GPU-optimized PlonK analyzes performance bottlenecks in ZK proof generation
Original title: Performance Bottlenecks in Computing ZK Proofs on a GPU
The paper explores parallelization trade-offs on multi-GPU environments using RDMA to maximize performance for large circuit sizes in zero-knowledge proofs.
This paper explores optimizing zero-knowledge proof generation, specifically PlonK, using GPUs for applications not amenable to incrementally verifiable computing. It introduces fundamental changes in low-level architectural structure affecting performance curves. Results lead to considering multi-GPU environments to maximize performance for large circuit sizes, discussing issues of communication, hardware management, data partition, and data transfers using RDMA.
- explores trade-offs between increased parallelization on GPUs and memory management problems
- implements parallelized PlonK to analyze data flows
- motivates multi-GPU implementations for large circuit sizes
- discusses cost models and use of RDMA for data transfer
From the original article
1 min read at arxiv.orgZero-knowledge proofs allow one party to prove the correctness of a statement or execution of a computation while revealing no more than the statement of correctness itself. Recent use cases, such as AI datasets, suggest the need for faster proof generation over increasingly large constraint sets.
By Victor Carolino · arxiv.org
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