Tanvir Ahmed Khan earns Towner Prize for Outstanding PhD Research

The award recognizes creative and outstanding research achievements.
Tanvir Ahmed Khan

Tanvir Ahmed Khan, a PhD candidate in computer science and engineering, has been awarded the Richard F. and Eleanor A. Towner Prize for Outstanding PhD Research by the College of Engineering for his creativity and outstanding research achievements. Khan works to improve the efficiency of modern data center processors with optimizations that lie at the intersection of compilers, operating systems, and computer architecture.

To serve billions of users across the planet, modern web applications have to access huge datasets with complex application logic. This scale leads to two major issues for their operation in data centers: poor data access patterns and inefficient instruction fetch operations. Khan has devised a wide range of optimization techniques to address these bottlenecks, making use of a profile-guided approach and software-hardware codesign.

Khan’s work, which he titles “Rescuing Data Center Processors,” is driven by the observation that both data access and instruction fetch in data center applications follow a deeply repetitive pattern. This has allowed him to “profile” these processes and provide predictive performance boosts based on their past behavior. He’s applied this profile-guided design to a number of efficiency-boosting instruction-fetch technologies, including instruction prefetching (I-SPY) and replacement (Ripple), branch target buffer prefetching (Twig) and replacement (Thermometer). All of these techniques have been shown to boost the speed of data center applications, and a number of them are being adopted by real-world data center processor designers like Intel. In addition to these performance optimizations, Khan has also worked on systems that repair bad data access patterns in real time with profiling.

Khan’s research on data center applications’ performance optimizations has appeared in top computer architecture and systems venues like ISCA, MICRO, FAST, EuroSys, PLDI, and OSDI. His performance analysis methodology made real-world workloads twice as faster and consequently was adopted in the Arm Neoverse N1 Core, the leading data center CPU powering 49% of all Amazon Web Service machines.