<p>Denna rapport behandlar procedurell generering av träd och skapandet av en applikation som med hjälp av multitrådade tekniker genererar och renderar dessa i realtid. Detta exemplifieras av att träden påverkas av vindkrafter. Algoritmen för trädgenerering beskrivs utförligt samt redogör för olika tekniker för att uppnå realtidsprestanda när träden påverkas av vindkrafter.</p>

Denna rapport behandlar procedurell generering av träd och skapandet av en applikation som med hjälp av multitrådade tekniker genererar och renderar dessa i realtid. Detta exemplifieras av att träden påverkas av vindkrafter. Algoritmen för trädgenerering beskrivs utförligt samt redogör för olika tekniker för att uppnå realtidsprestanda när träden påverkas av vindkrafter.

Executable and Translatable UML (xtUML) is a modeling methodology where a system is constructed using a set of UML models and an action language, which can be translated to a target implementation and compiled into an executable program. It allows for good readability and understanding of the workings of the system and relations between its different parts, easy testing and reusability. With a subset of UML diagrams and finite state machines, the actors in the system and the execution progression can be defined. These models are then made into an executable program using a model compiler. Saab Dynamics has developed their own model to C++ compiler, also made using xtUML with the open source tool Bridgepoint. In the current implementation of the compiler, events which trigger a class instance to transition from one state in a state machine to another, are picked from a queue and processed one by one. In theory, a speedup of the execution time for programs could be achieved if multiple events were run simultaneously. To enable parallel execution, additional functionality needs to be added to the compiler to map dependencies between classes and schedule events on threads.To achieve this a parser was implemented, which iterates every state machine and finds statements which access other classes and could result in a potential data race. These shared data accesses are mapped as instances of a Dependency class if at least one writes to it. These are then later used by the compiler to determine for each class to which classes it has a dependency. During execution when events are picked from queue, a check is made for the target class of the event to the currently executing classes on other active threads, to determine if the event is allowed to be processed immediately or if it should be placed in queue again. Threads are created at the start of the program in a thread pool, and are awakened once an independent event is found and added to the thread's own queue. Results from test models compiled using the new version of the model compiler show that the parser finds all data accesses to other classes and accurately maps the dependencies between them. The end results of the programs are equal to that of the serial executions, and the principles of xtUML are maintained. While there are still improvements to be made to increase the parallelization of events, there was a significant speedup in execution time to be seen for models containing time consuming independent state machines.

Cloud-based video processing is an area depending heavily on the hardware’s ability to process huge amounts of packets. Nowadays, we can observe industry drifting away from commonly used FPGAs in lieu of a more flexible software approach. Docker container has been considered a promising technology for constructing video streaming pipelines as it provides a fast and easy way to package and distribute software. Recent developments in the Network Function Virtualization field showed that fast packet processing frameworks like Intel Data Plane Development Kit (DPDK) have a potential to improve the performance of network function chains. This technology could be an enabler for software video processing to approach hardware solutions, yet it is still in quite an early stage and generates many questions about usage, deployment, and performance. This thesis shows that it is possible to build packet processing pipelines using DPDK, running dozens of video processing microservices simultaneously on a single machine. The project implementation was evaluated in terms of latency and throughput and behaviour of co-running applications on a single CPU core was modelled. / Molntjänster Inom området videobearbetning är starkt beroende av hårdvarans förmåga att kontinuerligt bearbeta mycket stora mängder paket. Idag har det blivit vanligt inom professionell användning att välja bort tidigare vanliga FPGA-baserade lösningar, till förmån för mer flexibla mjukvarubaserade lösningar. Docker containers har setts som en lovande teknologi för att konstruera pipelines för strömmande video, då de erbjuder ett snabbt och enkelt sätt att paketera och distribuera mjukvara. Utvecklingen inom virtualisering av nätverksfunktioner (NFV) har visat att ramverk för snabb paketprocessning, såsom Intel DPDK, har potential att förbättra prestandan hos kedjor av nätverksfunktioner. Denna teknologi gör det möjligt för mjukvarubaserad videobehandling att hävda sig i jämförense med hårdvarubaserade varianter. Den är dock relativt ny och oprövad och det kvarstår många öppna frågor om användning, driftsättning och prestanda. Detta examensarbete visar att det är möjligt att bygga paketbearbetande pipelines med DPDK, som kör dussintals nätverksfunktioner samtidigt på en maskin. En implementation har konstruerats och utvärderats med fokus på latens och flöde, och beteendemönster för applikationer som kör samtidigt på samma CPU har modellerats.

Radio access technologies for cellular mobile networks are continuously being evolved to meet the future demands for higher data rates, and lower end‐to‐end delays. In the research and development of LTE, radio network simulations play an essential role. The evolution of parallel processing hardware makes it desirable to exploit the potential gains of parallelizing LTE radio network simulations using multithreading techniques in contrast to distributing experiments over processors as independent simulation job processes. There is a hypothesis that parallel speedup gain diminishes when running many parallel simulation jobs concurrently on the same machine due to the increased memory requirements. A proposed multithreaded prototype of the Ericsson LTE simulator has been constructed, encapsulating scheduling, execution and synchronization of asynchronous physical layer computations. In order to provide implementation transparency, an algorithm has been proposed to sort and synchronize log events enabling a sequential logging model on top of non‐deterministic execution. In order to evaluate and compare multithreading techniques to parallel simulation job distribution, a large number of experiments have been carried out for four very diverse simulation scenarios. The evaluation of the results from these experiments involved analysis of average measured execution times and comparison with ideal estimates derived from Amdahl’s law in order to analyze overhead. It has been shown that the proposed multithreaded task‐oriented framework provides a convenient way to execute LTE physical layer models asynchronously on multi‐core processors, still providing deterministic results that are equivalent to the results of a sequential simulator. However, it has been indicated that distributing parallel independent jobs over processors is currently more efficient than multithreading techniques, even though the achieved speedup is far from ideal. This conclusion is based on the observation that the overhead caused by increased memory requirements, memory access and system bus congestion is currently smaller than the thread management and synchronization overhead of the proposed multithreaded Java prototype.