BIG DATA
Aalborg University in Denmark taps Jacket for research in wireless communication transceivers
Researchers at Aalborg University (AAU) in Denmark are applying new GPU technology to the communications field in a quest to advance theory, modeling, design, implementation, and test of RF systems and circuits.
Aalborg's Department of Electronics Systems and Technology Platforms Section focus on the theory, modeling, design, implementation, and test of RF systems and circuits, mainly within RF-CMOS, HW/SW co-design and design space exploration, advanced circuit and system theory, modeling and analysis as well as synthesis. Recently a significant effort has been started on the use of GPUs to solve numerical problems in the communication technology area, where the ultimate goal is to improve communication systems and devices.
As a stepping stone towards broader use of GPU technology, Professor Torben Larsen's team at AAU has employed a number of systems housing various GPU configurations to explore the possibilities. The systems include:
Theory, modeling, design, implementation, and testing of RF systems and circuits are essentially to address advanced signal processing applications. Signal processing is a natural application area for GPUs and well suited within the Jacket environment. The types of computations performed in signal processing include Power Series, Singular Value Decomposition (SVD), Fast Fourier Transforms (FFT), Basic Linear Algebra Subprograms (BLAS), and optimization among others. The signal processing community is constantly looking for better ways to improve signal processing run times to reach real-time results necessary to deliver on production and research - missions and objectives. The combination of the latest GPU technology, Jacket, and the familiar MATLAB environment has allowed AAU to explore more cost effective and productive ways to execute on research objectives and in turn deliver results to their funding organizations such as the Danish Research Council for Technology and Production, The Danish Research Ministry, The European Union's Framework Programs, and the Danish National Advanced Technology Foundation, in addition to companies like Infineon Technologies, Agilent, Texas Instruments, ETI, RF Micro Devices, and Neurodan.
Professor Larsen's Section has recently received two large research projects in open competition and will before second quarter 2010 have 10 researchers working on different aspects of GPU computing for communication system modeling and simulation. In all cases it will be based on the Jacket computing platform. "We often have few production runs and frequent model changes, which means that Jacket is an ideal solution for us," says Prof. Larsen. He continues saying, "with Jacket we gain a lot of performance for a very modest investment in learning to take advantage of Jacket. We can't afford to use a lot of time to develop C/FORTRAN programs - we rather let the computers together with Jacket do the hard computations. The speed-ups we have seen from Jacket compared to CPU solutions are very, very impressive."
Jacket delivers GPU computing power to domain professionals that have little to no CUDA programming background and would prefer to delay or eliminate parallel programming or low level programming languages like C and C++. It enables faster prototyping and problem solving across a range of signal processing - as well as financial, scientific, and engineering - applications. Jacket eliminates the need to re-program applications into complex languages which would otherwise require advanced programming knowledge and months to complete.
The adoption of Jacket by Larsen's team at AAU has resulted in a unique collaboration between AccelerEyes and AAU. Dr. Larsen provides continuous feedback to the engineering and development team at AccelerEyes to advance Jacket. Most recently Dr. Larsen has launched Torben's Corner on the AccelerEyes Wiki to provide valuable information to the Jacket user community, globally. Torben's Corner provides Tips and Tricks, a Performance Index, and more, for users to gain relevant information directly from another user of Jacket. AccelerEyes and AAU are very excited about providing this resource to Jacket users.
"I hope that Torbens Corner will produce material, which is of interest to the Jacket community. From the beginning I plan on making material for two areas: 1) Tips and Tricks and 2) Jacket Performance Index. The former is as the name says, a plan to show different examples of what Jacket can be used for. The latter will present a rating for how suitable different functions are to be computed by the GPU. In all cases the focus will be on practical uses of Jacket where all source code is public available," says Larsen.
"Having a strong collaboration with Dr. Larsen and his team has already made a big difference for other Jacket users and provides validation for Jacket," said David Gibson, AccelerEyes' vice president of sales and marketing. "AAU is proving to be a leader in the adoption of rapid application development for GPU computing. Our software will help them extend that lead."
Aalborg's Department of Electronics Systems and Technology Platforms Section focus on the theory, modeling, design, implementation, and test of RF systems and circuits, mainly within RF-CMOS, HW/SW co-design and design space exploration, advanced circuit and system theory, modeling and analysis as well as synthesis. Recently a significant effort has been started on the use of GPUs to solve numerical problems in the communication technology area, where the ultimate goal is to improve communication systems and devices.
As a stepping stone towards broader use of GPU technology, Professor Torben Larsen's team at AAU has employed a number of systems housing various GPU configurations to explore the possibilities. The systems include:
- 1. Intel Core i7 975 (3.33GHz) with nVidia Quadro FX-3800 + nVidia GeForce 9800HT + nVidia C1060 Tesla GPUs running Windows 7
- 2. Apple MacBook Pro with Intel Core 2 Duo (2.8 GHz) and nVidia GeForce running Ubuntu Linux
- 3. Apple MacBook Pro with Intel Core 2 Duo (2.8 GHz) and nVidia GeForce 9400M and 9600M GT running Snow Leopard
- 4. Colfax CXT-3000 with four nVidia C1060 Tesla running Windows Vista
- 5. Colfax CXT-5000 with three nVidia C1060 Tesla and one nVidia Quadro FX-5800 running Windows Vista
- 6. Colfax GPU Cluster with 18 Intel X5570s and 16-24 nVidia C1060 Teslas running 64 bit Red Hat Linux - with a 20 Gb/s InfiniBand backbone connecting all nodes
Theory, modeling, design, implementation, and testing of RF systems and circuits are essentially to address advanced signal processing applications. Signal processing is a natural application area for GPUs and well suited within the Jacket environment. The types of computations performed in signal processing include Power Series, Singular Value Decomposition (SVD), Fast Fourier Transforms (FFT), Basic Linear Algebra Subprograms (BLAS), and optimization among others. The signal processing community is constantly looking for better ways to improve signal processing run times to reach real-time results necessary to deliver on production and research - missions and objectives. The combination of the latest GPU technology, Jacket, and the familiar MATLAB environment has allowed AAU to explore more cost effective and productive ways to execute on research objectives and in turn deliver results to their funding organizations such as the Danish Research Council for Technology and Production, The Danish Research Ministry, The European Union's Framework Programs, and the Danish National Advanced Technology Foundation, in addition to companies like Infineon Technologies, Agilent, Texas Instruments, ETI, RF Micro Devices, and Neurodan.
Professor Larsen's Section has recently received two large research projects in open competition and will before second quarter 2010 have 10 researchers working on different aspects of GPU computing for communication system modeling and simulation. In all cases it will be based on the Jacket computing platform. "We often have few production runs and frequent model changes, which means that Jacket is an ideal solution for us," says Prof. Larsen. He continues saying, "with Jacket we gain a lot of performance for a very modest investment in learning to take advantage of Jacket. We can't afford to use a lot of time to develop C/FORTRAN programs - we rather let the computers together with Jacket do the hard computations. The speed-ups we have seen from Jacket compared to CPU solutions are very, very impressive."
Jacket delivers GPU computing power to domain professionals that have little to no CUDA programming background and would prefer to delay or eliminate parallel programming or low level programming languages like C and C++. It enables faster prototyping and problem solving across a range of signal processing - as well as financial, scientific, and engineering - applications. Jacket eliminates the need to re-program applications into complex languages which would otherwise require advanced programming knowledge and months to complete.
The adoption of Jacket by Larsen's team at AAU has resulted in a unique collaboration between AccelerEyes and AAU. Dr. Larsen provides continuous feedback to the engineering and development team at AccelerEyes to advance Jacket. Most recently Dr. Larsen has launched Torben's Corner on the AccelerEyes Wiki to provide valuable information to the Jacket user community, globally. Torben's Corner provides Tips and Tricks, a Performance Index, and more, for users to gain relevant information directly from another user of Jacket. AccelerEyes and AAU are very excited about providing this resource to Jacket users.
"I hope that Torbens Corner will produce material, which is of interest to the Jacket community. From the beginning I plan on making material for two areas: 1) Tips and Tricks and 2) Jacket Performance Index. The former is as the name says, a plan to show different examples of what Jacket can be used for. The latter will present a rating for how suitable different functions are to be computed by the GPU. In all cases the focus will be on practical uses of Jacket where all source code is public available," says Larsen.
"Having a strong collaboration with Dr. Larsen and his team has already made a big difference for other Jacket users and provides validation for Jacket," said David Gibson, AccelerEyes' vice president of sales and marketing. "AAU is proving to be a leader in the adoption of rapid application development for GPU computing. Our software will help them extend that lead."
