arXiv subjects

Yizou Chen

Publications and source records attributed to Yizou Chen.

3 recordsLinked to original sources

arXiv2026

DMG: A Scalable and Efficient Memory-Disaggregated Graph Processing System

Traditional graph processing systems are built on monolithic servers, which couple a fixed ratio of compute and memory resources but often result in resource under-utilization in data centers. Although the disaggregated memory (DM) architecture has emerged to address this inefficiency, we identify that existing graph processing systems on DM remain highly impractical. They rely on unscalable architectures that fail to scale beyond a single memory node and a single compute node, and they require compute-side caches that are orders of magnitude larger than conventional practice in DM. To this end, this paper presents DMG, the first practical graph processing system on DM, which demonstrates superior system scalability and cache efficiency while delivering high performance. To improve efficiency of graph retrieval on DM, DMG proposes a DM-friendly graph store with retrieval optimizations. To mitigate costly update propagation, DMG presents an adaptive update coordinator that coordinates compute and memory nodes to perform update propagation with low overhead. To enable fast and effective load balancing, DMG employs a two-stage workload manager that includes a coarse-grained initial partitioning and a fine-grained runtime re-scheduling. Experimental results substantiate that compared with the state-of-the-art DM-based graph processing system, DMG can elastically scale up both compute and memory resources, delivering up to 4.9X better performance and accommodating graphs with ever-increasing sizes; meanwhile, it effectively tames the compute-side cache demands by up to 18.9X, positioning itself as a DM-ready solution in practice.

cs.DB↗

arXiv2026

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state per decoding step and extending the KV storage to CPU memory. In practice, however, these algorithmic savings rarely translate into end-to-end system-level gains because sparse methods typically operate at different granularities and thus rely on ad hoc, per-algorithm implementations. At the same time, hierarchical KV storage introduces a new systems bottleneck: retrieving fine-grained, irregular KV subsets across the GPU-CPU boundary can easily erase the benefits of sparsity. We present SPIN, a sparse-attention-aware inference framework that co-designs the execution pipeline with hierarchical KV storage through three techniques: (1) a unified partition abstraction that maps different sparsity granularities onto a shared page-based KV substrate; (2) a locality-aware KV cache manager that dynamically sizes per-request HBM budgets and uses a GPU-friendly bucketed LRU policy to cut PCIe round-trips; and (3) a two-level hierarchical metadata layout sized to the active working set rather than the worst-case address space. Built on vLLM with three representative sparse attention algorithms, SPIN delivers 1.66-5.66x higher end-to-end throughput and 7-9x lower TTFT than vLLM, and reduces TPOT by up to 58% over the original sparse-attention implementations.

cs.LG↗

arXiv2025

Lotus: Optimizing Disaggregated Transactions with Disaggregated Locks

Disaggregated memory (DM) separates compute and memory resources, allowing flexible scaling to achieve high resource utilization. To ensure atomic and consistent data access on DM, distributed transaction systems have been adapted, where compute nodes (CNs) rely on one-sided RDMA operations to access remote data in memory nodes (MNs). However, we observe that in existing transaction systems, the RDMA network interface cards at MNs become a primary performance bottleneck. This bottleneck arises from the high volume of one-sided atomic operations used for locks, which hinders the system's ability to scale efficiently. To address this issue, this paper presents Lotus, a scalable distributed transaction system with lock disaggregation on DM. The key innovation of Lotus is to disaggregate locks from data and execute all locks on CNs, thus eliminating the bottleneck at MN RNICs. To achieve efficient lock management on CNs, Lotus employs an application-aware lock management mechanism that leverages the locality of the OLTP workloads to shard locks while maintaining load balance. To ensure consistent transaction processing with lock disaggregation, Lotus introduces a lock-first transaction protocol, which separates the locking phase as the first step in each read-write transaction execution. This protocol allows the system to determine the success of lock acquisitions early and proactively abort conflicting transactions, improving overall efficiency. To tolerate lock loss during CN failures, Lotus employs a lock-rebuild-free recovery mechanism that treats locks as ephemeral and avoids their reconstruction, ensuring lightweight recovery for CN failures. Experimental results demonstrate that Lotus improves transaction throughput by up to 2.1$\times$ and reduces latency by up to 49.4% compared to state-of-the-art transaction systems on DM.

cs.DC↗