A focused digest of distributed computing, cloud-native infrastructure, and engineering research from authoritative sources.
arXiv:2609.01821v1 Announce Type: new Abstract: With the rise of inference as today's dominant AI workload, the industry is transitioning to high-bandwidth photonic interconnects to meet the large scale-up requirements of increasingly complex Mixture-of-Experts (MoE) models. This paper quantifies the benefits of 3D-in…
Read at sourcearXiv:2609.01864v1 Announce Type: new Abstract: NVIDIA distributed shared memory (DSMEM) enables direct shared-memory access within a thread block cluster. However, cluster synchronization, remote access, and resource costs make it difficult to determine when DSMEM improves performance. To fill this gap, we propose CR…
Read at sourcearXiv:2609.01975v1 Announce Type: new Abstract: Recent GPU generations include special-purpose ray tracing (RT) cores for graphics applications. While RT cores are primarily used for rendering, recent works show they can be leveraged for general-purpose tasks, including similarity searches. However, existing approache…
Read at sourcearXiv:2609.02109v1 Announce Type: new Abstract: Deploying heterogeneous AI models concurrently on a shared GPU introduces resource contention that complicates runtime scheduling. While surrogate models avoid costly online benchmarking, their profiling requirements typically grow combinatorially with the number of co-r…
Read at sourcearXiv:2609.02227v1 Announce Type: new Abstract: Genome-wide association studies (GWAS) gain statistical power from large, ancestrally diverse cohorts, but privacy regulations and data-residency constraints often prevent genomic data from being centrally pooled across institutional or national borders. We present a pri…
Read at sourcearXiv:2609.02238v1 Announce Type: new Abstract: The American Science Cloud (AmSC), established under the Genesis Mission of the U.S. Department of Energy (DOE), aims to integrate DOE high-performance computing systems, experimental facilities, and data resources into a single, coordinated, AI-driven discovery platform…
Read at sourcearXiv:2609.02514v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) on edge devices typically relies on model compression or split inference. However, compression degrades reasoning capabilities, while split inference suffers from severe Wide Area Network (WAN) communication bottlenecks. Edge-cloud…
Read at sourcearXiv:2609.00275v1 Announce Type: cross Abstract: Fleets of LLM agents now externalize effects that cannot be fully undone: they move money, deploy code, delete data, and disclose information. Current controls check one effect at a time, so a fleet of individually authorized agents can overdraw its principal's risk un…
Read at sourcearXiv:2609.01939v1 Announce Type: cross Abstract: Autonomous agent runtimes execute tool actions that mutate databases, repositories, and cloud services across organizational boundaries. Authorization and local compensation cover pre-action admission and in-runtime rollback, but neither settles the residual harm left…
Read at sourcearXiv:2609.02450v1 Announce Type: cross Abstract: Semantic triggers in federated learning (FL) can be less conspicuous than synthetic patches, but sample-dependent placement may weaken backdoor implantation across aggregation rounds. This challenge is compounded in decentralized FL (DFL), where topology-dependent peer…
Read at sourcearXiv:2609.02736v1 Announce Type: cross Abstract: Semiconductor quantum-dot arrays are a compelling platform for scalable quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devices. Existing automation tools rely on simplified physical models---such as constant-capa…
Read at sourcearXiv:2609.02866v1 Announce Type: cross Abstract: Revocation completion, clean state, or operation success can leave authorized work able to cause an effect the application rejects while the provider stays within its contract. We call the absence of all such paths policy-relative effect closure, or effect closure for…
Read at sourcearXiv:2609.02876v1 Announce Type: cross Abstract: Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters. Ensembles of independent trajectories suit graphics processing units (GPUs), but current GPU softwar…
Read at sourcearXiv:2509.23722v3 Announce Type: replace Abstract: Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbates pipeline bubbles, thereby reducing training efficiency. Prior approaches typically optimize a single phase of the pipeline…
Read at sourcearXiv:2510.21549v3 Announce Type: replace Abstract: The distributed coloring problem is arguably one of the key problems studied in the area of distributed graph algorithms. The most standard variant of the problem asks for a proper vertex coloring of a graph with $\Delta + 1$ colors, where $\Delta$ is the maximum deg…
Read at sourcearXiv:2603.08003v3 Announce Type: replace Abstract: Data replication is critical to data center design, ensuring high availability, scalability, and fault tolerance. However, replicas must be coordinated to maintain convergence and database integrity constraints under transactional workloads. Commutative Replicated Da…
Read at sourcearXiv:2604.08812v2 Announce Type: replace Abstract: Real-time tsunami early warning relies on distributed sensor networks to infer seismic sources and seafloor motion. Optimizing these networks via Bayesian optimal experimental design (OED) is exceptionally challenging for systems governed by hyperbolic partial differ…
Read at sourcearXiv:2606.21430v3 Announce Type: replace Abstract: Many algorithms in statistics and machine learning can be parallelized in an asynchronous manner where workers need to communicate through shared state rather than execute independent tasks dispatched by a central controller. Especially in modern hyperparameter optim…
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