Graphs model relationships across real-world entities in web graphs, social network graphs, and road network graphs. Graph algorithms analyze and transform a graph to discover graph properties or to apply a computation. For instance, a pagerank algorithm computes a rank for each page in a webgraph, and a community detection algorithm discovers likely communities in a social network, while a shortest path algorithm computes the quickest way to reach a place from another, in a road network. In Domains such as social information systems, the number of edges can be in billions or trillions. Such large graphs are processed on distributed computer systems or clusters. Graph algorithms can be executed on multi-core CPUs, GPUs with thousands of cores, multi-GPU devices, and CPU+GPU clusters, depending on the size of the graph object. While programming such algorithms on heterogeneous targets, a programmer is required to deal with parallelism and and also manage explicit data communication between distributed devices. This implies that a programmer is required to learn CUDA, OpenMP, MPI, etc., and also the details of the hardware architecture. Such codes are error prone and di cult to debug. A Domain Speci c Language (DSL) which hides all the hardware details and lets the programmer concentrate only the algorithmic logic will be very useful. With this as the research goal, Falcon, graph DSL and its compiler have been developed. Falcon programs are explicitly parallel and Falcon hides all the hardware details from the programmer. Large graphs that do not t into the memory of a single device are automatically partitioned by the Falcon compiler. Another feature of Falcon is that it supports mutation of graph objects and thus enables programming dynamic graph algorithms. The Falcon compiler converts a single DSL code to heterogeneous targets such as multi-core CPUs, GPUs, multi-GPU devices, and CPU+GPU clusters. Compiled codes of Falcon match or outperform state-of-the-art graph frameworks for di erent target platforms and benchmarks.

Modelling objects and simulating them do not always map to each other, and often requires defining additional information outside the scope of the original model to achieve an accurate simulation. For example: cables in \textit{AGX Dynamics} (a simulation library from Algoryx AB) are entirely defined by its physical parameters (e.g. Young's modulus, stiffness, etc.), radius, and the route through which the cables run. This thesis explores two approaches to closing the gap between the modelling of a cable and the creation of one in AGX Dynamics through evaluating current methods applied to generating a route and radius from a mesh. Two methods are identified as being useful in generating a route for a cable from a mesh: one which is a surface simplification algorithm, creating approximations of models using non-manifold meshes with radii defined at each vertex, and another method which creates a skeleton from a model using the surface's curvature to gradually shrink the model into a zero-volume shape. Both methods are evaluated using two different approaches to measuring the closeness to the original mesh from the results: using the metric introduced in the surface simplification method applied along the route, and measuring the mean distance from each point on the surface to the route. We show a clear advantage in the first method's inherent way of approximating the radius of the model but also its lack of detail. We also demonstrate that the second method produces more detailed skeletons, but in turn has issues with skewed routes which do not follow the original mesh. Both methods have their own advantages and disadvantages and with improvements to both radius calculations or adaptions to the fundamental algorithms, they could provide a great way of creating AGX cables from mesh models.