia and implemented by the graphics processing units (GPUs) that they produce
- GPGPU – general-purpose computation on GPUs
- OpenCL – cross-platform standard supported by both Nvidia and AMD/ATI as well as Intel and others
References[edit]
- ^ "Tech-X Products". Tech-X Corporation. GLULib. Archived from the original on 24 October 2017.
- ^ "MAGMA".
- ^ Cheong, F. C., Krishnatreya, B. J., & Grier, D. G. (2010). Strategies for three-dimensional particle tracking with holographic video microscopy. Optics Express, 18(13), 13563. doi:10.1364/OE.18.013563
- ^ Cheong, F., Sun, B., Dreyfus, R., Amato-Grill, J., Xiao, K., Dixon, L., & Grier, D. (2009). Flow visualization and flow cytometry with holographic video microscopy. Optics Express, 17(15), 13071–13079.
- ^ Fillmore, D., Messmer, P., Mullowney, P., & Amyx, K. (2008). Acceleration of Data Analysis Applications using GPUs. American Geophysical Union, 23, 1099.
- ^ Canty, Morton J. Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition. CRC Press, 2009.
- ^ Rademakers, Lisa and Coleman, Daniel. Spinoff, 2011: NASA Technologies Benefit Society. Government Printing Office, 2012.
- ^ Messmer, P., & Mullowney, P. J. (2008). GPULib: GPU Computing in High-Level Languages. Computing in Science & Engineering, 10, 70–73.
- ^ "CUDA GPUs". 4 June 2012.
- ^ Hetlan, Magnus Lie. Python Algorithms: Mastering Basic Algorithms in the Python Language. Apress, 2010.
- ^ "GPULib 1.6.2 API". Tech-X Corporation. Archived from the original on 17 November 2016.