Research
Computer Systems
Context-Aware and Context-Adaptive Code Optimization for New General High-performance Computers
Xiaoming Li
- Custom instrumentation and power conversion circuits for extremely efficient solar energy conversion
- Design of 2D Read-out Integrated Circuit for 3-D Laser-radar Imaging Systems
- Driver and receiver circuits for extremely low-power and low electromagnetic interference (EMI) multi-gigabit electrical signaling for small portable and mobile equipment
- Driver and receiver circuits for Optical Fuzing and Scene Generation
- Context-Aware and Context-Adaptive Code Optimization for New General High-performance Computers
- Intelligent Profiling and Code Generation Techniques for Multi-core Processors
- Machine Learning Based Library Generation Techniques for Multi-core Processors and GPUs
- Propagation and Mobility Modeling for Urban Mesh Networks
- Modeling and simulation of urban mesh networks
- Mobility management for large-scale urban mesh networks
Current funding
NSF
Group Staff
Graduate Student
Murat Bolat
The new high-performance computing platforms, such as Chip Multi Processors (CMP) and Graphic Processing Units (GPU), designates everyone a user of high-performance computing. The new high-performance computing platforms will likely include a large number of simultaneously multi-hreaded processor cores on a chip and resources that are shared among the cores.
Unlike the traditional scientific-computing-oriented high-performance computers, the new high-performance computers will simultaneously run multiple programs and a large number of threads competing for shared resources, such as caches. This presents unprecedented challenges for code optimization of the new general high-performance computers. First, the ubiquitous existence of multi-program and multi-thread makes a program run in a runtime environment that differs significantly from that of traditional high-performance computers.
Threads have extensive resource competition among themselves. Second, the execution environment is always changing during the life of a program. Consequently, a program that is optimized in only one way will not perform equally well for the changing runtime environment. To tackle the challenges, our group will develop program optimization and runtime adaptation techniques to address the unique challenges facing the new high-performance computers that must be solved in order to ensure their successes.
Recent publications
Haiping Wu, Eunjung Park, Mihailo Kaplarevic, Yingping Zhang, Murat Bolat, Xiaoming Li, Guang R. Gao, "Automatic Program Segment Similarity Detection in Targeted Program Performance Improvement", Workshop on Performance Optimization for High-Level Languages and Libraries, in conjunction with 21st IEEE International Parallel & Distributed Processing Symposium (IPDPS), March 2007.
