cs.DC · 2026-06-25 · No. 34
Distributed, Parallel, and Cluster Computing, 2026-06-25.
9 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
9 entries-
01
Interference-Aware Cross-Application Placement: A Multi-Objective Optimization Approach for Microservice Clusters
Iqra Zafar, Christian Medeiros Adriano, Holger Giese
cs.DC
In modern cloud architectures, multiple applications often run within the same clustered environment, sharing underlying resources. This resource sharing can cause interference among applications, leading to degraded latency and reduced system stability. As containerized microservices become increasingly central to cloud-native applications, their performance can suffer from complex interference scenarios related to resource competition....
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02
AI-Assisted Computational Reproducibility on the FABRIC Testbed
Komal Thareja, Paul Ruth, Berent Aldikacti, Michael Zink
cs.DC · cs.AI
Computational reproducibility remains difficult despite being central to scientific research. In this paper, we show how the international FABRIC testbed, combined with large language model (LLM) coding assistants through LoomAI, can simplify reproducing published experiments across multiple domains. We reproduced three case studies on FABRIC, covering BBR-family congestion-control evaluations, LAMMPS molecular dynamics scaling benchmarks on...
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03
NEURON-Fabric: Architecture-Runtime Co-Design for Controlled Low-Bit Gradient Communication
Ziqiang Wang, Changcheng Huang, Chung-Horng Lung
cs.DC
Large-scale neural-network training repeatedly aggregates gradients across devices, making communication a central cost in distributed learning. Low-bit gradient aggregation can reduce this cost, but applying it as a static replacement for full-precision communication can destabilize training because safe precision depends on training phase, model structure, runtime bucketization, and the communication substrate. This paper presents...
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04
Endeavor: Efficient PairHMM for Detection of DNA Variants in Genome-Scale Datasets
Miguel Graça, Aleksandar Ilic
cs.DC
DNA variant calling represents a key operation in bioinformatics pipelines that aims at identifying genetic variants. Given an evidenced explosion in genomic data availability, there is an urgent need for a high-performant, portable and efficient solution for variant calling, which can further improve our understanding of genomic structure and genetic basis for complex diseases. In its most common formulation, the Pair Hidden Markov Model...
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05
Dynamic Load Balancing for Uncertainty Quantification with Applications in Bayesian Inversion
C. M. Loi, M. Wille, A. Reinarz
cs.DC
Uncertainty Quantification (UQ) workflows present a particular scheduling challenge in high performance computing environments, as they typically generate large numbers of heterogeneous model evaluations with loose but non-trivial dependencies between tasks. A static one-size-fits-all approach in traditional schedulers is inadequate to handle heterogeneous tasks optimally. We introduce an improved load balancer in the UQ and Modelling Bridge...
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06
TwoStepDemocracy: Prototyping of self-evolving, democratic, and decentralized systems
Stan Verlaan, Johan Pouwelse
cs.DC
Decentralised systems are often built to avoid central control, but their evolution almost always depends on centralised platforms, informal maintainer authority, and a surprising amount of unpaid goodwill. To address this uncomfortable mismatch, we introduce TwoStepDemocracy, a technical proof-of-concept for protocol-native software evolution. The prototype combines costly cryptographic identities, peer-to-peer dissemination, issue and...
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07
Latency-Aware Service Placement using Neural Combinatorial Optimisers for Edge--Cloud Systems
Kimia Abedpour, Mohammadsadeq Garshasbi Herabad, Zheng Li, Javid Taheri
cs.DC
The growth of Internet of Things (IoT) applications and latency-sensitive services has increased the demand for efficient service placement across compute continuum platforms, such as edge--cloud systems. Modern applications are decomposed into interdependent microservices deployed over heterogeneous infrastructures, making placement under resource and network constraints an intractable NP-hard combinatorial optimisation problem. This study...
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08
EmuGEMM: Fused Tensor Core Kernels for Precision Emulation in Matrix Multiplication
Denghui Lu, Alexander Maeder, Mathieu Luisier, Alexandros Nikolaos Ziogas
cs.DC · cs.MS · cs.PF
Modern GPUs devote an increasing silicon budget to low-precision matrix-multiplication units, widening the precision-throughput gap for scientific computing workloads. Ozaki Schemes I and II offer an alternative by reconstructing high-precision general matrix multiplication (GEMM) from low-precision operations, yet existing implementations leave substantial performance untapped. In particular, intermediate results are repeatedly materialized...
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09
Programmable Probabilistic Computer with 1,000,000 p-bits
Navid Anjum Aadit, Xiuqi Zhang, Shuvro Chowdhury, Kevin Callahan-Coray, Kyle Lee, Saleh Bunaiyan, Sanjay Seshan,...
cs.DC · cs.AR
Probabilistic computers built from p-bits have been proposed as hardware accelerators for sampling and optimizing Ising models, but existing systems have been confined to a single chip, capped by its capacity and memory bandwidth. Here we break this limit by networking FPGAs into a single Ising machine far larger than any one device could hold, realizing a programmable probabilistic computer with one million p-bits. The machine performs Gibbs...
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