{"id":3723,"date":"2025-09-24T03:56:01","date_gmt":"2025-09-24T03:56:01","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3723"},"modified":"2025-09-24T14:30:46","modified_gmt":"2025-09-24T14:30:46","slug":"flash-attention-inspired-queuing-for-ultra-low-latency-communication-networks","status":"publish","type":"page","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3723","title":{"rendered":"Flash Attention-Inspired Queuing for Ultra-Low Latency Communication Networks"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/Flash-Attention-Inspired-Queuing-for-Ultra-Low-1.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Flash Attention-Inspired Queuing for Ultra-Low.\"><\/object><a id=\"wp-block-file--media-ce59b72f-52fe-4c2f-a949-18acf7446901\" href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/Flash-Attention-Inspired-Queuing-for-Ultra-Low-1.pdf\">Flash Attention-Inspired Queuing for Ultra-Low<\/a><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/Flash-Attention-Inspired-Queuing-for-Ultra-Low-1.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-ce59b72f-52fe-4c2f-a949-18acf7446901\">Download<\/a><\/div>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2308.05022v3\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p>We present a queuing subsystem that adapts FlashAttention ideas to message middleware: (i) FlashQueue, a priority<br>queue with optional async event loop; and (ii) MemoryMappedFlashQueue, which adds a \u201chot\u201d SRAM-like buffer and a cold<br>HBM-like backing priority queue. We report latency, cachehit ratio, and throughput under synthetic workloads, showing<br>predictable wins from hot-buffer admission control and async<br>event loops<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2308.05022v3\" target=\"_blank\" rel=\"noreferrer noopener\">Exploring Frequency-Inspired Optimization in Transformer for &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2308.05022v3\" target=\"_blank\" rel=\"noreferrer noopener\">Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2308.05022v3\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2410.06112v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2410.06112v1\" target=\"_blank\" rel=\"noreferrer noopener\">Optimizing Low-Latency Applications with Swift Packet Queuing &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2410.06112v1\" target=\"_blank\" rel=\"noreferrer noopener\">Inspired by this, we present SwiftQueue, a new L4S queue-selection system driven by a custom Transformer for per-packet latency prediction.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2410.06112v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2403.10808\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2403.10808\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Transformer-Based Wireless Traffic Prediction and Network &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2403.10808\" target=\"_blank\" rel=\"noreferrer noopener\">Abstract\u2014This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2403.10808\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2407.05941v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2407.05941v1\" target=\"_blank\" rel=\"noreferrer noopener\">Reducing Vision Transformer Latency on Edge Devices via GPU &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2407.05941v1\" target=\"_blank\" rel=\"noreferrer noopener\">This paper investigates how to efficiently deploy transformer-based neural networks on edge devices. Recent methods reduce the latency of &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2407.05941v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.19342v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.19342v1\" target=\"_blank\" rel=\"noreferrer noopener\">Communication-Efficient Multi-Device Inference Acceleration &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.19342v1\" target=\"_blank\" rel=\"noreferrer noopener\">We propose Astra, a communication-efficient framework that accelerates Transformer inference through a novel integration of sequence parallelism &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.19342v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.05199v2\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.05199v2\" target=\"_blank\" rel=\"noreferrer noopener\">Decision Transformers for Wireless Communications: A New &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.05199v2\" target=\"_blank\" rel=\"noreferrer noopener\">In this paper, we adopt an alternative AI technology, namely, Decision Transformer (DT), and propose a DT-based adaptive decision architecture for wireless &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.05199v2\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.03621v4\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.03621v4\" target=\"_blank\" rel=\"noreferrer noopener\">JPPO++: Joint Power and Denoising-inspired Prompt Optimization &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.03621v4\" target=\"_blank\" rel=\"noreferrer noopener\">We propose Joint Prompt and Power Optimization (JPPO), a framework that jointly optimizes prompt compression and wireless transmission power for mobile LLM &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.03621v4\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.13492v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.13492v1\" target=\"_blank\" rel=\"noreferrer noopener\">Quantized Spike-driven Transformer &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.13492v1\" target=\"_blank\" rel=\"noreferrer noopener\">Optimized potential initialization for low-latency spiking neural networks. Proceedings of the AAAI Conference on Artificial Intelligence &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.13492v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2406.16962?\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2406.16962?\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Meta-Learning Inspired Transformer Selection for Green Semantic &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2406.16962?\" target=\"_blank\" rel=\"noreferrer noopener\">This evolution promises significant benefits, including reduced latency, lower bandwidth usage, and higher throughput compared to traditional &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2406.16962?\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.02891v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.02891v1\" target=\"_blank\" rel=\"noreferrer noopener\">Vision Transformers on the Edge: A Comprehensive Survey &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.02891v1\" target=\"_blank\" rel=\"noreferrer noopener\">We systematically categorize and analyze the latest advancements in pruning, quantization, knowledge distillation, and hardware-aware optimizations. Furthermore &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.02891v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2405.02803v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2405.02803v1\" target=\"_blank\" rel=\"noreferrer noopener\">Is Flash Attention Stable? &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2405.02803v1\" target=\"_blank\" rel=\"noreferrer noopener\">Flash Attention is a widely-adopted technique used to speed up the attention mechanism, often considered a system bottleneck in transformer &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2405.02803v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540sthanikamsanthosh1994\/introduction-to-flash-attention-a-breakthrough-in-efficient-attention-mechanism-3eb47e8962c3\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540sthanikamsanthosh1994\/introduction-to-flash-attention-a-breakthrough-in-efficient-attention-mechanism-3eb47e8962c3\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to Flash Attention: A Breakthrough in Efficient &#8230; &#8211; Medium<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540sthanikamsanthosh1994\/introduction-to-flash-attention-a-breakthrough-in-efficient-attention-mechanism-3eb47e8962c3\" target=\"_blank\" rel=\"noreferrer noopener\">Flash Attention marks a significant advancement in attention mechanisms, addressing efficiency concerns and enabling faster and more memory-efficient training &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540sthanikamsanthosh1994\/introduction-to-flash-attention-a-breakthrough-in-efficient-attention-mechanism-3eb47e8962c3\" target=\"_blank\" rel=\"noreferrer noopener\">medium.com<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.mdpi.com\/2227-7390\/13\/13\/2164\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.mdpi.com\/2227-7390\/13\/13\/2164\" target=\"_blank\" rel=\"noreferrer noopener\">Flash-Attention-Enhanced Multi-Agent Deep Deterministic Policy &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.mdpi.com\/2227-7390\/13\/13\/2164\" target=\"_blank\" rel=\"noreferrer noopener\">To improve performance in a MEC scenario, this paper proposes a Flash-Attention-enhanced MADDPG algorithm (FA-MADDPG) for decision making, and its time &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.mdpi.com\/2227-7390\/13\/13\/2164\" target=\"_blank\" rel=\"noreferrer noopener\">mdpi.com<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41467-025-60252-z\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41467-025-60252-z\" target=\"_blank\" rel=\"noreferrer noopener\">An end-to-end attention-based approach for learning on graphs<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41467-025-60252-z\" target=\"_blank\" rel=\"noreferrer noopener\">GraphGPS natively supports Flash attention, while Graphormer requires specific modifications to the attention matrix that are not currently &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41467-025-60252-z\" target=\"_blank\" rel=\"noreferrer noopener\">nature.com<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2405.02803\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2405.02803\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Is Flash Attention Stable? &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2405.02803\" target=\"_blank\" rel=\"noreferrer noopener\">We find that Flash Attention sees roughly an order of magnitude more numeric deviation as compared to. Baseline Attention at BF16 when measured &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2405.02803\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.shaped.ai\/blog\/jagged-flash-attention-optimization\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.shaped.ai\/blog\/jagged-flash-attention-optimization\" target=\"_blank\" rel=\"noreferrer noopener\">Jagged Flash Attention Optimization | Shaped Blog<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.shaped.ai\/blog\/jagged-flash-attention-optimization\" target=\"_blank\" rel=\"noreferrer noopener\">By combining jagged tensors with flash attention, this innovation achieves up to 9\u00d7 speedup and 22\u00d7 memory reduction compared to dense attention &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.shaped.ai\/blog\/jagged-flash-attention-optimization\" target=\"_blank\" rel=\"noreferrer noopener\">shaped.ai<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/machinelearningatscale.substack.com\/p\/64-challenges-and-solutions-of-long\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/machinelearningatscale.substack.com\/p\/64-challenges-and-solutions-of-long\" target=\"_blank\" rel=\"noreferrer noopener\">64. Breaking the Attention Barrier: A Deep Dive into Scaling LLM &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/machinelearningatscale.substack.com\/p\/64-challenges-and-solutions-of-long\" target=\"_blank\" rel=\"noreferrer noopener\">Flash Attention is an algorithm designed to address the memory and computational bottlenecks associated with attention mechanisms in large &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/machinelearningatscale.substack.com\/p\/64-challenges-and-solutions-of-long\" target=\"_blank\" rel=\"noreferrer noopener\">machinelearningatscale.substack.com<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/openreview.net\/forum?id=H4DqfPSibmx\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/openreview.net\/forum?id=H4DqfPSibmx\" target=\"_blank\" rel=\"noreferrer noopener\">FlashAttention: Fast and Memory-Efficient Exact Attention with IO &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/openreview.net\/forum?id=H4DqfPSibmx\" target=\"_blank\" rel=\"noreferrer noopener\">It would interesting to see a roofline plot to demonstrate the compute-bound and memory access trade-off with and without flash-attention.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/openreview.net\/forum?id=H4DqfPSibmx\" target=\"_blank\" rel=\"noreferrer noopener\">openreview.net<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3735358.3735382\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3735358.3735382\" target=\"_blank\" rel=\"noreferrer noopener\">Rethinking Dynamic Networks and Heterogeneous Computing with &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3735358.3735382\" target=\"_blank\" rel=\"noreferrer noopener\">A classic example is Flash Attention[8], which combines originally independent operations such as matmul, dropout, softmax, and mask into a &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3735358.3735382\" target=\"_blank\" rel=\"noreferrer noopener\">dl.acm.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540damienjose\/flash-attention-with-cuda-c45d9167e8dc\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540damienjose\/flash-attention-with-cuda-c45d9167e8dc\" target=\"_blank\" rel=\"noreferrer noopener\">Flash Attention with CUDA. Introduction | by Damien J | Medium<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540damienjose\/flash-attention-with-cuda-c45d9167e8dc\" target=\"_blank\" rel=\"noreferrer noopener\">Flash Attention, as the name suggests, brings a fast and memory-efficient solution to attention mechanisms. It addresses some of the &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/%2540damienjose\/flash-attention-with-cuda-c45d9167e8dc\" target=\"_blank\" rel=\"noreferrer noopener\">medium.com<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.08944v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.08944v1\" target=\"_blank\" rel=\"noreferrer noopener\">Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.08944v1\" target=\"_blank\" rel=\"noreferrer noopener\">This paper introduces Asynchronous Expert Parallelism (AEP), a new paradigm that decouples layer execution from barrier-style synchronization.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2505.08944v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.09591v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.09591v1\" target=\"_blank\" rel=\"noreferrer noopener\">HierMoE: Accelerating MoE Training with Hierarchical Token &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.09591v1\" target=\"_blank\" rel=\"noreferrer noopener\">The mixture-of-experts (MoE) architecture with sparse activation has gained significant research interest in large language models (LLMs) [1, 2, &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.09591v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.13337v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.13337v1\" target=\"_blank\" rel=\"noreferrer noopener\">X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.13337v1\" target=\"_blank\" rel=\"noreferrer noopener\">Emerging expert-specialized Mixture-of-Experts (MoE) architectures, such as DeepSeek-MoE, deliver strong model quality through fine-grained &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2508.13337v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2508.13337?\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2508.13337?\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2508.13337?\" target=\"_blank\" rel=\"noreferrer noopener\">X-MoE accomplishes this via a combination of techniques, such as padding-free MoE training with cross-platform kernels for improved memory and.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2508.13337?\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2404.19429?\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2404.19429?\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Lancet: Accelerating Mixture-of-Experts Training via Whole Graph &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2404.19429?\" target=\"_blank\" rel=\"noreferrer noopener\">ABSTRACT. The Mixture-of-Expert (MoE) technique plays a crucial role in expanding the size of DNN model parameters.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2404.19429?\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.10714v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.10714v1\" target=\"_blank\" rel=\"noreferrer noopener\">FSMoE: A Flexible and Scalable Training System for Sparse Mixture &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.10714v1\" target=\"_blank\" rel=\"noreferrer noopener\">As the experts are distributed across multiple devices, the dispatch operation uses a collective communication technique called AlltoAll &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2501.10714v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.04667v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.04667v1\" target=\"_blank\" rel=\"noreferrer noopener\">FlashDMoE: Fast Distributed MoE in a Single Kernel &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.04667v1\" target=\"_blank\" rel=\"noreferrer noopener\">This work introduces FlashDMoE, the first system to fuse the entire Mixture-of-Experts (MoE) operator into a single, persistent GPU kernel. We &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.04667v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2509.07003v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2509.07003v1\" target=\"_blank\" rel=\"noreferrer noopener\">Consistent and Efficient Tensor Programming with Eager-Mode SPMD<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2509.07003v1\" target=\"_blank\" rel=\"noreferrer noopener\">&#8230; dispatch overhead per operator is intolerable, particularly in models containing Mixture of Experts (MoE). Because thousands of lightweight &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2509.07003v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2402.14672v2\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2402.14672v2\" target=\"_blank\" rel=\"noreferrer noopener\">Middleware for LLMs: Tools Are Instrumental for Language Agents &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2402.14672v2\" target=\"_blank\" rel=\"noreferrer noopener\">In particular, Mixtral represents an advanced mixture-of-experts model that has demonstrated superior performance and even surpasses GPT-3.5-turbo on Chatbot &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2402.14672v2\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.19429v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.19429v1\" target=\"_blank\" rel=\"noreferrer noopener\">1 Introduction &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.19429v1\" target=\"_blank\" rel=\"noreferrer noopener\">The Mixture-of-Expert (MoE) technique plays a crucial role in expanding the size of DNN model parameters. However, it faces the challenge of &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.19429v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.04398v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.04398v1\" target=\"_blank\" rel=\"noreferrer noopener\">Communication Efficient Parallel MoE Inference with Speculative &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.04398v1\" target=\"_blank\" rel=\"noreferrer noopener\">Speculative MoE has two speculative parallelization schemes, speculative token shuffling and speculative expert grouping, which predict outstanding tokens&#8217; &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.04398v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.19732v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.19732v1\" target=\"_blank\" rel=\"noreferrer noopener\">Speculative Decoding and Beyond: An In-Depth Review of &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.19732v1\" target=\"_blank\" rel=\"noreferrer noopener\">This survey presents a comprehensive taxonomy of generation-refinement frameworks, analyzing methods across autoregressive sequence tasks.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.19732v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2509.04576\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2509.04576\" target=\"_blank\" rel=\"noreferrer noopener\">Communication-Efficient Collaborative LLM Inference via Distributed &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2509.04576\" target=\"_blank\" rel=\"noreferrer noopener\">Abstract:Speculative decoding is an emerging technique that accelerates large language model (LLM) inference by allowing a smaller draft &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2509.04576\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/1801.01203\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/1801.01203\" target=\"_blank\" rel=\"noreferrer noopener\">[1801.01203] Spectre Attacks: Exploiting Speculative Execution &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/1801.01203\" target=\"_blank\" rel=\"noreferrer noopener\">Spectre attacks exploit speculative execution, inducing a victim to perform operations that leak confidential information via side channels.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/1801.01203\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2502.19732\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2502.19732\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Speculative Decoding and Beyond: An In-Depth Survey of Techniques<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2502.19732\" target=\"_blank\" rel=\"noreferrer noopener\">Speculative decoding (SD) uses a two-phase process: a draft model predicts multiple tokens in parallel, followed by verification using the &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2502.19732\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.14897v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.14897v1\" target=\"_blank\" rel=\"noreferrer noopener\">A Survey of Speculative Execution in Large Language Models &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.14897v1\" target=\"_blank\" rel=\"noreferrer noopener\">We present the very first survey paper that reviews and unifies literature of speculative execution in LLMs (eg, blockwise parallel decoding, speculative &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2404.14897v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.04557v3\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.04557v3\" target=\"_blank\" rel=\"noreferrer noopener\">Speeding up Speculative Decoding via Sequential Approximate &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.04557v3\" target=\"_blank\" rel=\"noreferrer noopener\">Speculative Decoding (SD) is a recently proposed technique for faster inference using Large Language Models (LLMs).<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.04557v3\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2204.03552\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2204.03552\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] On the Correctness of Speculative Consensus &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2204.03552\" target=\"_blank\" rel=\"noreferrer noopener\">Consensus protocols allow changes supported by the majority. The Proof-of-Execution (PoE) protocol uses speculative execution to minimize &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2204.03552\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.09397v3\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.09397v3\" target=\"_blank\" rel=\"noreferrer noopener\">A Speculative LLM Decoding Framework for Efficient Edge Serving<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.09397v3\" target=\"_blank\" rel=\"noreferrer noopener\">This position paper introduces a new framework that leverages speculative decoding, previously viewed primarily as a decoding acceleration &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2506.09397v3\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.10325v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.10325v1\" target=\"_blank\" rel=\"noreferrer noopener\">Collaborative Speculative Inference for Efficient LLM Inference Serving<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.10325v1\" target=\"_blank\" rel=\"noreferrer noopener\">Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.10325v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.01889v4\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.01889v4\" target=\"_blank\" rel=\"noreferrer noopener\">Ring Attention with Blockwise Transformers for Near-Infinite Context<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.01889v4\" target=\"_blank\" rel=\"noreferrer noopener\">Our proposed approach Ring Attention allows training up to device count times longer sequence than baselines and enables the training of sequences that exceed &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.01889v4\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2310.01889\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2310.01889\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Ring Attention with Blockwise Transformers for Near-Infinite Context.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2310.01889\" target=\"_blank\" rel=\"noreferrer noopener\">Transformers have emerged as the architecture of choice for many state-of-the-art AI models, showcasing exceptional performance across a &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2310.01889\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2311.09431\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2311.09431\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Striped Attention: Faster Ring Attention for Causal Transformers &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2311.09431\" target=\"_blank\" rel=\"noreferrer noopener\">We propose Striped Attention, a variant of Ring Attention which permutes the input sequence in a way which almost entirely eliminates the &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2311.09431\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.03294v2\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.03294v2\" target=\"_blank\" rel=\"noreferrer noopener\">Distributed Memory-efficient Attention for Long-context LLMs Training<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.03294v2\" target=\"_blank\" rel=\"noreferrer noopener\">In this paper, we introduce DistFlashAttn, a distributed memory-efficient attention mechanism optimized for long-context LLMs training.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2310.03294v2\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.15758v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.15758v1\" target=\"_blank\" rel=\"noreferrer noopener\">Communication Efficient Distributed Self-Attention Mechanism &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.15758v1\" target=\"_blank\" rel=\"noreferrer noopener\">The original Ring Attention&#8217;s [20] block distribution caused load imbalances when applying causal attention. &#8230; Large scale distributed deep &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2503.15758v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.20501v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.20501v1\" target=\"_blank\" rel=\"noreferrer noopener\">TokenRing: An Efficient Parallelism Framework for Infinite-Context &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.20501v1\" target=\"_blank\" rel=\"noreferrer noopener\">TokenRing addresses a critical challenge in distributed systems\u2014such as in Ring Attention\u2014where communication and computation cannot be &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2412.20501v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.02406v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.02406v1\" target=\"_blank\" rel=\"noreferrer noopener\">LV-XAttn: Distributed Cross-Attention for Long Visual Inputs &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.02406v1\" target=\"_blank\" rel=\"noreferrer noopener\">Figure 2 shows that cross-attention operations distributed with Ring Attention (Liu et al., 2024a) can account for up to 87% of the &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.02406v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2411.17116\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2411.17116\" target=\"_blank\" rel=\"noreferrer noopener\">Star Attention: Efficient LLM Inference over Long Sequences &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2411.17116\" target=\"_blank\" rel=\"noreferrer noopener\">Among these, only Ring Attention is a distributed algorithm designed to scale inference across multiple GPUs. Since Star Attention also targets distributed &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2411.17116\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2502.07563\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2502.07563\" target=\"_blank\" rel=\"noreferrer noopener\">LASP-2: Rethinking Sequence Parallelism for Linear Attention and &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2502.07563\" target=\"_blank\" rel=\"noreferrer noopener\">In this paper, we introduce LASP-2, a new SP method to enhance both communication and computation parallelism when training linear attention transformer models.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2502.07563\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.01659v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.01659v1\" target=\"_blank\" rel=\"noreferrer noopener\">Increasing Transformer Context Length with Sparse Graph &#8230; &#8211; arXiv<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.01659v1\" target=\"_blank\" rel=\"noreferrer noopener\">Ring attention achieves sequence parallelism &#8230; networks with global attention,\u201d Advances in Neural Information Processing Systems, vol.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/html\/2502.01659v1\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a queuing subsystem that adapts FlashAttention ideas to message middleware: (i) FlashQueue, a priorityqueue with optional async event loop; and (ii) MemoryMappedFlashQueue, which adds a \u201chot\u201d SRAM-like buffer and a coldHBM-like backing priority queue. We report latency, cachehit ratio, and throughput under synthetic workloads, showingpredictable wins from hot-buffer admission control and asyncevent loops&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3723\" rel=\"bookmark\"><span class=\"screen-reader-text\">Flash Attention-Inspired Queuing for Ultra-Low Latency Communication Networks<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":2692,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"googlesitekit_rrm_CAowgMPcCw:productID":"","neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-3723","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/pages\/3723","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3723"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/pages\/3723\/revisions"}],"predecessor-version":[{"id":3727,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/pages\/3723\/revisions\/3727"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/2692"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}