|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Barrier Invariants : a Shared State Abstraction for Data-dependent GPU Kernels >
If you have a question about this talk, please contact Jonathan Hayman.
Graphics processing units (GPUs) are highly parallel processors that are now commonly used in the acceleration of a wide range of computationally intensive tasks. The focus of this talk is on verifying race-freedom of data-dependent GPU kernels, whose data or control flow are dependent on the input of the program. Kernels in this small but important class are difficult to verify because they require reasoning about shared state manipulated by many parallel threads. Existing verification techniques for GPU kernels achieve soundness and scalability by using a two-thread reduction and making the contents of the shared state nondeterministic each time threads synchronise at a barrier, to account for all possible thread interactions. This abstraction is too coarse for data-dependent kernels.
This talk will present barrier invariants, a new abstraction technique that allows key properties about the shared state of a kernel to be preserved across barriers during formal reasoning. By integrating this technique into the GPU Verify tool, we have verified kernels that were previously beyond existing verification techniques for GPU kernels.
This talk is part of the Logic and Semantics Seminar (Computer Laboratory) series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsKarma Talkies Humanitas and General Science Computer Laboratory Tech Talks
Other talksWhen a stone is not a stone: doing alchemy with plants and animals Ritual, Community, and Conflict Art speak Mutational processes in the human genome ENERGY GENERATION AND STORAGE SYSTEMS The Lorenz curve method for remote sensing assessment of forest structure and competitive dominance