Copertina

Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Georganas, Evangelos, Heinecke, Alexander, Dubey, Pradeep
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
di: Georganas, Evangelos, et al.
Pubblicazione: (2023)

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
di: Brock, Benjamin, et al.
Pubblicazione: (2025)

Towards a high-performance AI compiler with upstream MLIR
di: Golin, Renato, et al.
Pubblicazione: (2024)

LMetric: Simple is Better - Multiplication May Be All You Need for LLM Request Scheduling
di: Zhang, Dingyan, et al.
Pubblicazione: (2026)

Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
di: Spoczynski, Marcin, et al.
Pubblicazione: (2026)

FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformation
di: Palihawadana, Chamath, et al.
Pubblicazione: (2024)

ELANA: A Simple Energy and Latency Analyzer for LLMs
di: Chiang, Hung-Yueh, et al.
Pubblicazione: (2025)

SimpleFSDP: Simpler Fully Sharded Data Parallel with torch.compile
di: Zhang, Ruisi, et al.
Pubblicazione: (2024)

Position: LLM Serving Needs Mathematical Optimization and Algorithmic Foundations, Not Just Heuristics
di: Zhou, Zijie
Pubblicazione: (2026)

Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
di: Tariq, Asadullah, et al.
Pubblicazione: (2025)

Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
di: Huang, Yafan, et al.
Pubblicazione: (2026)

FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
di: Li, Qingyuan, et al.
Pubblicazione: (2025)

Multi-IaC-Eval: Benchmarking Cloud Infrastructure as Code Across Multiple Formats
di: Davidson, Sam, et al.
Pubblicazione: (2025)

Demystifying the Communication Characteristics for Distributed Transformer Models
di: Anthony, Quentin, et al.
Pubblicazione: (2024)

Hello SME! Generating Fast Matrix Multiplication Kernels Using the Scalable Matrix Extension
di: Remke, Stefan, et al.
Pubblicazione: (2024)

UCCL-Zip: Lossless Compression Supercharged GPU Communication
di: Ma, Shuang, et al.
Pubblicazione: (2026)

MSCCL++: Rethinking GPU Communication Abstractions for AI Inference
di: Hwang, Changho, et al.
Pubblicazione: (2025)

iOS as Acceleration
di: Chen, Alexander K.
Pubblicazione: (2025)

Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
di: Li, Chengxi, et al.
Pubblicazione: (2026)

Speculative Decoding in Decentralized LLM Inference: Turning Communication Latency into Computation Throughput
di: Song, Jingwei, et al.
Pubblicazione: (2025)

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
di: Liang, Feng, et al.
Pubblicazione: (2024)

Fork is All You Need in Heterogeneous Systems
di: Wang, Zixuan, et al.
Pubblicazione: (2024)

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training
di: Liu, Man, et al.
Pubblicazione: (2026)

Mesh-Attention: A New Communication-Efficient Distributed Attention with Improved Data Locality
di: Chen, Sirui, et al.
Pubblicazione: (2025)

From Data Center IoT Telemetry to Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need
di: Khan, Junaid Ahmed, et al.
Pubblicazione: (2025)

Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression
di: Sung, Mingyu, et al.
Pubblicazione: (2025)

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning
di: Wang, Yisu, et al.
Pubblicazione: (2025)

KAITIAN: A Unified Communication Framework for Enabling Efficient Collaboration Across Heterogeneous Accelerators in Embodied AI Systems
di: Lin, Jieke, et al.
Pubblicazione: (2025)

InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling
di: Jiang, Xiaoxiao, et al.
Pubblicazione: (2025)

Fast Kronecker Matrix-Matrix Multiplication on GPUs
di: Jangda, Abhinav, et al.
Pubblicazione: (2024)

A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication
di: Rodio, Angelo, et al.
Pubblicazione: (2025)

Bike Assisted Evacuation on a Line of Robots with Communication Faults
di: Jawhar, Khaled, et al.
Pubblicazione: (2023)

Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study
di: Wiesner, Philipp, et al.
Pubblicazione: (2026)

Scaling Performance of Large Language Model Pretraining
di: Interrante-Grant, Alexander, et al.
Pubblicazione: (2025)

Ephemeral Rollups are All you Need
di: Picco, Gabriele, et al.
Pubblicazione: (2023)

Sparsity-Aware Roofline Models for Sparse Matrix-Matrix Multiplication
di: Qian, Matthew, et al.
Pubblicazione: (2026)

CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics
di: Sharma, Harshit, et al.
Pubblicazione: (2024)

Low-Rank GEMM: Efficient Matrix Multiplication via Low-Rank Approximation with FP8 Acceleration
di: Metere, Alfredo
Pubblicazione: (2025)

DGEMM on Integer Matrix Multiplication Unit
di: Ootomo, Hiroyuki, et al.
Pubblicazione: (2023)

Accelerating Sparse Matrix-Matrix Multiplication on GPUs with Processing Near HBMs
di: Li, Shiju, et al.
Pubblicazione: (2025)