GPU-parallelisering av astronomisk bildsubtraction

Astronomical image subtraction is a method for generating a difference image from two images, which covers the same area but taken at different times, in order to see changes over time. Due to the images being taken at different times, one of the images has to be convolved, to match the atmospheric conditions ofthe other image. HOTPANTS is an open source software used for astronomical image subtraction. The problem is that HOTPANTS is written in serial C and therefore does not scale well with growing image sizes. There have been previous efforts to parallelize HOTPANTS, which include P-HOTPANTS and GBAISP. However, these projects are outdated or unavailable, respectively. The latest effort, BACH, is a reimplementation of HOTPANTS in C++, where the convolution and subtraction parts have been parallelized on a GPU using OpenCL. This thesis project is a continuation of BACH, called X-BACH, which aims to parallelize the remaining parts of the HOTPANTS algorithm using OpenCL. The results show that some parts of the HOTPANTS algorithm, excluding convolution and subtraction, are highly suitable for the GPU while other parts arenot suitable for the GPU. It is believed that some parts which are not suitable forthe GPU are highly suitable for CPU parallelization. Overall, running on an external GPU, X-BACH achieves a relative speed of 1 to 2 compared to BACH, and a relative of 0.8 to 2.5 compared to HOTPANTS. When running on an integrated GPU, X-BACH achieves a relative speed of 0.5 to 1.2 compared to BACH, and a relative speed of 0.3 to 2 compared to HOTPANTS. Some parts of the algorithm achieves a speedup of up to 10 times when parallelized on a GPU. In terms of accuracy, X-BACH generally obtains a maximum relative error in order of magnitude ranging from 10−7 to 10−1. However, on certain test images, the algorithm has been observed to be unstable

GPU-parallelisering av astronomisk bildsubtraction

Astronomical image subtraction is a method for generating a difference image from two images, which covers the same area but taken at different times, in order to see changes over time. Due to the images being taken at different times, one of the images has to be convolved, to match the atmospheric conditions ofthe other image. HOTPANTS is an open source software used for astronomical image subtraction. The problem is that HOTPANTS is written in serial C and therefore does not scale well with growing image sizes. There have been previous efforts to parallelize HOTPANTS, which include P-HOTPANTS and GBAISP. However, these projects are outdated or unavailable, respectively. The latest effort, BACH, is a reimplementation of HOTPANTS in C++, where the convolution and subtraction parts have been parallelized on a GPU using OpenCL. This thesis project is a continuation of BACH, called X-BACH, which aims to parallelize the remaining parts of the HOTPANTS algorithm using OpenCL. The results show that some parts of the HOTPANTS algorithm, excluding convolution and subtraction, are highly suitable for the GPU while other parts arenot suitable for the GPU. It is believed that some parts which are not suitable forthe GPU are highly suitable for CPU parallelization. Overall, running on an external GPU, X-BACH achieves a relative speed of 1 to 2 compared to BACH, and a relative of 0.8 to 2.5 compared to HOTPANTS. When running on an integrated GPU, X-BACH achieves a relative speed of 0.5 to 1.2 compared to BACH, and a relative speed of 0.3 to 2 compared to HOTPANTS. Some parts of the algorithm achieves a speedup of up to 10 times when parallelized on a GPU. In terms of accuracy, X-BACH generally obtains a maximum relative error in order of magnitude ranging from 10−7 to 10−1. However, on certain test images, the algorithm has been observed to be unstable

Simulera beteende av stridsflygplan med hjälp av AI : Simulating behavior of combat aircraft with AI

I denna rapport beskrivs ett kandidatarbete som utfördes på beställning av Saab. Det kunden var intresserad av var att simulera beteende av stridsflygplan med hjälp av AI-tekniker. Projektgruppen använde maskininlärningsalgoritmen Q-inlärning för att försöka uppnå detta. Utöver detta utvecklade gruppen en egen simulator som en miljö för kurvstrider mellan simulerade stridsflygplan. Projektet utfördes av sju studenter på programmen civilingenjör inom datateknik och civilingenjör inom mjukvaruteknik vid Linköpings universitet. Resultatet av projektet blev att ett tillfredsställande beteende för det AI-styrda stridsflygplanet ej kunde uppnås, dock kunde ett förbättrat beteende observeras. Mer forskning på området behövs dock för att åstadkomma mer tillfredsställande resultat. Förstärkningsinlärning visar sig vara lovande som metod för att lösa problemet inför framtiden. Diskussioner och insikter om vad som kan göras för att vidareutveckla AI:n för en bättre prestation hos det simulerade planet dokumenterades. I slutet av rapporten finns även individuella bidrag skrivna av var och en av medlemmarna i projektgruppen. De ämnena som valdes berörde projektet, antingen som en del av utvecklingsprocessen eller som en del av temat till projektet