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GPUs are more and more used as low cost high performance computing platforms. While new parallel computing architectures and languages such as OpenCL and CUDA, as well as some new libraries ease up their programming, it is still relatively difficult to design code for them in an efficient way and it gives us a taste of what pioneers experimented in the 50's when programming the first computers. Also most of the current implementations suppose the co-existence of CPUs and GPUs, making the communication between those a real bottleneck in the proposed architectures. However, while new generations of GPU and architectures arise, a set of open questions that the Panel will attempt to address are still pending: — GPU and energy-efficiency: friend or foe? — Can GPU accelerate applications which are not ‘embarrassingly’ parallel? — Non-proprietary alternatives to GPU? — Programming support for GPU. Are we limited to imperative languages? — GPU clusters? — Security risks related to GPU? The Panel will also discuss the current state-of-the art and the opportunities and challenges ahead.