Software development is a continuous decision-making process that mainly relies on the software engineer’s experience and intuition. Furthermore, we report the challenges we have faced and discuss the lessons learned during the development process. In this study, we present the development process of a knowledge management system for ENVironmental Research Infrastructures, which are crucial pillars for environmental scientists in their quest for understanding and interpreting the complex Earth System. Thus, a knowledge management system is required to discover knowledge effectively, automate the knowledge acquisition based on artificial intelligence approaches, integrate the captured knowledge, and deliver consistent knowledge to agents, research communities, and end-users. Additionally, the exponential growth rate of knowledge in a specific domain surpasses human experts’ ability to formalize and capture tacit and explicit knowledge efficiently. Accordingly, researchers and practitioners face fundamental challenges introduced by fragmented knowledge from heterogeneous, autonomous sources with complicated and uncertain relations in particular research domains. However, such research infrastructures are typically domain-specific and often not connected. They provide rich data sources for scientists, such as services and software packages, via catalog and virtual research environments. ![]() Research infrastructures play an increasingly essential role in scientific research. Finally, we give benchmarks showing the performance of parallel collection operations. We show how to implement concrete parallel collections such as parallel arrays and parallel hash maps, proposing an efficient solution to parallel hash map construction. Our framework is easy to use and straightforward to extend to new collections. We present an approach to parallelizing collection operations in a generic way, which can be used to factor out common parallel operations in collection libraries. Their implementation often relies on iterators, which are not applicable to parallel operations due to their sequential nature. Such bulk operations usually traverse the entire collection and process the elements sequentially. These data structures come with a range of predefined operations which include sorting, filtering or finding elements. Modern languages and platforms provide collection frameworks with basic data structures like lists, hashtables and trees. Most applications manipulate structured data. ![]() I will focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism, and I hope to present performance numbers. In this talk I will describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. Blelloch’s pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). But that doesn’t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases.Ī particularly promising and well-studied approach to employing large numbers of processors is to use data parallelism. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. Our paper thus shows that the quantitative intensional information contained in the effort measure T can be abstracted away by the use of ○ and completely recovered by a suitable semantic interpretation of proofs.If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. We enrich intuitionistic logic with a lax modal operator ○ and define a corresponding intensional enrichment of Kripke models M = (W, ⊑, V) by a function T giving an effort measure T(w, u) ∈ N υ $ characterises L and that iLC-h generates complete information about iTh(L).
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