This program is tentative and subject to change.
Even though heavily researched, a full formal model of the x86-64 instruction set is still not available. We present libLISA, a tool for automated discovery and analysis of the ISA of a CPU. This produces the most extensive formal x86-64 model to date, with over 118000 different instruction groups. The process requires as little human specification as possible: specifically, we do not rely on a human-written (dis)assembler to dictate which instructions are executable on a given CPU, or what their in- and outputs are. The generated model is CPU-specific: behavior that is “undefined” is synthesized for the current machine. Producing models for five different x86-64 machines, we mutually compare them, discover undocumented instructions, and generate instruction sequences that are CPU-specific. Experimental evaluation shows that we enumerate virtually all instructions within scope, that the instructions’ semantics are correct w.r.t. existing work, and that we improve existing work by exposing bugs in their handwritten models.
This program is tentative and subject to change.
Wed 23 OctDisplayed time zone: Pacific Time (US & Canada) change
16:00 - 17:40 | Performance Analysis and Optimisation 2OOPSLA 2024 at IBR West Chair(s): Matthew Flatt University of Utah | ||
16:00 20mTalk | Jmvx: Fast Multi-threaded Multi-Version eXecution and Record-Replay for Managed Languages OOPSLA 2024 David Schwartz University of Illinois at Chicago, Ankith Kowshik University of Illinois Chicago, Luís Pina University of Illinois at Chicago | ||
16:20 20mTalk | libLISA: Instruction Discovery and Analysis on x86-64 OOPSLA 2024 Jos Craaijo Open Universiteit, Freek Verbeek Open Universiteit & Virginia Tech, Binoy Ravindran Virginia Tech | ||
16:40 20mTalk | Extending the C/C++ Memory Model with Inline Assembly OOPSLA 2024 Paulo Emílio de Vilhena Imperial College London, Ori Lahav Tel Aviv University, Viktor Vafeiadis MPI-SWS, Azalea Raad Imperial College London | ||
17:00 20mTalk | TorchQL: A Programming Framework for Integrity Constraints in Machine Learning OOPSLA 2024 Aaditya Naik University of Pennsylvania, Adam Stein University of Pennsylvania, Yinjun Wu University of Pennsylvania, Mayur Naik University of Pennsylvania, Eric Wong | ||
17:20 20mTalk | Verification of Neural Networks' Global Robustness OOPSLA 2024 |