Academia
Today's students are tomorrow's scientists, engineers, analysts, researchers, developers, and pioneers in industry, government, and academia. And it is here, in the education of undergraduates and postgraduate students that AccelerEyes began and still remains with a strong focus.
ArrayFire & Jacket in Course Instruction
The use of ArrayFire & Jacket in higher education and research is growing deep roots all over the globe. The world's most prominent universities and research institutions, such as Princeton, Caltech, Harvard, Oxford, Max Planck, MIT, Stanford, and many others use AccelerEyes' software, both for research and in course instruction.
Because of its unrivaled ability to streamline the process of innovation, ArrayFire & Jacket are easily integrated into the academic framework. Follow this link for a free download of Jacket class handouts from the Aalborg University, Denmark.
University Collaboration Program with Jacket
AccelerEyes is deeply involved in collaborating with universities to set up Campus-Wide Licenses. These licenses enable faculty, staff and students to harness the power of Jacket from their familiar workstation environments, while minimizing the IT effort required to set those machines up with individual licenses. Further, dedicated phone support is available for free under the University Collaboration Program.
Read more about the Campus-Wide License.
Example Applications
ArrayFire and Jacket can help educate students and support research in many application areas including:
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Comparing Jacket with Parallel Computing Toolbox™ for
various algorithms Stanford University |
Speedup: 8.5X |
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Comparison of Jacket versus PCT for various domain-specific algorithms
Authors: H. Chafi, K. Sujeeth, K. Brown, H. Lee,
A. Atreya, and K. Olukotun, Stanford University Researchers in the Pervasive Parallelism Laboratory at Stanford University published work describing a novel framework for parallel computing with a paper entitled, "Domain-Specific Approach to Heterogeneous Parallelism." Part of their research involved benchmarking the performance of Jacket and the Parallel Computing Toolbox™. They compared performance for algorithms such as Gaussian Discriminant Analysis, Restricted Boltzmann Machines etc. and found Jacket to be faster in every case. Last Updated: 31 Jan 2011 |
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Accelerating LTE Simulation Using Jacket Tsinghua University |
Speedup: 3X |
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Accelerating LTE Simulation Using Jacket
Authors: Yuan Gao, Yin Sun, Chun Hui Zhou, Xin Su, Xi Bin
Xu, Shi Dong Zhou, Tsinghua University Simulation in MATLAB is a driving force in several research projects. However, the accompanying long simulation times can tend to be a drag in many of these projects. In this article, we shall bring up the example of the work on 3GPP LTE System Simulation by Yuan Gao et al (from Tsinghua University, Beijing) and demonstrate how the use of Jacket can significantly improve the simulator performance and lead to faster validation times in simulation projects. Last Updated: 8 Aug 2011 |
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High Performance Compressive Sensing Rice University |
Speedup: 5X |
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High Performance Compressive Sensing
Authors: Nabor Reyna and Wotao Yin from Rice University This work deals with reconstruction of signals using partial Fourier matrices (RecPF). The major computational components of the algorithm involve shrinkage and FFTs. Jacket is employed to accelerate this compute-heavy code. Last Updated: 27 Jul 2011 |
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Power System Simulations Indian Institute of Technology, Roorkee, India |
Speedup: 35X |
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Power Flow on the GPU with MATLAB
Authors: Indian Institute of Technology, Roorkee Power flow studies are one of the most important aspects of power system planning and operation. The power flow reveals the sinusoidal steady state characteristics of the entire system - voltages, real and reactive power generated, and absorbed and line losses- elucidating the voltage magnitudes and angles at each bus, the generation of each generating unit, and real and reactive power losses in the system. All this is necessary to ensure the security, economy, and control of electrical energy distribution. Learn how Jacket, GPUs, and MATLAB can deliver magnitudes of performance improvement over CPU-based solutions. Last Updated: 18 Apr 2010 |
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Antenna Array Simulations University of Naples Federico II |
Speedup: 4.5X |
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Using Jacket to design and simulate echo generators
Authors: A. Capozzoli, C. Curcio, A. Liseno at University
of Naples Federico II Antenna array design involves repeated simulation to tune the many parameters involved, and waiting around for simulations to finish is no fun. Offloading the optimization problem onto the GPU cuts that time down significantly. In their recent paper, Capozzoli, Curcio, and Liseno of University of Naples Federico II demonstrated how a simple modification to their echo generator array simulation took advantage of the GPU to bring immediate speedups. Last Updated: 20 Jul 2011 |
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Laplace Transform Inversion Acunum Algorithms and Solutions |
Speedup: 3.8X |
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Laplace Transform Inversion on the GPU
Authors: Patrick Kano and Moysey Brio at Acunum
Algorithms and Solutions The numerical inversion of the Laplace transform is a long standing problem due its implicit ill-posedness. Patrick Kano and Moysey Brio of Acunum Algorithms and Solutions, with their experience in computational methods and algorithm development, found a solution that not only works, but is very fast. Last Updated: 13 May 2011 |
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Compressed Sensing for Image Reconstruction College of Engineering, Roorkee, India |
Speedup: 8X |
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Compressed Sensing Algorithms
Authors: Kuldeep Yadav, Ankush Mittal, M.A. Ansar and Avi
Srivastava, College of Engineering, Roorkee, India Compressed sensing is very critical in the areas of medical image reconstruction, image acquisition or sensor networks. An algorithm for compressed sensing developed using a Basis Pursuit Algorithm shows over 8X speedup when run on an NVIDIA GPU. Last Updated: 5 May 2011 |
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Fat/Water Reconstruction for Medical Images Case Western Reserve University |
Speedup: 11.6X |
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Improved Fat/Water Reconstruction Algorithm with Jacket
Authors: D. H. Johnson, S. Narayan, C. A. Flask and
D. L. Wilson, Case Western Reserve University Case Western Reserve University researchers turned to GPUs running Jacket to develop a fast and robust version of the "Iterative Decomposition of water and fat with an Echo Asymmetry and Least-squares" (IDEAL) reconstruction algorithm. This algorithm uses a lot of Image Processing algorithms for reconstruction, and was shown to achieve very high speedups. Last Updated: 25 Mar 2011 |