cs.DC · 2026-06-24 · No. 33
Distributed, Parallel, and Cluster Computing, 2026-06-24.
11 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
11 entries-
01
A Natively Blocked, Device-Resident Algebraic Multigrid GPU Path in PETSc
Mark F. Adams
cs.DC
Smoothed-aggregation algebraic multigrid (AMG) is widely used for the linear systems arising from finite-element discretizations of vector PDEs such as elasticity, but its GPU implementations have used scalar sparse matrix formats. These problems carry a natural block structure: matrix nonzeros occur in dense bs x bs blocks sharing one column index, so storing the blocks directly removes most of the index data and raises the arithmetic...
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02
Solvability of Approximate Agreement on Graphs and Simplicial Complexes
Joel Rybicki, Yaroslav Verbitsky
cs.DC
Approximate agreement tasks on graphs are discrete relaxations of consensus, where each process in a distributed system is given as input a vertex on a graph $G$, and processes have to output vertices that lie on a clique of $G$ contained in the convex hull of the input vertices. Although such tasks have been widely studied in a variety of models, graph classes and notions of convexity, it remains largely open for which classes of graphs...
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03
Unified Position-Invariant Random Access Through Two Compression Layers via Absolute-Offset Coordinates: A Bit-Perfect Device-Resident Proof
Yakiv Shavidze
cs.DC
Random access into compressed data is normally confined to a single layer. Entropy-layer methods (Recoil) seek within rANS by storing intermediate decoder states; dictionary/match-layer methods seek within LZ-style references. We are not aware of a format that supports a single position-invariant seek through both an entropy layer and a match layer addressed by one coordinate. We show that ACEAPEX's absolute-offset design provides exactly...
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04
CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation
Zhuoren Ye, Tianyu Wo, Dinghao Xue, Mingming Zhang, Yuchen Teng, Chunming Hu, Renyu Yang
cs.DC · cs.AI · cs.LG · cs.PF
Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold. This creates a GPU memory problem: model weights are stable and model-determined, while KV-cache is transient and demand-determined. Because cold models rarely reach peak KV-cache demand at the same time, reserving worst-case KV capacity per model wastes memory; a shared KV-cache pool can instead provision aggregate active...
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05
cuSBF: A Minimizer-Aware Bloom Filter for Genomic Sequence Data on Modern GPUs
Tim Dortmann, Markus Vieth, Bertil Schmidt
cs.DC · cs.DS · q-bio.GN
Efficient genomic k-mer indexing depends on approximate membership query (AMQ) structures that must deliver high throughput, low false-positive rates (FPR), and modest memory footprints. The Super Bloom filter (SBF) is attractive for this scenario because minimizer-guided sharding and the Findere scheme exploit the redundancy of overlapping k-mers. However, those same features cause high per-k-mer compute cost, severe register pressure, and...
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06
BiJuTy: An Interactive HPC-Aware Big Data Cluster Lifecycle Manager and Performance Assessment Utility for JupyterHub
Apurv Deepak Kulkarni, Jan Frenzel, Siavash Ghiasvand
cs.DC
The increasing demand for data processing has created a pressing need for access to high-performance computing (HPC) systems. Nevertheless, leveraging these systems to execute complex big data processing workflows remains a significant challenge, especially for beginners. This work presents BiJuTy, a solution designed to bridge the accessibility gap for big data workflows on HPC systems within the Jupyter ecosystem. By providing an...
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07
Semantic Lock: Synchronization Based on the Analysis of the Operation Conflict Graph
Denis Korotchenko, Vitaly Aksenov
cs.DC
This paper presents a new lock, SemanticLock, based on the conflict graph between operations. We can consider it a generalization of a read-write lock where conflicts exist between write operations and all other operations. We demonstrate the effectiveness of our lock in two applications. In the first, we design a toy data structure: an array supporting point queries and different range queries. In the second, potentially of greater interest,...
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08
Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment
Víctor Hidalgo-Izquierdo, Carmen Carrión, Blanca Caminero
cs.DC
This paper presents an extension of the Flower federated learning framework to support Semi-Asynchronous Federated Learning. The proposed approach adapts the traditional synchronous paradigm to better handle client heterogeneity and straggler effects. By introducing a semi-asynchronous training strategy, the system allows partial synchronization among clients while maintaining training efficiency and scalability. We implement and evaluate the...
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09
Aquifer: Hierarchical Memory Pooling with CXL and RDMA for MicroVM Snapshots
Junliang Hu, Huaicheng Li, Ming-Chang Yang
cs.DC · cs.OS
Memory stranding wastes 25-35% of installed DRAM in production cloud clusters. Memory pooling over CXL and RDMA offers a remedy, but neither technology alone suffices: CXL provides low-latency, load/store-transparent access limited to a pod, while RDMA provides cluster-wide reach at higher latency with software overhead. A hierarchical architecture combining both tiers is the practical path forward, yet remains unexplored for MicroVM-based...
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10
Hash Table Design for RDMA:Challenges and Opportunities
Shuchen She, Haipeng Dai
cs.DC
Hash tables complete the insertion, lookup, and deletion of a single key in constant time on average, and they are widely used in databases, key-value stores, and network systems. In the Internet of Things (IoT), the number of devices and the volume of sensed data keep growing, so the hash tables that store or index these data consume more and more memory. When a single server runs out of memory, the system can place part of the data in the...
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11
The Serialized Bridge: Understanding and Recovering LLM Serving Performance under Blackwell GPU Confidential Computing
Hang Yin, Kevin Wang
cs.DC · cs.CR · cs.PF
GPU Confidential Computing (GPU-CC) now preserves GPU-local performance: on NVIDIA B300, BF16 matmul runs at 0.998x of non-confidential performance. Yet LLM serving under Intel TDX plus GPU-CC still loses 13-27% of throughput, and KV-cache restore latency can more than double. This paper studies that gap on two Blackwell platforms, RTX Pro 6000 and B300 HGX, and identifies its dominant cause: the confidential VM-GPU bridge, not GPU compute....
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