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In order to achieve the highest possible performance, the ray traversal and intersection routines at the core of every high-performance ray tracer are usually hand-coded, heavily optimized, and implemented separately for each hardware platform—even though they share most of their algorithmic core. The results are implementations that heavily mix algorithmic aspects with hardware and implementation details, making the code non-portable and difficult to change and maintain.
In this paper, we present a new approach that offers the ability to define in a functional language a set of conceptual, high-level language abstractions that are optimized away by a special compiler in order to maximize performance. Using this abstraction mechanism we separate a generic ray traversal and intersection algorithm from its low-level aspects that are specific to the target hardware. We demonstrate that our code is not only significantly more flexible, simpler to write, and more concise but also that the compiled results perform as well as state-of-the-art implementations on any of the tested CPU and GPU platforms.
Maßgefertigte Abläufe
(2017)
Enterprises demand universities not to limit education to theoretical knowledge, but instead, to prepare students for future challenges in the job. While demanding a focus on current technologies and practices appears reasonable, it contradicts academia’s general focus on sustainable knowledge. This “conflict-ofinterest” can be bridged through extra-curricular professional training. MOOCs are hyped as solution because they allow to simultaneously addressing masses of students. However, with the increasing number of learners, anonymity in education increases and first-level support decreases. Within the extracurricular online program erp4students we found that individual support is considered most relevant to successfully complete professional training.