Searcharxiv⌕ Search

arXiv · 2610.23014

QuarkShard: Decomposing Cross-Shard Requests into Single-Input Transactions

Abstract

Sharding blockchains enhance scalability by distributing the workload across multiple shards. Within sharding blockchains, securely and efficiently processing cross-shard user requests remains a pivotal challenge. Traditional sharding blockchain protocols execute cross-shard requests under a processing paradigm that treats each user request as a monolithic entity to preserve its atomicity, inherently introducing significant intra-shard BFT execution overhead and cross-shard communication overhead. We propose QuarkShard, a sharding protocol that breaks away from this traditional paradigm to achieve intra-shard and cross-shard overheads below the lower bounds imposed by conventional approaches. QuarkShard decomposes cross-shard requests into single-input-shard transactions, where each transaction requires consensus and processing within only a single shard, thereby substantially reducing total request processing overhead. Meanwhile, through a carefully designed request decomposition algorithm, QuarkShard guarantees fund atomicity or location atomicity during request execution. QuarkShard also enhances the security of blockchain sharding systems against MEV attacks and flooding attacks. Empirical evaluation across a wide-area network spanning four AWS regions demonstrates that QuarkShard achieves superior performance, attaining a peak throughput of $1024$k requests per second with sub-$4$-second latency. Benchmark comparisons show that QuarkShard achieves up to $210.7\%$ and $314.8\%$ of the maximum throughputs of Kronos and 2PC, respectively.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Changyu Duan, Zhixuan Fang. 2026-10-16. QuarkShard: Decomposing Cross-Shard Requests into Single-Input Transactions. https://arxiv.org/abs/2610.23014

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Load Balancing with Evolving Workloads in Large-Scale Language Model Serving

Serving large language models requires assigning requests whose workloads grow during processing and whose completion times are unknown. With a synchronization barrier, lightly loaded workers wait for the slowest worker at every decode iteration. We model this loss through aggregate workload imbalance and formulate a joint admission and assignment problem with sticky requests and capacity limits. Short lookahead incorporates predicted completions of ongoing requests. For exact optimization without lookahead, we prove a comparison under independent geometric decode lengths, bounded independent nondegenerate prompt sizes, and batched releases satisfying a cumulative supply condition. The long-run expected imbalance improvement over first come, first served admission with request-count join-shortest-queue grows at least as the square root of the product of worker capacity and the logarithm of worker count, in the stated scaling regime. The comparison extends to capacity-aware round robin, random routing, and power of two choices. BalanceRoute approximations connect the formulation to practical routing. Experiments using up to 144 NPUs with proprietary and public traces show lower workload spread and higher throughput than the evaluated baselines. These empirical results complement the guarantee for exact optimization.

cs.DC↗

Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs

The landscape of large language models (LLMs) has become increasingly fragmented, with no single model dominating the field. Meanwhile, cloud providers offer a diverse array of mid-tier and older-generation GPUs. These heterogeneous resources provide better availability and deliver comparable performance-per-dollar to top-tier hardware. To efficiently harness these GPUs for concurrent multi-LLM serving, we introduce Coral: an adaptive, heterogeneity-aware serving system. The key idea behind Coral is the joint optimization of resource allocation and replica serving strategies across all models. To keep pace with shifting throughput demands and resource availability, Coral employs a lossless, two-stage decomposition. This approach preserves joint optimality while reducing online solve times from hours to mere tens of seconds. Evaluations across 6 models and 20 GPU configurations demonstrate that Coral reduces serving costs by up to 2.79$\times$ compared to the strongest baseline, while delivering up to 2.39$\times$ higher goodput under scarce resource constraints.

cs.DC↗

Domain Extension of Lock-Freedom and Wait-Freedom for Group Computations

A domain extension of a definition refers to broadening the scope of a definition so that it applies to a larger set of cases than originally specified. The notion of lock-free and wait-free computation is designed for the domain of tasks that are completed by a single thread (in competition with other threads). The goal of this paper is to extend the definition of lock-freedom and wait-freedom to group-computations (denoted by gl-freedom and gw-freedom) that require that the task at hand must be completed by a collaboration between multiple threads. When extending a definition, certain constraints must be respected: the new domain must remain logically consistent with the original meaning, the extension should not introduce contradictions or ambiguities, and it must preserve the essential properties that make the definition valid and useful. We demonstrate this by showing that our extended definition is consistent with the original definition when the group consists of a single thread. We note that extension allows us to characterize programs in a new domain (distributed computing, NUMA computation systems, systems with private data for different threads, etc.) instead of relegating them to be in the same category (deadlock/livelock-free) without regard to the actual properties of that program. We also illustrate this definition with various examples.

cs.DC↗