Universitat Jaume I


Universitat Jaume IAvda. Sos Baynat, s/n12.071-Castellon



High Performance Computing & Architectures (HPC&A)DirectorEnrique S. Quintana-Orti


Depto. De Ingenieria y Ciencia de Computadores

Universitat Jaume I



Institution Background

The Universitat Jaume I (UJI) is the public university in the north of the Valencian Community, a region on the European Mediterranean coast located between the cities of Valencia and Barcelona. Established in 1991, the UJI has positioned itself as a university of proximity characterised by its personal attention, smooth-running management procedures and the high levels of participation of its members in university life, due, among other things, to its convenient size, with about 15,000 students, and its integrated, modern, functional and sustainable campus.


Research Group Background

The High Performance Computing & Architectures (HPC&A) group was created in 1991 at the Universitat Jaume I (UJI). This group pursues the optimization of scientific applications on general-purpose processors as well as hardware accelerators (e.g., GPUs and FPGAs), and their parallelization on clusters and shared-memory mutiprocessors (SMPs, CC-NUMA multiprocessors, and multi- core architectures). The HPC&A group is currently composed of 1 professor, 10 associate professors, and 7 full-time researchers. The group administers its own small data center with two clusters, and several GPU platforms.

The HPCA group’s mid-term objective is to develop software (HPC runtimes, numerical libraries, virtualization middleware, and energy analysis tools) which can be transferred to the industry, thus transferring their research results to the society. UJI maintains strong collaborations with large industrials partners. In the last years HPCA has been highly motivated to pursue research topics related to Green Computing.

During the past years, the members of the HPC&A group have acquired a deep understanding of the architecture of current general-purpose processors as well as hardware accelerators, parallel computing techniques, and existing system tools and applications. Based on this knowledge, the group is currently collaborating with KIT in the application of energy saving strategies to the iterative solution of large sparse linear.


Major Interest in Action Scientific Topics

High performance computing, runtime systems, energy efficiency.