Ofertes de projectes
Consulta ofertes d'altres estudis i especialitats
Machine Learning (ML) has taken the world by storm and has become a fundamental pillar of engineering. As a result, the last decade has witnessed an explosive growth in the use of deep neural networks (DNNs) in pursuit of exploiting the advantages of ML in virtually every aspect of our lives: computer vision, natural language processing, medicine or economics are just a few examples. However, NOT all DNNs fit to all problems: convolutional NNs are good for computer vision, recurrent NNs are good for temporal analysis, and so on. In this context, the main focus of N3Cat and BNN-UPC is to explore the possibilities of the new and less explored variant called Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured data. This has huge implications in fields such as quantum chemistry, computer networks, or social networks among others. OBJECTIVES =========== N3Cat and BNN-UPC are looking for students wanting to work in the area of Graph Neural Networks studying their uses, processing architectures, and algorithms. To this end, the candidate will work on ONE of the following areas: - Investigating the state of the art on this area, surveying the different works done in terms of applications, processing frameworks, algorithms, benchmarks, datasets. This can be taken from a hardware or software perspective. - Helping to build a testbed formed by a cluster of GPUs that will be running pyTorch or Tensorflow. We will instrument the testbed to measure the computation workload and communication flows between GPUs. - Analyzing the communication workload of running a GNN either in the testbed or by means of architectural simulations. - Developing means of accelerating GNN processing in software (e.g., improving scheduling of the message passing) or hardware (e.g. designing a domain-specific architecture).
Companies and scientists working in areas such as finance or genomics are generating enormously large datasets (in the order of petabytes) commonly referred as Big Data. How to efficiently and effectively process such large amounts of data is an open research problem. Since communication is involved in Big Data processing at many levels, at the NaNoNetworking Center in Catalunya (N3Cat) we are currently investigating the potential role of wireless communications in the Big Data scenario. The main focus of the project is to evaluate the impact of applying wireless communications and networking methods to processors and data centers oriented to the management of Big Data. OBJECTIVES =========== N3Cat is looking for students wanting to work in the area of wireless communications for Big Data. To this end, the candidate will work on one of the following areas: - Traffic analysis of Big Data frameworks and applications, as well as in smaller manycore systems. - Channel characterization in Big Data environments: indoor, within the racks of a data center, within the package of CPU, within a chip. - Design of wireless communication protocols for computing systems from the processor level to the data center level.
Navigation meshes are necessary to represent the walkable space of an environment so that agents can perform pathfinding and move through them. Current navigation meshes tend to flatten the environmetn to represent it as 2D polygons connected by edges (square cells, triangles or larger convex polygons). This abstraction presents problems when dealing with complex outdor geometry where the terrain may not be completely flat. With this project, we would develop a novel navigation mesh that can keep the complexity of any 3D input geometry, while still generating small graphs
GPUs are increasingly considered for safety critical systems such as autonomous driving. Vulkan SC and SYCL SC (Safety Critical) are the new GPU programming models focusing on these types of systems. However, there are still many open development and research points to be explored in order to increase their use. Several master theses related to these technologies are available at the Barcelona Supercomputing Center (BSC).
Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, a quantum computer faces many challenges relative to the movement of qubits which is completely different than the movement of classical data. This thesis delves into these challenges and proposes solutions to create scalable quantum computers
We have developed LoRaMesher, an on-going implementation for doing mesh networking with LoRa nodes. https://github.com/LoRaMesher/LoRaMesher The TFM will develop LoRaMesher further on a specific topic, such as embedded systems, network level, machine learning or application level, according to the interest.
Reading charts in desktop and virtual reality environments can be difficult depending on the configuration of different parameters such as the width and height of different visual marks. The project aims to explore the perceptual limits of a set of well-known visualization techniques in desktop and VR-based environments.
UPC and Nestlé are offering a new position to develop the TFM in the field of Machine Learning and Cybersecurity. This TFM will be fully funded (internship) and carried out in collaboration with the Global Security Operations Center of Nestlé and UPC.
Rust has been increasingly considered for use in space systems thanks to its memory safety and built in support for parallelism. In this thesis, a set of open source benchmarks for on-board space applications which have been developed at the Barcelona Supercomputing Center for the European Space Agency (ESA) will be ported to Rust and their performance and programmability will be evaluated compared to implementations in other not-memory safe languages such as C. The work will be performed in the Barcelona Supercomputing Center.
Software randomisation at source code level is an effective solution to increase the safety and the security of safety critical systems. Previous prototypes at BSC have been implemented for C and CUDA. In this thesis, a software randomisation source to source compiler will be implementated for C++ in Clang.
Programming models for HPC have to be adapted to a new paradigm where every HPC system has accelerators. In BSC we develop the OmpSs-2 shared-memory programming model, which has support for offloading tasks to GPUs and FPGAs. However, programmers must still manage memory copy operations manually, or use unified memory. The project will consist on implementing automatic memory management inside the OmpSs-2 runtime, tracking and moving data transparently from hosts to accelerators and vice-versa.
The goal of this project is to develop a high-performance game engine for large-multicore and accelerated systems. The game engines are the key component that underlay all video games. It is a complex software that has to continuously process user input, execute the game logic, update the game state, produce the next frame and render it in a timely fashion. All these activities are usually expressed as a Directed Acyclic Graph (DAG) of tasks.
SPEChpc is a benchmark suite that provides a wide set of applications designed to measure the real-world performance of HPC systems. They include distributed, hybrid and accelerated (CUDA) workloads. This project consists on porting some of the applications on SPEChpc to the OmpSs-2 programming model, a data-flow task-based model which is developed in the BSC. The goal is to improve performance of SPEChpc applications leveraging OmpSs-2's advanced features
This master's thesis aims to analyze the feasibility of a remote VR system based on the use of mobile devices with cardboard glasses and low-cost interaction devices. It will start from a system based on HTC-VIVES programmed with Unity. Different portability alternatives to the new platform will be analyzed both in terms of the rendering of the models (locally or on a server) and the limitations of the interaction and connection between students and teacher. A prototype will be developed with basic interaction techniques and its usability will be analyzed.
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