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This paper deals with the behavior of virtual environments from the collaboration point-of-view, in which actors (human or virtual beings) interact and collaborate by means of interdependent tasks. In this sense, actors may realize tasks that are dependent on tasks performed by other actors, while the interdependencies between tasks (through resource management and temporal relations) delineate the overall behavior of a virtual environment. Our main goal is to propose an approach for the coordination of those behaviors. Initially a generic study of possible interdependencies between collaborative tasks is presented, followed by the formal modeling (using Petri Nets) of coordination mechanisms for those dependencies. In order to implement such mechanisms, an architecture of reusable and pluggable coordination components is also introduced. These components are used in an implementation of a multi-user videogame. The presented approach is a concrete step to create virtual societies of actors that collaborate to reach common goals without the risk of getting involved in conflicting or repetitive tasks. r

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

Open source software development enabled distributed teams of programmers to contribute to large software systems that became standards in the operation of government and business. Crowdsourcing went further by enabling contributions in the form of small and independent tasks. This allowed teams to scale from dozens to hundreds of people. While crowdsourcing established as industry practice in the areas of software testing, it is challenging for source code related tasks, e.g., software debugging. One of the reasons is that the complex dependencies in the source code can make many tasks difficult to partition and sequence, and later aggregate their outcomes. I am investigating these problems in the context of failure resolution tasks. A failure resolution task consists of inspecting the source code with the objective to identify and explain the root-cause of a software failure. My approach partitions code inspection into questions that are automatically instantiated from templates. I present here my research plan and the early results of experiments on the efficacy, efficiency, and scalability of my approach.