Scalable parallel systems are becoming the standard architecture for high-performance computing. However, achieving close to peak performance requires careful attention to a plethora of system details. Not only do hundreds of processors interact on a microsecond time scale, but also the space of possible performance optimizations is large, complex, and highly sensitive to both application behavior and system software. If the event frequency is high and the number of processors is large (in the hundreds), the aggregate data rate can be many megabytes/second. Moreover, for a fixed application problem size, both processor-interaction frequency and performance data volume can grow superlinearly with the number of processors. Finally, the relations of specific performance metrics to application performance can vary widely across applications and parallel architectures. To understand these relations while managing potentially large volumes of dynamic performance data, we have developed a data-immersive virtual environment, called Avatar, which explores performance data and provides real-time adaptive control of application behavior. >
['Daniel A. Reed', 'Keith A. Shields', 'Will H. Scullin', 'Luis F. Tavera', 'Christopher L. Elford']
Virtual reality and parallel systems performance analysis