Accelerated Fourier SAT (AFSAT): Fully Realising a GPU-based Symmetric Pseudo-Boolean SAT Solver
arXiv:2606.06641v1 Announce Type: new Abstract: We present Accelerated Fourier SAT (AFSAT), a GPU-accelerated solver for pseudo-Boolean satisfiability based on continuous local search (CLS). AFSAT realises the proof-of-concept approach, FastFourierSAT, into a fully-engineered solver supporting any heterogeneous mixture of symmetric constraint types and lengths within a single problem instance. Using the JAX compiler, AFSAT leverages pure function composition, automatic vectorisation, automatic differentiation, and just-in-time (JIT) compilation to perform massively parallel CLS across batches
The continuous advancements in GPU technology and compiler optimizations, exemplified by JAX's capabilities, are enabling new computational paradigms for complex problems like SAT solving.
This development significantly enhances the efficiency and scalability of solving highly complex constraint satisfaction problems, which underpin many AI, optimization, and verification tasks.
The ability to run highly parallel continuous local search on GPUs makes pseudo-Boolean SAT solving dramatically faster and more versatile, supporting a wider range of symmetric constraint types within a single problem instance.
- · AI researchers and deep learning practitioners
- · GPU manufacturers
- · Computational optimization sector
- · Software verification and logic synthesis
- · Traditional CPU-bound SAT solver developers
- · Sectors reliant on slower computational methods
Increased speed and efficiency in solving complex computational problems across various scientific and industrial applications.
Acceleration of research and development in fields heavily dependent on SAT solving, such as formal verification, AI planning, and drug discovery.
Potentially enables new classes of AI systems or complex simulations that were previously computationally intractable due to the bottleneck of constraint satisfaction.
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Read at arXiv cs.AI