Available thesis proposals:
- Parallel and distributed scientific applications: performance and efficiency
- Energy efficiency and sustainability in computation
- Resource-allocation mechanism for voluntary large-scale distributed systems
|
Thesis proposals |
Researchers |
Research Group |
|
Parallel and distributed scientific applications: performance and efficiency There are currently various bottlenecks in the growth in parallel and distributed programming paradigms and environments, which are affecting the ability to provide efficient applications for performing concurrent computations. We need to know the platforms, their performance, the underlying hardware and networking technologies, and we must be able to produce optimized software that statically or dynamically may take advantage of the computational resources available. In this line of research we study different approaches to producing better scientific applications, and to making tools (via automatic performance analysis), which can understand the application model and the underlying programming paradigm. We try to tune the performance of these to a dynamically changing computational environment, in which the resources (and their characteristics) can be homogeneous or heterogeneous depending on the hardware platform. In particular we focus our research on shared memory and message-passing paradigms, and in many-core/multi-core environments including multi-core CPUs, GPUs (graphic cards computing) and cluster/grid/cloud/super computing platforms. |
Mail: jjorbae@uoc.edu |
|
|
Energy efficiency and sustainability in computation
In the current growth of HPC/Cloud/IA big data centers, there is a need for more energy efficiency and more sustainable sources of energy. In some cases, the initial focus comes in the form of an overall cost reduction, other times, the main objective is maintaining the computational scalability without excessive cost penalties. In the end, there is a need for more generic models for energy efficiency, and also for searching the best energy sources at each moment in the nowadays big computations.
In this research line, we will focus on different approaches to enhance computation to make it more energy-conscious, and more effective management in cloud/edge hybrid paradigms, mixing remote and local computations. And how to make those environments more sustainable. Also in the HPC/BigData/IA/IoT emerging industrial sectors, they have found different issues, in the form of scalability limitations (penalized by cost), that could lead to a halt in their activity if they do not find more flexible and sustainable ways to control the energy consumed.
We will analyze and design new predictive models to model computational workloads, their energy needs, and the best possible schedule to use different available energetical sources. Also making dynamical interaction models between energy markets and the current huge-scale computational needs.
We are looking for PhD candidates interested in helping to design computational systems more energy-aware and making better future models to optimize computation taking into account energy sources and their sustainability.
|
Mail: jjorbae@uoc.edu Mail: ipisad@uoc.edu |
WINE |
|
Resource-allocation mechanism for voluntary large-scale distributed systems We work on a multi-criteria allocation mechanism for large-scale distributed systems using computers voluntarily provided by participants in the system.
This PhD proposal is aimed at improving our multi-criteria allocation mechanism. Possible research lines:
Our main papers related to this allocation mechanism are as follows: It defines the basis of our multi-criteria allocation mechanism (MCBR).
2. CLARA: A novel clustering-based resource-allocation mechanism for exploiting low-availability complementarities of voluntarily contributed nodes. 2022. Future Generation Computer Systems, Vol. 128, pp. 248-264. It improves the allocation mechanism by allowing groups of complementary nodes to behave as a node. To find the complementarities between the available computers, we must first predict their availability and then sort them into groups using a clustering technique.
3. NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks. 2025. Future Generation Computer Systems, Vol. 164. It extends the multi-criteria mechanism with network information. |
Mail: jmarquesp@uoc.edu |