If you are passionate about performance analysis and optimization, run-time adaptation and SW/HW co-design, as well as collaborative and reproducible experimentation, we would like to draw your attention to several exciting internships at dividiti available for HiPEAC PhD students:
- Collective Knowledge on Deep Learning (apply here).
- Crowdtuning and runtime adaptation of open-source CPU/GPU libraries (apply here).
- Solving grand challenges in computer systems via knowledge sharing and crowdsourcing (apply here).
Collective Knowledge on Deep Learning
Sounds interesting? Please read more about our initiatives in the latest HiPEAC newsletter (1, 2), try out our Android app and... apply!
Crowdtuning and runtime adaptation of open-source CPU/GPU libraries
You will contribute to an ambitious and exciting open-source initiative to enable library crowd-tuning via our Collective Knowledge framework and repository. This initiative will allow the community to easily compare various implementations of library routines across different data sets and diverse hardware, gradually expose more and more optimization choices, continuously crowd-tune such routines, share optimization statistics in a public repository, and automatically assemble the best and possibly adaptive solution for a given platform.
Sounds interesting? Please read more about our initiatives in the latest HiPEAC newsletter (1, 2), and apply!