A focused digest of distributed computing, cloud-native infrastructure, and engineering research from authoritative sources.
AI workloads are changing what platform teams need from infrastructure. Provisioning GPUs and standing up a cluster no longer makes a platform “AI-ready.” Once training spans more than one node, the bottlenecks show up in places...
Read at sourcearXiv:2609.10542v1 Announce Type: new Abstract: The Internet of Things is on a trajectory toward a trillion connected devices deployed across multiple domains. These devices are no longer simple sensing and actuation endpoints; they are mobile platforms with embedded processing and intelligent on-device services. The…
Read at sourcearXiv:2609.10545v1 Announce Type: new Abstract: Federated Learning (FL) enables distributed model training without centralizing data but suffers from high communication overhead. To address this, we propose Block-Wise Marginal Utility Contribution (MUC), a framework that selectively transmits only the most impactful d…
Read at sourcearXiv:2609.10547v1 Announce Type: new Abstract: Digital-token standards such as ERC-20, ERC-721, and ERC-1155 have been essential to blockchain adoption because they standardize callable interfaces. Interface standardization, however, does not fully specify token meaning. Two implementations may expose the same transf…
Read at sourcearXiv:2609.10552v1 Announce Type: new Abstract: We describe the design, implementation, and operation of a small federated GPU cluster built for Digital Humanities (DH) research within the Austrian DHinfra.at project. The system spans two university sites, brokers logins from a national identity federation, and expose…
Read at sourcearXiv:2609.10554v1 Announce Type: new Abstract: Heterogeneous memory systems that combine high-bandwidth memory (HBM) with commodity DRAM can accelerate bandwidth-bound HPC workloads, but current page placement largely depends on manual tuning or OS heuristics not designed for multi-tier dynamics. We present SHAMBLES…
Read at sourcearXiv:2609.10790v1 Announce Type: new Abstract: We present a Kubernetes Dynamic Resource Allocation (DRA) driver that makes composable CXL memory a schedulable cluster resource, and evaluate the resulting shared-memory tier for cross-node KV-cache reuse in LLM serving. The driver composes CXL regions on demand, materi…
Read at sourcearXiv:2609.10812v1 Announce Type: new Abstract: Cloud-native LLM serving frameworks have made deployment routine in data centers, yet deploying them on leadership-class supercomputers remains an engineering challenge requiring scheduler integration, MPI launch, accelerator selection, node-local weight staging, and pla…
Read at sourcearXiv:2609.10885v1 Announce Type: new Abstract: The small-world phenomenon was given an algorithmic foundation by Kleinberg, who showed that in an augmented $k$-dimensional lattice a decentralized greedy algorithm delivers a message in $O(\log^2 n)$ expected steps. We study predicted-greedy routing, in which a mobile…
Read at sourcearXiv:2609.11013v1 Announce Type: new Abstract: We study the problem of treasure hunt by a group of $k \geq 1$ agents in vertex-permuted dynamic rings (VP). In this model, the $n$ vertices remain on a ring but are permuted at each time step. We first show that treasure hunt is impossible for any $k \leq n-3$ agents, i…
Read at sourcearXiv:2609.11145v1 Announce Type: new Abstract: Distributed computing and distributed artificial intelligence require frequent exchanges of intermediate results, although many applications need only an aggregate rather than messages from individual devices. Conventional systems recover each message before computing th…
Read at sourcearXiv:2609.11264v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promising capabilities in generating remediation actions for microservice failures. However, directly executing AI-generated repair actions in production risks cascading collateral damage. We propose GuardedAct, a sandbox-first rem…
Read at sourcearXiv:2609.11352v1 Announce Type: new Abstract: The sparse matrix-vector multiplication (SpMV) algorithm is a fundamental computational kernel of linear algebra and serves as a building block for numerous applications, primarily iterative solvers for systems of linear equations used in scientific and engineering simul…
Read at sourcearXiv:2609.11356v1 Announce Type: new Abstract: Determinism and numerical reproducibility are increasingly required of GPU kernels in machine learning systems, yet deterministic implementations of the same kernel can still differ bit for bit. Floating-point reduction order is the primary cause, alongside partial-sum p…
Read at sourcearXiv:2609.11562v1 Announce Type: new Abstract: Modern high-performance GPU computations partition tensors into tiles to exploit data reuse and parallelism. Individual tile computations complete earlier than the full tensor computation, creating opportunities to overlap computation and communication. However, a mismat…
Read at sourcearXiv:2609.11744v1 Announce Type: new Abstract: Prefix caching can reduce the time to first token (TTFT) of long-context LLM requests by reusing previously computed key-value (KV) states, but for short prefixes or fast GPUs, recomputation can be faster than loading from an external cache. We characterize this tradeoff…
Read at sourcearXiv:2609.11923v1 Announce Type: new Abstract: Counterfactual regret minimization (CFR) is one of the few large numerical workloads that still runs faster on CPUs than on GPUs. Each iteration sweeps a game tree with up to billions of states in millions of small, interdependent gather and scatter steps issued through…
Read at sourcearXiv:2609.11058v1 Announce Type: cross Abstract: Recent advances in multimodal large language mod- els (MLLMs) have opened new opportunities for edge intelligence by enabling reasoning across heterogeneous sensor modalities, such as vision, text, and telemetry data. However, deploying these capabilities on resource-c…
Read at source