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
大猫的无限游戏
大猫的无限游戏
J
Java Code Geeks
MongoDB | Blog
MongoDB | Blog
Martin Fowler
Martin Fowler
GbyAI
GbyAI
Microsoft Azure Blog
Microsoft Azure Blog
Recent Announcements
Recent Announcements
F
Fortinet All Blogs
B
Blog
U
Unit 42
B
Blog RSS Feed
D
DataBreaches.Net
Google DeepMind News
Google DeepMind News
人人都是产品经理
人人都是产品经理
腾讯CDC
量子位
酷 壳 – CoolShell
酷 壳 – CoolShell
V
Visual Studio Blog
博客园 - 聂微东
MyScale Blog
MyScale Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 三生石上(FineUI控件)
Engineering at Meta
Engineering at Meta

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
Report on the performance portability demonstrated for th...
Carlos Osuna · 2019-08-17 · via cs.DC updates on arXiv.org

This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying Weather & Climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. This deliverable provides an evaluation of the work performed within ESCAPE to port different dwarfs to accelerators, using different programming models. A key metric of the evaluation is the performance portability of the resulting porting efforts. Portability means that a single source code containing the numerical operators can be compiled and run in multiple architectures, while performance portability additionally requires that the single source code runs efficiently in all the different architectures. As results of other deliverables like D2.1, D2.4 ESCAPE provides a collection of dwarfs ported to different computing architectures like traditional CPUs, Intel XeonPhi and NVIDIA GPUs. Additionally D3.3 went through an optimization process to obtain efficient and energy efficient dwarfs. In this deliverable we present a review of the different programming models employed and their use to port various dwarfs of ESCAPE. A final evaluation of the different approaches based on different metrics like performance portability, readability of the numerical methods, efforts to port a dwarf and efficiency of the implementation obtained is reported.