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.

Faculty Supervisor:

Drs. Ilias Kotsireas and Marc Moreno Maza

Student:

Yuzhen Xie

Partner:

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Wilfrid Laurier University

Program:

Elevate

Related projects

Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.

View all
Current openings

Find the perfect opportunity to put your academic skills and knowledge into practice!

Find Projects

Mitacs Partners

The strong support from governments across Canada, international partners, universities, colleges, companies, and community organizations has enabled Mitacs to focus on the core idea that talent and partnerships power innovation — and innovation creates a better future.

Government
Academic
Industry
Innovation

International

Load More

International

Load More

Join a thriving innovation ecosystem.

Subscribe to our newsletter now!

Subscribe