Student Posters Repository

My research has always concentrated on improving numerical simulations. First I have worked to address the automatic detection of parallelism in its source code and the generation of an efficient counterpart for heterogeneous architectures. The proposal generates OpenMP code for multicores, and OpenHMPP (an OpenACC precursor) for GPUs, focusing on the locality of reference. It has been licensed and is being exploited by the spin-off company Appentra for the creation of the Parallware tools.

Then I have continued collaborating on the improvement of the locality of reference in scientific codes by rebuilding affine loop nests from a trace of memory accesses. The method does not require user intervention nor usage of source/binary codes. Potential applications include hardware and software prefetching, data placement for locality optimization, dependence analysis, optimal design of embedded memory systems, etc. 

My next project is to break the traditional paradigm in the analysis of the results of computational simulations. It typically starts after the simulation has finished but, thanks to the distributed streaming dataflow engine Apache Flink, data can be processed while being generated. In this way, it could be beneficial to reduce communications using the same cluster for simulation and analysis. Resources would need to be managed carefully, and the real-time resource scaling for Big Data workloads seems a good point to explore.