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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
ViPIOS - VIenna Parallel Input Output System: Language, C...
Erich Schikuta, Helmut Wanek, Heinz Stockinger, Kurt Stockinger, · 2018-08-03 · via cs.DC updates on arXiv.org

For an increasing number of data intensive scientific applications, parallel I/O concepts are a major performance issue. Tackling this issue, we develop an input/output system designed for highly efficient, scalable and conveniently usable parallel I/O on distributed memory systems. The main focus of this research is the parallel I/O runtime system support provided for software-generated programs produced by parallelizing compilers in the context of High Performance FORTRAN efforts. Specifically, our design aims for the Vienna Fortran Compilation System. In our research project we investigate the I/O problem from a runtime system support perspective. We focus on the design of an advanced parallel I/O support, called ViPIOS (VIenna Parallel I/O System), to be targeted by language compilers supporting the same programming model like High Performance Fortran (HPF). The ViPIOS design is partly influenced by the concepts of parallel database technology. At the beginning of the project we developed a formal model, which forms a theoretical framework on which the ViPIOS design is based. This model describes the mapping of the problem specific data space starting from the application program data structures down to the physical layout on disk across several intermediate representation levels. Based on this formal model we designed and developed an I/O runtime system, ViPIOS, which provides support for several issues, as - parallel access to files for read/write operations, - optimization of data-layout on disks, - redistribution of data stored on disks, - communication of out-of-core (OOC) data, and - many optimizations including data prefetching from disks based on the access pattern knowledge extracted from the program by the compiler or provided by a user specification.