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

爱范儿
爱范儿
H
Help Net Security
Jina AI
Jina AI
T
The Blog of Author Tim Ferriss
宝玉的分享
宝玉的分享
博客园 - 叶小钗
Y
Y Combinator Blog
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
WordPress大学
WordPress大学
C
Check Point Blog
Recent Announcements
Recent Announcements
IT之家
IT之家
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
美团技术团队
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
H
Hackread – Cybersecurity News, Data Breaches, AI and More
S
SegmentFault 最新的问题
MyScale Blog
MyScale Blog
Apple Machine Learning Research
Apple Machine Learning Research
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
B
Blog

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
A Low Cost Distributed Computing Approach to Pulsar Searc...
2004-07-08 · via cs.DC updates on arXiv.org

We describe a distributed processing cluster of inexpensive Linux machines developed jointly by the Astronomy and Computer Science departments at Haverford College which has been successfully used to search a large volume of data from a recent radio pulsar survey. Analysis of radio pulsar surveys requires significant computational resources to handle the demanding data storage and processing needs. One goal of this project was to explore issues encountered when processing a large amount of pulsar survey data with limited computational resources. This cluster, which was developed and activated in only a few weeks by supervised undergraduate summer research students, used existing decommissioned computers, the campus network, and a script-based, client-oriented, self-scheduled data distribution approach to process the data. This setup provided simplicity, efficiency, and "on-the-fly" scalability at low cost. The entire 570 GB data set from the pulsar survey was processed at Haverford over the course of a ten-week summer period using this cluster. We conclude that this cluster can serve as a useful computational model in cases where data processing must be carried out on a limited budget. We have also constructed a DVD archive of the raw survey data in order to investigate the feasibility of using DVD as an inexpensive and easily accessible raw data storage format for pulsar surveys. DVD-based storage has not been widely explored in the pulsar community, but it has several advantages. The DVD archive we have constructed is reliable, portable, inexpensive, and can be easily read by any standard modern machine.