Please note: This seminar will take place online.
Xiangyao Yu, Assistant Professor
Computer Sciences Department, University of Wisconsin-Madison
The performance gap between GPUs and CPUs has been widening over years as the hardware improves. Existing GPU databases demonstrate good performance, but suffer from limited GPU memory capacity and PCIe bandwidth, thereby failing to scale to large datasets. We conduct a series of projects to address these challenges, paving the way for wider GPU database adoption.
In particular, I will present several projects: (1) an execution engine optimized for GPU architecture, (2) efficient data compression and decompression in GPU, (3) heterogeneous CPU-GPU query processing, (4) optimized user-defined functions, and (5) multi-GPU databases. We believe GPUs can potentially become the new modality of SQL analytics in the near future.
Bio: Xiangyao Yu is an Assistant Professor at the University of Wisconsin-Madison. His research interests include (1) cloud-native databases, (2) new hardware for databases, and (3) core DB techniques in both transaction and analytical processing. Before joining UW-Madison, he finished postdoc and PhD at MIT. Xiangyao received the NSF CAREER Award and the Sloan Research Fellowship.