I am just finishing off reading the chapters of the Processor and System-on-Chip Simulation book (where I was part of contributing a chapter), and just read through the chapter about the Tensilica instruction-set simulator (ISS) solutions written by Grant Martin, Nenad Nedeljkovic and David Heine. They have a slightly different architecture from most other ISS solutions, since that they have an inherently variable target in the configurable and extensible Tensilica cores. However, the more interesting part of the chapter was the discussion on system modeling beyond the core. In particular, how they deal with interrupts to the core in the context of a temporally decoupled simulation.
Monthly Archives: February 2011
I previously blogged about the HAVEGE algorithm that is billed as extracting randomness from microarchitectural variations in modern processors. Since it was supposed to rely on hardware timing variations, I wondered what would happen if I ran it on Simics that does not model the processor pipeline, caches, and branch predictor. Wouldn’t that make the randomness of HAVEGE go away?
When I was working on my PhD in WCET – Worst-Case Execution Time analysis - our goal was to utterly precisely predict the precise number of cycles that a processor would take to execute a certain piece of code. We and other groups designed analyses for caches, pipelines, even branch predictors, and ways to take into account information about program flow and variable values.
The complexity of modern processors – even a decade ago – was such that predictability was very difficult to achieve in practice. We used to joke that a complex enough processor would be like a random number generator.
Funnily enough, it turns out that someone has been using processors just like that. Guess that proves the point, in some way.