Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months. Statistical sampling and techniques like SimPoint that pick small sets of execution samples have been shown to provide accurate results while significantly reducing simulation time. The inefficiencies in sampling are (a) needing the correct memory image to execute the sample, and (b) needing to having warm architecture state when simulating the sample. In this paper we examine efficient Sampling Startup techniques for representing the correct memory image during simulation, and for dealing with warmup. Representing the correct memory image ensures the memory values consumed during the sample's simulation are correct. Warmup techniques focus on reducing error due to the architecture state not being fully representative of the complete execution that proceeds the sample to be simulated. This paper presents several Sampling Startup techniques and compares them against previously proposed techniques for both uniprocessor and simultaneous multithreading architecture simulation.
The authors of these documents have submitted their reports to this technical report series for the purpose of non-commercial dissemination of scientific work. The reports are copyrighted by the authors, and their existence in electronic format does not imply that the authors have relinquished any rights. You may copy a report for scholarly, non-commercial purposes, such as research or instruction, provided that you agree to respect the author's copyright. For information concerning the use of this document for other than research or instructional purposes, contact the authors. Other information concerning this technical report series can be obtained from the Computer Science and Engineering Department at the University of California at San Diego, email@example.com.
[ Search ]