Formalising CXL Cache Coherence
October 21, 2024 ยท Declared Dead ยท ๐ International Conference on Architectural Support for Programming Languages and Operating Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Chengsong Tan, Alastair F. Donaldson, John Wickerson
arXiv ID
2410.15908
Category
cs.AR: Hardware Architecture
Cross-listed
cs.PL
Citations
8
Venue
International Conference on Architectural Support for Programming Languages and Operating Systems
Last Checked
3 months ago
Abstract
We report our experience formally modelling and verifying CXL.cache, the inter-device cache coherence protocol of the Compute Express Link standard. We have used the Isabelle proof assistant to create a formal model for CXL.cache based on the prose English specification. This led to us identifying and proposing fixes to several problems we identified as unclear, ambiguous or inaccurate, some of which could lead to incoherence if left unfixed. Nearly all our issues and proposed fixes have been confirmed and tentatively accepted by the CXL consortium for adoption, save for one which is still under discussion. To validate the faithfulness of our model we performed scenario verification of essential restrictions such as "Snoop-pushes-GO", and produced a fully mechanised proof of a coherence property of the model. The considerable size of this proof, comprising tens of thousands of lemmas, prompted us to develop new proof automation tools, which we have made available for other Isabelle users working with similarly cumbersome proofs.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Hardware Architecture
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Corona: System Implications of Emerging Nanophotonic Technology
R.I.P.
๐ป
Ghosted
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
R.I.P.
๐ป
Ghosted
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
R.I.P.
๐ป
Ghosted
Splitwise: Efficient generative LLM inference using phase splitting
R.I.P.
๐ป
Ghosted
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted