GPGPU

4TU Delft
4TU Eindhoven
4TU Twente
4TU Wageningen

Graphics processing units (GPUs) initially were designed to support computer graphics. Their specific architecture provides high processing power and massive parallelism, which allows for efficient solving of typical graphics-related tasks. However, soon it was realised that such architectures are also suitable for many other computational tasks, which led to the development of the area of GPGPU (General Purpose GPU) programming. As a result, GPUs are now used in many different fields, including numerical simulation, media processing, medical imaging, eye-tracking, genomics, fluid dynamics, and machine learning.

Complex GPU applications typically need to be programmed at a relatively low level, since they require expert knowledge on the specific hardware used. Given the importance, range and increasing complexity of GPGPU applications today, the main research challenge for GPGPU programming is that we urgently need systematic techniques that can optimise given GPGPU applications without requiring expert knowledge. This would allow GPGPU applications to be developed at a more convenient higher abstraction level. These techniques should offer a predictable performance increase, while preserving the original behaviour.

Website of GPU

Researchers involved:

  • Dr. Ing. Anton Wijs, TUE Software Engineering & Technology
  • Prof. Dr. Henk Corporaal, TUE Embedded Systems Architecture
  • Dr. Andrei Jalba, TUE Algorithms and Visualization
  • Prof. Dr. Marieke Huisman, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Hajo Broersma, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Gerard Smit, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ir. Jan Kuper, UT Computer Architecture for Embedded Systems (CAES)
  • Prof. Dr. Ir. Marco Bekooij, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ruud van Damme, UT Multiscale Modeling and Simulation 
  • Prof. Dr. Henk Sips, TUD Parallel and Distributed Systems (PDS)
  • Dr. Ir. Alexandru Iosup, TUD Parallel and Distributed Systems (PDS)
  • Prof. Dr. Ir. Dick Epema, TUD Parallel and Distributed Systems (PDS), part-time at TUE System and Networking Engineering group 
  • Prof. Dr. Ir. Kees Vuik, TUD Numerical Analysis 
  • Dr. Ana-Lucia Varbanescu, UVA System and Networking Engineering, guest researcher at TUD Parallel and Distributed Systems (PDS)

Graphics processing units (GPUs) initially were designed to support computer graphics. Their specific architecture provides high processing power and massive parallelism, which allows for efficient solving of typical graphics-related tasks. However, soon it was realised that such architectures are also suitable for many other computational tasks, which led to the development of the area of GPGPU (General Purpose GPU) programming. As a result, GPUs are now used in many different fields, including numerical simulation, media processing, medical imaging, eye-tracking, genomics, fluid dynamics, and machine learning.

Complex GPU applications typically need to be programmed at a relatively low level, since they require expert knowledge on the specific hardware used. Given the importance, range and increasing complexity of GPGPU applications today, the main research challenge for GPGPU programming is that we urgently need systematic techniques that can optimise given GPGPU applications without requiring expert knowledge. This would allow GPGPU applications to be developed at a more convenient higher abstraction level. These techniques should offer a predictable performance increase, while preserving the original behaviour.

Website of GPU

Researchers involved:

  • Dr. Ing. Anton Wijs, TUE Software Engineering & Technology
  • Prof. Dr. Henk Corporaal, TUE Embedded Systems Architecture
  • Dr. Andrei Jalba, TUE Algorithms and Visualization
  • Prof. Dr. Marieke Huisman, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Hajo Broersma, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Gerard Smit, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ir. Jan Kuper, UT Computer Architecture for Embedded Systems (CAES)
  • Prof. Dr. Ir. Marco Bekooij, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ruud van Damme, UT Multiscale Modeling and Simulation 
  • Prof. Dr. Henk Sips, TUD Parallel and Distributed Systems (PDS)
  • Dr. Ir. Alexandru Iosup, TUD Parallel and Distributed Systems (PDS)
  • Prof. Dr. Ir. Dick Epema, TUD Parallel and Distributed Systems (PDS), part-time at TUE System and Networking Engineering group 
  • Prof. Dr. Ir. Kees Vuik, TUD Numerical Analysis 
  • Dr. Ana-Lucia Varbanescu, UVA System and Networking Engineering, guest researcher at TUD Parallel and Distributed Systems (PDS)

GPGPU

Graphics processing units (GPUs) initially were designed to support computer graphics. Their specific architecture provides high processing power and massive parallelism, which allows for efficient solving of typical graphics-related tasks. However, soon it was realised that such architectures are also suitable for many other computational tasks, which led to the development of the area of GPGPU (General Purpose GPU) programming. As a result, GPUs are now used in many different fields, including numerical simulation, media processing, medical imaging, eye-tracking, genomics, fluid dynamics, and machine learning.

Complex GPU applications typically need to be programmed at a relatively low level, since they require expert knowledge on the specific hardware used. Given the importance, range and increasing complexity of GPGPU applications today, the main research challenge for GPGPU programming is that we urgently need systematic techniques that can optimise given GPGPU applications without requiring expert knowledge. This would allow GPGPU applications to be developed at a more convenient higher abstraction level. These techniques should offer a predictable performance increase, while preserving the original behaviour.

Website of GPU

Researchers involved:

  • Dr. Ing. Anton Wijs, TUE Software Engineering & Technology
  • Prof. Dr. Henk Corporaal, TUE Embedded Systems Architecture
  • Dr. Andrei Jalba, TUE Algorithms and Visualization
  • Prof. Dr. Marieke Huisman, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Hajo Broersma, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Gerard Smit, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ir. Jan Kuper, UT Computer Architecture for Embedded Systems (CAES)
  • Prof. Dr. Ir. Marco Bekooij, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ruud van Damme, UT Multiscale Modeling and Simulation 
  • Prof. Dr. Henk Sips, TUD Parallel and Distributed Systems (PDS)
  • Dr. Ir. Alexandru Iosup, TUD Parallel and Distributed Systems (PDS)
  • Prof. Dr. Ir. Dick Epema, TUD Parallel and Distributed Systems (PDS), part-time at TUE System and Networking Engineering group 
  • Prof. Dr. Ir. Kees Vuik, TUD Numerical Analysis 
  • Dr. Ana-Lucia Varbanescu, UVA System and Networking Engineering, guest researcher at TUD Parallel and Distributed Systems (PDS)

Graphics processing units (GPUs) initially were designed to support computer graphics. Their specific architecture provides high processing power and massive parallelism, which allows for efficient solving of typical graphics-related tasks. However, soon it was realised that such architectures are also suitable for many other computational tasks, which led to the development of the area of GPGPU (General Purpose GPU) programming. As a result, GPUs are now used in many different fields, including numerical simulation, media processing, medical imaging, eye-tracking, genomics, fluid dynamics, and machine learning.

Complex GPU applications typically need to be programmed at a relatively low level, since they require expert knowledge on the specific hardware used. Given the importance, range and increasing complexity of GPGPU applications today, the main research challenge for GPGPU programming is that we urgently need systematic techniques that can optimise given GPGPU applications without requiring expert knowledge. This would allow GPGPU applications to be developed at a more convenient higher abstraction level. These techniques should offer a predictable performance increase, while preserving the original behaviour.

Website of GPU

Researchers involved:

  • Dr. Ing. Anton Wijs, TUE Software Engineering & Technology
  • Prof. Dr. Henk Corporaal, TUE Embedded Systems Architecture
  • Dr. Andrei Jalba, TUE Algorithms and Visualization
  • Prof. Dr. Marieke Huisman, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Hajo Broersma, UT Formal Methods and Tools (FMT)
  • Prof. Dr. Ir. Gerard Smit, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ir. Jan Kuper, UT Computer Architecture for Embedded Systems (CAES)
  • Prof. Dr. Ir. Marco Bekooij, UT Computer Architecture for Embedded Systems (CAES)
  • Dr. Ruud van Damme, UT Multiscale Modeling and Simulation 
  • Prof. Dr. Henk Sips, TUD Parallel and Distributed Systems (PDS)
  • Dr. Ir. Alexandru Iosup, TUD Parallel and Distributed Systems (PDS)
  • Prof. Dr. Ir. Dick Epema, TUD Parallel and Distributed Systems (PDS), part-time at TUE System and Networking Engineering group 
  • Prof. Dr. Ir. Kees Vuik, TUD Numerical Analysis 
  • Dr. Ana-Lucia Varbanescu, UVA System and Networking Engineering, guest researcher at TUD Parallel and Distributed Systems (PDS)