On simulation model complexity
Proceedings of the 32nd conference on Winter simulation
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Software performance testing based on workload characterization
WOSP '02 Proceedings of the 3rd international workshop on Software and performance
Conceptual Modeling and Simulation
ICCD '99 Proceedings of the 1999 IEEE International Conference on Computer Design
Cache modeling and optimization for portable devices running MPEG-4 video decoder
Multimedia Tools and Applications
The Challenges for High Performance Embedded Systems
DSD '06 Proceedings of the 9th EUROMICRO Conference on Digital System Design
Conceptual modeling for simulation: issues and research requirements
Proceedings of the 38th conference on Winter simulation
Multicore design is the challenge! what is the solution?
Proceedings of the 45th annual Design Automation Conference
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Conceptual modeling is drawing more attention among researchers from many organizations and different disciplines including high-performance lowpower multicore systems. As the popularity and demand grow, more processing power is required by the mission critical systems. Several chip vendors have developed teraflop computing systems (like Intel 80-Core Research Chip). Even though IBM offers the Roadrunner supercomputer, NSF and other research organization are looking for more powerful petascale computer system. In a petascale computer system, thousands to millions of processing cores are expected to work together using petabytes of memory to produce the expected performance. A high performance system like Roadrunner requires a very sophisticated power management system. No wonder, such a gigantic system costs millions of dollars. Conceptual modeling offers the opportunity to conduct research on complex and expensive systems in an inexpensive way. In this work, we address some key conceptual modeling issues and discuss a methodology to develop conceptual models of petascale computer systems. Important features of this methodology include data acquisition to improve accuracy by capturing the important details about the system under consideration and (system-to-) model abstraction to make the modeling manageable without eliminating any significant characteristics of the target system. We believe our findings help develop sound conceptual models of complex high performance petascale computer systems.