Anastasia Ailamaki will investigate the potentials of hardware/software co-design for efficient utilization of micro-architectural resources in collaboration with Huawei. Past research has shown that that DBMSs severely under-utilize their micro-architectural resources with more than 50% of the CPU cycles going for memory stalls and the number of retired instructions per cycle barely reaching one on machines that are able to retire four instructions per cycle. Pure software-level optimizations are not enough to fully exploit the micro-architectural resources. This under-utilization limits the performance of DBMSs and leads to poor energy efficiency. The goal of the project is to reconsider the design of OLTP systems by making the utilization of micro-architectural resources the highest priority so as to achieve high throughput, low latency, hardware utilization and better energy efficiency.
- James Larus Wins MICRO Test of Time Award November 8, 2018
- Machine Learning in the Face of Adversity October 22, 2018
- Martin Jaggi wins Google Focused Research Award September 24, 2018
- ACM SIGSPATIAL Best Paper Award for DIAS Researchers September 17, 2018
- DIAS Scientist Wins Chorafas Award September 10, 2018