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Overview

GPULib enables users to access high performance computing with minimal modification to their existing programs. By providing bindings for a number of Very High-Level Languages (VHLLs), including IDL, 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.

Features

What do our customers say?

GPUs are a game-changer for simulating antenna behavior on electrically large platforms and Tech-X is helping us take full advantage of them.

Dr. John S. Asvestas, Naval Air Systems Command (NAVAIR)

GPULib caught my eye immediately as a tailor-made way of boosting the speed of my remote sensing algorithms without having to write my own CUDA kernels.

Dr. Mort Canty, Forschungszentrum Juelich, Germany

Tech-X’s innovative tools are instrumental for visualizing our complex astrophysical simulations and observations.

Dr. Daniel Pomarede, CEA, France

Tech-X's expertise in both numerical methods and GPU computing make them ideal partners for porting complex simulation codes to GPUs.

Dr. Dave Swensen Reaction Engineering Intl., Salt Lake City

Hardware acceleration with Tech-X’s GPUlib gives a 20-fold speed-up over our best CPU implementation.

Prof. Dr. David Grier, New York University

With their GPULib product, Tech-X delivers industry-leading GPU performance that is accessible to our IDL and ENVI customers via an intuitive and easy to use programming interface.

Kirk Benell, CTO, ITT VIS, Boulder, Colorado

GPULib provides the ability to efficiently utilize the power of GPUs for computationally intensive imaging applications in the radiation treatment of cancer.

Prof. John Roeske, Director Radiation Physics, Loyola University, Maywood, IL

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