arXiv AI
NVIDIA Spectrum-X: Hardware-Accelerated Multiplane Networking for Large-Scale AI Training
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arXiv Machine Learning
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MLCC: A Congestion Control Technique to Accelerate ML Training
arXiv:2402. 09589v2 Announce Type: replace-cross Abstract: We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner.
arXiv Machine Learning
Sep 30
Mixture-of-Kittens: MoE Megakernel for NVL72s
arXiv:2609.36070v1 Announce Type: cross Abstract: AI accelerator systems are rapidly consolidating into scale-up architectures, where tens to thousands of GPUs communicate over high-bandwidth, single...
arXiv AI
Aug 3
Topology-Aware Data Movement for Disaggregated GPU Inference
arXiv:2607. 28633v1 Announce Type: cross Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly.
arXiv Machine Learning
Sep 10
Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems
The paper reports an empirical scalability study of data‑parallel training for Kolmogorov‑Arnold Networks (KANs) on high‑performance computing systems. Using up to eight NVIDIA A100 GPUs across four nodes on the FinisTerrae III supercomputer, the authors evaluate strong and weak scaling, communication overhead, and model‑size scaling, finding a 74.7% parallel efficiency and a 5.97× speedup at eight GPUs. They observe non‑monotonic communication costs driven by All‑Reduce choices and inter‑node latency, and note that while the parameter‑to‑memory ratio improves with larger models, training time scales less favorably, leading to guidelines for GPU topology and model‑size selection.
arXiv Machine Learning
Jun 10
ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
arXiv:2606. 10440v1 Announce Type: cross Abstract: Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications.
arXiv AI
Aug 11
LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs
arXiv:2608. 07733v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems.