Jul 2026· Fall Joint Computer Conference· pp. 273-280· 0 citations· 20 references
Abstract
Zero-knowledge proofs (ZKPs) are increasingly used in blockchain systems, privacy-preserving applications, and verifiable cloud computation, but their practical deployment is still constrained by the cost of proof generation. This cost is especially important when proof generation is provided as a cloud-side service, where prover latency directly affects service throughput and resource efficiency. Existing heterogeneous provers mainly follow an operator-centric design, offloading selected GPU-friendly kernels while leaving the overall proving pipeline fragmented across fine-grained execution boundaries. This approach is effective at reducing the cost of major operators, but it also changes the bottleneck structure of modern proving: once dominant kernels are accelerated, end-to-end inefficiency increasingly arises from fragmented cross-stage execution, including host-device transitions, intermediate-state materialization, and CPU-side coordination overhead. In this paper, we argue that the key systems problem is no longer simply which operator to accelerate next, but what the right runtime execution unit should be for heterogeneous ZKP proving. To this end, we propose region-centric execution, a locality-aware runtime abstraction that groups tightly dependent computations into larger execution units based on dependency locality, state continuity, and boundary cost. Building on this abstraction, we design execution mechanisms that preserve deviceresident state, delay unnecessary materialization, coordinate dependent computations within a region, and reduce excessive boundary crossings. We implement our design on top of a Plonky2-based GPU prover and evaluate it on four representative workloads. Region-centric execution improves end-to-end proving performance by $1.36 \times$ on average and up to $1.52 \times$ over an operator-centric baseline, with the gains coming primarily from lower coordination overhead and reduced host-device transfer cost rather than uniformly faster GPU kernels.
Protocol-level validity mechanisms, such as validity rollups and STARK-based proving systems, improve blockchain scalability, but do not remove the cost of executing computation-intensive application logic inside smart contracts. At the application layer, a meaningful class of structured arithmetic workloads—including polynomial evaluations, cryptographic primitive computations, and signal-processing operations over finite fields—arises naturally in contract logic, yet remains subject to the on-chain fee model regardless of protocol-level optimizations. This paper investigates whether outsourcing such workloads and replacing direct execution with transparent on-chain verification can provide a practical cost advantage under realistic smart-contract fee models. We present a cross-platform implementation of GKR-based transparent on-chain verification for outsourced structured arithmetic circuits on the Ethereum Virtual Machine (Solidity) and Starknet (Cairo). GKR requires no trusted setup, which makes it particularly suitable for deployment in permissionless public blockchain environments where trust assumptions must be minimized. The design targets layered circuits with regular structure and incorporates implementation techniques that reduce verifier-side overhead and eliminate the linear input-handling bottleneck in naive deployments. These workloads can be represented as layered arithmetic circuits with regular wiring and logarithmic depth. Our evaluation shows that direct execution cost increases rapidly with input size, whereas GKR-based verification scales much more favorably. At size 4096, verification reduces execution cost by approximately 84% on the EVM and 91% on Starknet. These results identify outsourced execution with on-chain GKR verification as a practical design point for application-layer verifiable computation, especially in public blockchain environments where transparency and the absence of trusted setup are important.
We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeline on their private inputs and learn nothing beyond the result (e.g., aggregate statistics). Unlike existing MPC solutions that support a single threat model or workload type, CryptDough provides built-in support for cross-domain analytics (relational, time series, ML inference) under various threat models, all within the same system runtime. CryptDough contributes (i) a hierarchical system design that facilitates modularity and extensibility through progressive lowering of abstractions, and (ii) the concept of virtual vectors that enable users to write single-threaded code across all layers of the software stack, while pushing the complexity of communication, parallelization, and memory management down to the execution engine. We show that CryptDough generalizes the functionality of state-of-the-art MPC systems and remains competitive on the analytics they support, often outperforming them by more than $2\times$.
Muhammad Faisal, Alessandra M Lanz, Sam Buxbaum et al.· 0 citations
Ethereum execution clients implement identical Ethereum Virtual Machine (EVM) semantics, yet can exhibit substantially different performance in practice. We study this divergence through a three-step systems analysis of two representative clients, Geth and Reth. First, across historical synchronization, live synchronization, and transaction replay, we show that Reth is markedly faster whenever repeated local execution dominates, reducing historical local processing by 3.69× and replay time by 6.87×, while Geth is 2.70× faster end-to-end in live synchronization because verification and canonical commitment lie on the critical path. Second, by replaying 100,000 mainnet blocks and attributing runtime to individual opcodes, we show that this performance gap is overwhelmingly concentrated in read-side state access: SLOAD alone explains 95.8% of the measured inter-client difference, with further asymmetry concentrated in account- and code-lookup opcodes. Third, in a public-view builder case study, we use this diagnosis to reduce how much candidate evaluation reaches the expensive exact-simulation stage: on a frozen test split of 886 blocks, an XGBoost prefilter at top-0.1% ratio to top-2.0% ratio reduces checked candidates by 254,708 to 604,776 relative to an effective-priority-fee baseline at matched Flashbots-hit levels, while speeding the ranking-and-selection stage by about 1.75×. These results show that execution-client divergence is best understood as a critical-path problem: the same semantic interface can expose very different bottlenecks depending on how state access, verification, and workload structure interact.
Chon Kit Lao, Nora Sinong Lu, Jingyi Ning et al.· International Conference on...· 0 citations
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
Containerized workloads are commonly built via CI/CD pipelines, stored in registries, and executed across heterogeneous infrastructures, including cloud and edge environments. A single compromised build step or credential can turn routine automation into large-scale distribution of malicious artifacts, motivating integrity, transparency, and enforceable deployment-time checks. In this paper, we present an architecture for verifiable container image distribution that addresses key-management challenges and enables policy-enforced admission-time verification. A transparency service generates one-time signing keys bound to authenticated identities, records signing events in an append-only transparency registry, and returns cryptographically verifiable proofs of inclusion. These proofs and identity attributes are attached to image metadata and evaluated by policy-as-code at admission time, so only compliant artifacts are deployed. We implement a proof-of-concept integrated with GitHub Actions and GitLab Runners and evaluate how the resulting pipeline mitigates common supply-chain attacks under a realistic threat model.
N. Fotiou, Lefteris Georgiadis, Ignacio Lacalle et al.· 0 citations
This work presents a distributed inference framework that integrates speculative decoding across edge and cloud, and shifts the bulk of computation to the edge, significantly lowers inference time and cloud cost, and preserves the accuracy of the big model without any retraining requirement.
D. J. Bajpai, K. Upadhyay, M. Hanawal· 0 citations