Please note: This seminar will take place in DC 1304.
Tianzheng
Wang,
Assistant
Professor
School
of
Computing
Science,
Simon
Fraser
University
Traditional OLTP engines are limited by various latencies, such as I/O, memory stalls, synchronization and scheduling. Hiding such latency has been a major goal to achieve high transaction processing performance, but prior efforts have seen limited adoption by missing joint optimizations that mitigate the impact of multiple latency sources. A prime example is software prefetching which interleaves memory access and compute is often at odds with asynchronous I/O.
In this talk, we will revisit various sources of latency in memory-optimized, larger-than-memory OLTP engines and propose several solutions that allow joint optimizations. We show how I/O and other sources of latency can be hidden to achieve high throughput, without cancelling out the benefits of software prefetching. The gist is a simple but effective scheduling scheme based on lightweight asynchronous I/O and stackless coroutines (e.g., those in C++20). We also emphasize the effort to make these work in an end-to-end database engine and considerations beyond performance, such as programmability and backward compatibility.
Bio: Tianzheng Wang is an assistant professor in the School of Computing Science at Simon Fraser University (SFU) in Metro Vancouver, Canada. His research centres around the making of database systems in the context of modern hardware, new programming language features and primitives, and new applications. His work also often extends to related areas such as operating systems, parallel programming and distributed systems.
Tianzheng Wang received his Ph.D. and M.Sc. degrees in Computer Science from the University of Toronto in 2017 and 2014, respectively. He received his B.Sc. in Computing degree (First Class Honours) from Hong Kong Polytechnic University in 2012. Prior to joining SFU, he spent one year (2017-2018) at Huawei Canada Research Centre (Toronto) as a research engineer. In addition to adoptions by major cloud vendors and startups, his work has been recognized by two ACM SIGMOD Research Highlight Awards (2020 and 2022), a 2019 IEEE TCSC Award for Excellence in Scalable Computing (Early Career Researchers) and nominations for best/memorable paper awards.