By providing bindings between Interactive Data Language (IDL) and large function libraries, GPULib can accelerate new applications or be incorporated into existing applications with minimal effort. No knowledge of GPU programming or memory management is required.
GPULib is built on top of NVIDIA's Compute Unified Device Architecture (CUDA) platform. CUDA is supported by a wide range of NVIDIA products, including GeForce, Quadro, and Tesla cards.
Note: By default, GPULib supports only IDL 8.4 and CUDA 6.5. However, for interested GPULib Basic customers Tech-X may perform custom builds with different versions of IDL and CUDA.
Speed increases due to GPULib.
Hardware NVIDIA® Tesla® C2070 vc. Intel Xeon® CPU X5650 (2.67 GHz).
(All 12 Xeon cores were use in performance test.)
|Algorithm or Demo||Speedup|
|FFT (250x250 to 10000x10000)||10-60x|
|decon_hubble_demo (FFT based deconvolution)||7.7x|
|LGamma demo (1e6 element arrays)||22.8x|
|Levenberg-Marquardt fitting algorithm||6.25x|
Levenberg-Marquardt fitting (bottom) is used to retrieve the value for saturated pixels (top) by fitting a sum of Gaussian functions to the original data. GPU evaluation of the fitting function improves the performance of the fitting algorithm by a factor of 6.25 times.
Deconvolution of a Hubble Space Telescope image (left) and the same image after GPULib is used to sharpen the quality (right). On a system with an NVIDIA Tesla C2070 and Intel® Xeon® CPU X5650 at 2.67GHz, GPULib computed the deconvolution in 0.317 seconds vs. 2.15 seconds for the standard IDL array.
A comparison of the progress of a finite-difference time-domain (FDTD) simulation on CPU (left) and GPU (right) after the same amount of time visually demonstrates the GPU speed-up. The simulation launches an electromagnetic pulse with finite extent on the left and propagates it through the domain.
This simulation launches an electromagnetic pulse with finite extent on the left and propagates it through the domain. The upper results are given by GPULib, a library for accelerating IDL using NVIDIA graphical processing units (GPU), while the lower results are provided by IDL on the CPU.
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Directions for installing GPULib are available in the README.txt file inside the distribution.