Zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs) are a key technology to privacy-preserving applications today. The complexity of proof generation, however, heavily constrains throughput in latency-sensitive environments. The computational burden primarily stems from two fundamental algorithms: Multi-Scalar Multiplication (MSM) and the Number Theoretic Transform (NTT). We propose a series of optimizations for these two kernels, including computation-transfer pipelining, load balancing, and memory access fusion, achieving 1.97 × to 2.16 × proof generation speedup over a state-of-the-art open source GPU acceleration library. Our design also supports out-of-core computation, enabling the generation of large-scale ZKP proofs.
Xinwei Qiang, Liukun Yu, Xiyu Wang et al.· IEEE International Symposium...· 0 citations
With the rapid growth of artificial intelligence (AI) and high-performance computing (HPC), GPUs and other accelerators have become a shared computing substrate for a wide range of workloads. However, many shared accelerator clusters still rely on coarse device-level allocation, which often leads to low effective utilization, resource fragmentation, and long queueing delays. Although pooling technologies offer a promising direction, existing approaches remain limited in supporting fine-grained, low overhead sharing across heterogeneous accelerators and diverse co-located workloads. This paper presents gPooling, a hardware-agnostic accelerator pooling framework based on driver-level interception. gPooling creates elastic virtual devices on demand and extends fine-grained sharing across heterogeneous accelerators through a unified control path. We evaluate gPooling using benchmarks derived from real cluster traces and through deployment in a production GPU cluster. Results show that gPooling improves accelerator utilization, reduces user waiting time, and increases the overall efficiency of shared accelerator environments.
Kaicheng Guo, Jingyi Chen, Chen Chen et al.· IEEE Transactions on Paralle...· 0 citations
