Propuesta de tesis
Grupo de investigación
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.
Community-owned systems at the edge
Edge computing is a case of cloud computing where a portion of the computing part (data and/or services) is hosted in resources spread in Internet (“at the edges”). By community-owned systems at the edge we refer to systems that host their data and services in personal computers (mostly desktop computers or single-board computers such as Raspberry Pi) voluntarily contributed by participants in the system. Community-owned systems at the edge will be self-owned systems (community members own the computers where data and services are hosted); self-managed (with a decentralized and uncoupled structure); and self-growing. They also share the following characteristics:
(a) No central authority is responsible for providing the required computational resources.
(b) Heterogeneous (software and hardware) and low capacity computer resources spread across the Internet in contrast with high capacity cluster of computers on traditional clouds.
(c) The computational infrastructure belongs to the user and is shared to build the computational infrastructure.
Regarding the reliability and QoS of these community-owned systems at the edge they have
to guarantee to the user:
* Availability: the user can access data anytime from anywhere;
* Freshness: the user gets up-to-date data; and
* Immediateness: the user obtains the data in a time that is felt as immediate.
Therefore, this kind of system has to (a) guarantee a clever and optimal usage of the (likely scarce) contributed resources (storage, bandwidth, and CPU) to avoid wasting them; and (b) provide privacy and security guarantees.
We are looking for PhD candidates interested in large-scale distributed systems applied to community-owned systems at the edge in fields such as (a) optimal allocation of data and services in resources, (b) availability prediction, (c) efficient usage of resources, or (d) privacy and security.
|Dr Joan Manuel Marquès||ICSO|