惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Hugging Face - Blog
Hugging Face - Blog
WordPress大学
WordPress大学
Microsoft Azure Blog
Microsoft Azure Blog
F
Fortinet All Blogs
B
Blog RSS Feed
Last Week in AI
Last Week in AI
The Cloudflare Blog
大猫的无限游戏
大猫的无限游戏
人人都是产品经理
人人都是产品经理
P
Proofpoint News Feed
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Microsoft Security Blog
Microsoft Security Blog
博客园 - 三生石上(FineUI控件)
Y
Y Combinator Blog
GbyAI
GbyAI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
小众软件
小众软件
雷峰网
雷峰网
C
Check Point Blog
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
博客园 - 司徒正美
U
Unit 42
量子位

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
SAKURAONE: Empowering Transparent and Open AI Platforms t...
Fumikazu Konishi · 2025-07-03 · via cs.DC updates on arXiv.org

SAKURAONE is a managed high performance computing (HPC) cluster developed and operated by the SAKURA Internet Research Center. It reinforces the ``KOKARYOKU PHY'' configuration of bare-metal GPU servers and is designed as a cluster computing resource optimized for advanced workloads, including large language model (LLM) training. In the ISC 2025 edition of the TOP500 list, SAKURAONE was ranked \textbf{49th} in the world based on its High Performance Linpack (HPL) score, demonstrating its global competitiveness. In particular, it is the \textbf{only system within the top 100} that employs a fully open networking stack based on \textbf{800~GbE (Gigabit Ethernet)} and the \textbf{SONiC (Software for Open Networking in the Cloud)} operating system, highlighting the viability of open and vendor-neutral technologies in large-scale HPC infrastructure. SAKURAONE achieved a sustained performance of 33.95~PFLOP/s on the HPL benchmark (Rmax), and 396.295~TFLOP/s on the High Performance Conjugate Gradient (HPCG) benchmark. For the HPL-MxP benchmark, which targets low-precision workloads representative of AI applications, SAKURAONE delivered an impressive 339.86~PFLOP/s using FP8 precision. The system comprises 100 compute nodes, each equipped with eight NVIDIA H100 GPUs. It is supported by an all-flash Lustre storage subsystem with a total physical capacity of 2~petabytes, providing high-throughput and low-latency data access. Internode communication is enabled by a full-bisection bandwidth interconnect based on a Rail-Optimized topology, where the Leaf and Spine layers are interconnected via 800~GbE links. This topology, in combination with RoCEv2 (RDMA over Converged Ethernet version 2), enables high-speed, lossless data transfers and mitigates communication bottlenecks in large-scale parallel workloads.