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

WordPress大学
WordPress大学
Last Week in AI
Last Week in AI
U
Unit 42
aimingoo的专栏
aimingoo的专栏
Engineering at Meta
Engineering at Meta
博
博客园 - 聂微东
小众软件
小众软件
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Recent Announcements
Recent Announcements
罗
罗磊的独立博客
MongoDB | Blog
MongoDB | Blog
Stack Overflow Blog
Stack Overflow Blog
博
博客园_首页
M
MIT News - Artificial intelligence
博
博客园 - 司徒正美
T
The Blog of Author Tim Ferriss
D
DataBreaches.Net
IT之家
IT之家
C
Check Point Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Tailwind CSS Blog
D
Docker
Microsoft Security Blog
Microsoft Security Blog
Google DeepMind News
Google DeepMind News

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
Evaluation of Intel Max GPUs for CGYRO-based fusion simul...
Igor Sfiligoi, Jeff Candy, Emily A. Belli · 2024-10-08 · via cs.DC updates on arXiv.org

Intel Max GPUs are a new option available to CGYRO fusion simulation users. This paper outlines the changes that were needed to successfully run CGYRO on Intel Max 1550 GPUs on TACC's Stampede3 HPC system and presents benchmark results obtained there. Benchmark results were also run on Stampede3 Intel Max CPUs, as well as NVIDIA A100 and AMD MI250X GPUs at other major HPC systems. The Intel Max GPUs are shown to perform comparably to the other tested GPUs for smaller simulations but are noticeably slower for larger ones. Moreover, Intel Max GPUs are significantly faster than the tested Intel Max CPUs on Stampede3.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。