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

V
Visual Studio Blog
Recent Announcements
Recent Announcements
有赞技术团队
有赞技术团队
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium
腾讯CDC
A
About on SuperTechFans
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
D
DataBreaches.Net
D
Docker
宝玉的分享
宝玉的分享
量子位
Microsoft Azure Blog
Microsoft Azure Blog
Martin Fowler
Martin Fowler
博客园 - 三生石上(FineUI控件)
Jina AI
Jina AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
V
V2EX
Last Week in AI
Last Week in AI
H
Help Net Security
Hugging Face - Blog
Hugging Face - Blog
M
MIT News - Artificial intelligence

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
Cppless: Single-Source and High-Performance Serverless Pr...
Marcin Copik, Lukas Möller, Alexandru Calotoiu, Torsten Hoefler · 2024-01-20 · via cs.DC updates on arXiv.org

The rise of serverless computing introduced a new class of scalable, elastic and widely available parallel workers in the cloud. Many systems and applications benefit from offloading computations and parallel tasks to dynamically allocated resources. However, the developers of C++ applications find it difficult to integrate functions due to complex deployment, lack of compatibility between client and cloud environments, and loosely typed input and output data. To enable single-source and efficient serverless acceleration in C++, we introduce Cppless, an end-to-end framework for implementing remote functions which handles the creation, deployment, and invocation of serverless functions. Cppless is built on top of LLVM and requires only two compiler extensions to automatically extract C++ function objects and deploy them to the cloud. We demonstrate that offloading parallel computations, such as from a C++ application to serverless workers, can provide up to 59x speedup with minimal cost increase while requiring only minor code modifications.