Cache-oblivious and adaptive algorithms in symbolic computation

The pervasive ubiquity of parallel architectures and memory hierarchy has led to the emergence of a new quest for parallel mathematical algorithms and software capable of exploiting the various levels of parallelism: from hardware acceleration technologies (multi-core and multi-processor system on chip, GPGPU, FPGA) to cluster and global computing platforms. In this project, we propose to revisit fundamental algorithms in symbolic computation so as to optimize them in terms of data locality and parallelism and adapt them to these new modern computer architectures.

Yuzhen Xie
Faculty Supervisor: 
Drs. Ilias Kotsireas and Marc Moreno Maza