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cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Concepts in Practice: C++ MPI Bindings for the HPC Ecosys...
Tim Niklas Uhl, Matthias Schimek, Daniel Brommer · 2026-06-08 · via cs.DC updates on arXiv.org

The official C++ MPI bindings were removed from the standard in 2008, leaving a gap that numerous third-party libraries have attempted to fill. However, existing wrappers typically cover only a limited subset of MPI or target specific use cases, falling short of a general-purpose solution. A recent conceptual paper proposed general design principles for modern C++ bindings based on C++20 concepts, without committing to a concrete interface. We present the first concrete realization of these principles in a layered architecture. At the foundation, we define a core layer: refined C++20 concepts formalizing the MPI standard's notion of data buffers, automatic mapping of standard C++ constructs, non-intrusive customization points for third-party types, and concept-based wrappers for MPI procedures. The result is a low-level native C++ MPI interface that works directly with STL containers, is highly extensible, and lends itself to standardization. Built on this core, we present KaMPIng-v2 -- a C++ MPI library offering the convenience and memory-safety of KaMPIng with composable, pipe-based syntax inspired by C++ ranges for efficient, boilerplate-free MPI programming. Finally, we demonstrate the core layer's broad applicability by designing lightweight adapters for GPU and performance-portability libraries, making the HPC ecosystem a first-class citizen in MPI. Kokkos views, Thrust device vectors, and SYCL buffers can be passed directly to MPI procedures, with adapter logic remaining self-contained. All contributions are backed by a fully functional open-source reference implementation, demonstrating the practical viability of the proposed design.