A focused digest of distributed computing, cloud-native infrastructure, and engineering research from authoritative sources.
AI infrastructure conversations often start with GPUs. Accelerators provide much of the compute behind model training and inference, so the focus is understandable. But a production AI workload rarely starts and ends on a GPU. Data...
Read at sourceKubernetes isn’t brand new anymore. Yet, for many teams, adopting it still feels intimidating. Even if you’ve watched Kubernetes become the default foundation for production software and AI workloads, it can still feel like a big...
Read at sourcearXiv:2609.02925v1 Announce Type: new Abstract: Multi-agent quorums are widely used to authorize high-stakes infrastructure and policy mutations, yet distinct reviewers often share upstream telemetry, documents, or tool backends. When upstream inputs fail, multiple votes collapse onto a single corrupted cause: replica…
Read at sourcearXiv:2609.03063v1 Announce Type: new Abstract: Bitcoin's proof-of-work (PoW)-based protocol is remarkable for how little it asks of its participants. Not only can miners take breaks from work whenever they please, but it is almost unique in offering a path of contrition: corrupt miners can reclaim honest status simpl…
Read at sourcearXiv:2609.03145v1 Announce Type: new Abstract: Emerging edge, autonomous, and cyber-physical systems increasingly require mathematical computation across heterogeneous devices connected by unreliable communication networks. Traditional high-performance computing and distributed data-processing frameworks provide powe…
Read at sourcearXiv:2609.03151v1 Announce Type: new Abstract: Long-context reasoning for large language models (LLMs) is becoming increasingly important, but training over long sequences remains challenging due to massive memory and communication requirements. Sequence parallelism has emerged as an essential technique for addressin…
Read at sourcearXiv:2609.03270v1 Announce Type: new Abstract: Proliferation of cloud-based latency-sensitive workloads requires infrastructures tuned to their workload-specific latency constraints. Today, they shape the cloud from a generalized computing platform to diverse workload-specific cloud environments. As the demand for la…
Read at sourcearXiv:2609.03297v1 Announce Type: new Abstract: Load shifting across geographic regions to chase intermittent renewable energy availability is commonly used in reducing cloud infrastructure carbon footprint. However, it often omits low-latency applications due to high latency variances of wide area networks (WAN) that…
Read at sourcearXiv:2609.03315v1 Announce Type: new Abstract: Existing concurrency control protocols either introduce nondeterminism, resulting in a serial execution-replay dependency between primary and replica nodes, or rely on impractical prior knowledge of transaction read-write sets. In this paper, we present Lantern, a determ…
Read at sourcearXiv:2609.03318v1 Announce Type: new Abstract: Collaborative inference pools distributed resources to run compute-intensive Vision Transformers (ViTs) in satellite edge computing. Model partitioning enables such collaboration by assigning consecutive layer groups to different nodes, but the large volume of intermedia…
Read at sourcearXiv:2609.03335v1 Announce Type: new Abstract: Concurrent multi-agent workflows expose future dependencies and serving-state requirements while running on heterogeneous GPU pools with time-varying load, model residency, and resource availability. The logical workflow defines the required computation, whereas its phys…
Read at sourcearXiv:2609.03459v1 Announce Type: new Abstract: Tensor-Train (TT) decomposition effectively compresses large embedding tables in recommendation models, but TT-based embedding lookup remains inefficient because partially shared computation flows across input indices are not fully reused and intermediate results are rep…
Read at sourcearXiv:2609.03562v1 Announce Type: new Abstract: This work introduces JuPyLive, a migration mechanism that enables seamless transition of Jupyter notebooks between local resources of user's workstation and remote resources of high-performance computing~(HPC) environments, while preserving the user experience. JuPyLive…
Read at sourcearXiv:2609.03905v1 Announce Type: new Abstract: Distributing an AI computation across the GPUs of a multi-GPU server is one of the central problems in systems-for-AI. We present Einsummable, a prototype system that accepts a PyTorch-like description of an AI computation and automatically distributes it across a multi…
Read at sourcearXiv:2609.03978v1 Announce Type: new Abstract: In DAG-based consensus, all validators propose blocks concurrently, and designated leader blocks drive transaction commit. Having multiple leader slots per round cuts queuing latency, yet production deployments run a single leader because of head-of-line blocking: a slow…
Read at sourcearXiv:2609.04168v1 Announce Type: new Abstract: As edge-based deep learning applications become more complex, optimizing performance on heterogeneous System-on-Chips (SoCs) presents unique challenges. Traditional pipelining techniques distributing the computation across different on-chip processing units, while effect…
Read at sourcearXiv:2609.03352v1 Announce Type: cross Abstract: Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led modern GPU-accelerated frameworks to omit it or restrict it to lightweight forms. We present…
Read at sourcearXiv:2609.03922v1 Announce Type: cross Abstract: Transform-domain methods simplify analysis and computation, making them central to scientific computing and signal processing. However, existing adaptive strategies often introduce new data structures or require substantial workflow redesign, limiting efficient executi…
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