As blockchain technology grows more prevalent, its performance limitations have become a critical barrier to large-scale adoption. The rise of optimized heterogeneous blockchain systems has significantly increased the demand for fair performance testing frameworks. However, existing work, whether simulator-based or system-based, often relies on default settings, overlooking the impact of detailed configuration parameters, which can affect the fairness of performance evaluations. To fill this gap, we propose BlockAlign, a configuration alignment tool based on a rule tree, designed to enhance the fairness of performance testing across heterogeneous blockchain systems. Firstly, based on architectural analysis, we design a rule tree to filter, classify, and semantically align configurations. Secondly, we introduce a metric called fluctuation rate to measure performance differences before and after configuration alignment. Finally, we conduct experiments on Geth, Besu, and Conflux, demonstrating that BlockAlign significantly improves the fairness and credibility of performance comparisons.

The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state prediction in scenarios where the underlying dynamics are unknown. However, the performance, reliability, and uncertainty of data-driven predictors become compromised when encountering out-of-distribution observations relative to the training data. In this paper, we introduce a Plug-and-Play Physics-Informed Machine Learning (PnP-PIML) framework to address this challenge. Our method employs conformal prediction to identify outlier dynamics and, in that case, switches from a nominal predictor to a physics-consistent model, namely distributed Port-Hamiltonian systems (dPHS). We leverage Gaussian processes to model the energy function of the dPHS, enabling not only the learning of system dynamics but also the quantification of predictive uncertainty through its Bayesian nature. In this way, the proposed framework produces reliable physics-informed predictions even for the out-of-distribution scenarios.

This paper introduces HEMVM, an innovative heterogeneous blockchain framework that seamlessly integrates diverse virtual machines (VMs), including the Ethereum Virtual Machine (EVM) and the Move Virtual Machine (MoveVM), into a unified system. This integration facilitates interoperability while retaining compatibility with existing Ethereum and Move toolchains by preserving high-level language constructs. HEMVM's unique cross-VM operations allow users to interact with contracts across various VMs using any wallet software, effectively resolving the fragmentation in user experience caused by differing VM designs. Our experimental results demonstrate that HEMVM is both fast and efficient, incurring minimal overhead (less than 4.4 %) for intra-VM transactions and achieving up to 9300 TPS for cross-VM transactions. Our results also show that the cross-VM operations in HEMVM are sufficiently expressive to support complex decentralized finance interactions across multiple VMs. Finally, the parallelized prototype of HEMVM shows performance improvements up to 44.8 % compared to the sequential version of HEMVM under workloads with mixed transaction types.

Cryptocurrencies such as Bitcoin and Ethereum are gaining popularity thanks to their prominent advantages compared to legacy financial transaction systems. However, they require all participants to reach a consensus on the order of transactions, which fundamentally limits their performance in terms of confirmation latency and throughput, thus hindering their further deployment. Off-chain payment network is the state-of-the-art approach of solving this performance issue. Unfortunately, all existing payment networks are based on payment channels, which bring extra overhead, cost and vulnerabilities. In this paper, by leveraging trusted monotonic counters, we propose monoCash, the first off-chain payment network that is channel-free, thereby it is one-hop, routing-free, concurrency-friendly, rebalancing-free and wormhole-resilient. We implement and deploy monoCash on a wide area network of 3,000 nodes. The benchmark shows that it provides a throughput up to 30,000 transactions per second (higher than credit card systems, e.g., VISA).

We present the Layered Merkle Patricia Trie (LMPT), a performant storage data structure for processing transactions in high-throughput systems when compared to traditional Merkle Patricia Tries used in Ethereum clients. LMPTs keep smaller intermediary tries in memory to alleviate read and write amplification from high-latency disk storage. As an additional feat, they also allow for the I/O and transaction verifier threads to be scheduled in parallel and independently. LMPTs can ultimately reduce significant I/O traffic that happens on the critical path of transaction processing. Empirical results show that LMPTs can process up to$\times6$more transactions per second on real-life ERC20 smart contract workloads when compared to existing Ethereum clients.

Blockchain systems must be secure and offer high performance. These systems rely on transaction broadcast mechanisms to provide both of these features. Unfortunately, in today’s systems, the broadcast mechanisms are highly inefficient.We present Mercury, a new transaction broadcast protocol designed for high performance blockchains. Mercury shortens the transaction propagation delay using two techniques: a virtual coordinate system and an early outburst strategy. Simulation results show that Mercury outperforms prior propagation schemes and decreases overall propagation latency by up to 44%. When implemented in Conflux, an open-source high-throughput blockchain system, Mercury reduces transaction propagation latency by over 50% with less than 5% bandwidth overhead.

We present the Layered Merkle Patricia Trie (LMPT), a performant storage data structure for processing transactions in high-throughput systems when com-pared to traditional Merkle Patricia Tries used in Ethereum clients. LMPTs keep smaller intermediary tries in memory to alleviate read and write amplification from high-latency disk storage. As an additional feat, they also allow for the I/O and transaction verifier threads to be scheduled in parallel and independently. LMPTs can ultimately reduce significant I/O traffic that happens on the critical path of transaction processing. Empirical results presented here confirm that LMPTs can process up to × 6 more transactions per second on real-life workloads when compared to existing Ethereum clients.

Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this article, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract executionwithout hard-forks. Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions.

The success of Bitcoin and Ethereum has attracted many efforts to build high-throughput blockchain systems. This paper focuses on transaction dissemination --- a rather overlooked issue in these systems. We argue that efficient transaction dissemination is the key for a blockchain system to sustain at high-throughput --- usually thousands of transactions per second --- and the existing solutions fell short at doing so.

Existing Byzantine fault tolerance (BFT) protocols face significant challenges in safety, scalability, throughput, and latency. We present a new BFT protocol, Gosig, for the consortium blockchains. Gosig guarantees safety even in asynchronous networks fully controlled by adversaries, by combining secret leader selection with multi-round voting. We co-design both the consensus protocol and the underlying gossip network to optimize performance. In particular, we adopt transmission pipelining to fully utilize the network bandwidth while use aggregated signature gossip to reduce the number of messages. These optimizations help Gosig to achieve unprecedented single-chain performance. On a public cloud testbed spanning multiple data centers consisting of 280 nodes across 14 cities on five continents, Gosig achieves over 15,000 transactions per second with 15.8-second confirmation time. When the system scales to 5,000 nodes, Gosig can still achieve 3,000 transactions per second with about 23.9-second confirmation time.