GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

Real Time Log Length Measurement Using GPU Accelerated Visual Odometry

This thesis studies GPU accelerated visual odometry in measuring log length. The visual odometry would not suffer slippage nor require recalibration depending type of wood or temperature conditions compared to mechanical measurement. The requirement of the real-time performance is quite high. Image capturing in 120 Hz frequency is needed as log is moved several meters per second by harvester heads. Here GPU acceleration will be used as it can give speedup in magnitude of hundreds or more. Real-time performance is targeted by selecting fast algorithms for subtasks of measurement pipeline and considering possibilities to parallelize algorithm. In many cases performance boost is achieved, but not in expected magnitude. Physical constraints of the graphics card hardware become easily the limiting factor in parallelization. Real-time performance was achieved in this thesis but not with required accuracy. It remained for future work to find out which algorithms would give both targets.  Taman lisensiaatintutkimuksen aiheena on GPU laskennan kaytto konenäköön perustuvassa tukin pituuden mittauksessa. Konenäköön perustuva pituuden mittaus ei tarvitse uudelleen kalibrointia puulajin tai lämpötilan mukaan. Konenäköön perustuvassa mittauksessa myöskaan mittapyöra ei voi luistaa tukin pinnalla. Realiaikaisuuden vaatimus on tässä sovelluksessa korkea. Kuvat on otettu 120 Hz taajuudella, koska leikkuupää liikuttaa tukkia useita metrejä sekunnissa. GPU laskenta potentiaalisesti nopeuttaisi laskentaa tarvittavissa määrin. Realiaikaista vastetta haettiin seka algoritmien valinnalla etta harkitsemalla mahdollisuuksia rinnaisohjelmoinnin käyttämiseen. Monessa tapauksessa vasteet paranivat, vaikka grafiikkakortin ominaisuudet usein rajoittivat rinnaikkaisohjelmoinnista saatavaa hyötyä. Realiaikainen vaste saavutettiin, mutta ei tarvittavalla pituuden mittaamisen tarkkuudella. Molempien tavoitteiden saavuttaminen jai mahdollisten jatkotöiden tehtäväksi

GPU-accelerated 3D visualisation and analysis of migratory behaviour of long lived birds

With the amount of data we collect increasing, due to the efficacy of tagging technology improving, the methods we previously applied have begun to take longer and longer to process. As we move forward, it is important that the methods we develop also evolve with the data we collect. Maritime visualisation has already begun to leverage the power of parallel processing to accelerate visualisation. However, some of these techniques require the use of distributed computing, that while useful for datasets that contain billions of points, is harder to implement due to hardware requirements. Here we show that movement ecology can also significantly benefit from the use of parallel processing, while using GPGPU acceleration to enable the use of a single workstation. With only minor adjustments, algorithms can be implemented in parallel, enabling for computation to be completed in real time. We show this by first implementing a GPGPU accelerated visualisation of global environmental datasets. Through the use of OpenGL and CUDA, it is possible to visualise a dataset containing over 25 million datapoints per timestamp and swap between timestamps in 5ms, allowing for environmental context to be considered when visualising trajectories in real time. These can then be used alongside different GPU accelerated visualisation methods, such as aggregate flow diagrams, to explore large datasets in real time. We also continue to apply GPGPU acceleration to the analysis of migratory data through the use of parallel primitives. With these parallel primitives we show that GPGPU acceleration can allow researchers to accelerate their workflow without the need to completely understand the complexities of GPU programming, allowing for orders of magnitude faster computation times when compared to sequential CPU methods