Latency time的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Network and Parallel Computing: 18th IFIP WG 10.3 International Conference, NPC 2021, Paris, France, November 3-5, 2021, Proceed

為了解決Latency time的問題，作者 這樣論述：

Algorithms and Applications.- High Resolution of City-Level Climate Simulation by GPU with Multi-Physical Phenomena.- dgQuEST: Distributed-GPU Accelerated Large Scale Quantum Circuit Simulation.- vSketchDLC: A Sketch on Distributed Deep Learning Communication via Fine-grained Tracing Visualization.-

Scalable Algorithms Using Sparse Storage for Parallel Spectral Clustering on GPU.- XSP: Fast SSSP Based on Communication-Computation Collaboration.- A Class Of Fast And Accurate Multi-layer Block Summation And Dot Product Algorithms.- A KNN Query Method for Autonomous Driving Sensor Data.- System S

oftware and Resource Management.- A Novel Task-Allocation Framework based on Decision-Tree Classification Algorithm in MEC.- QoS-Aware Scheduling for Cellular Networks Using Deep Reinforcement Learning.- Adaptive Buffering Scheme for PCM/DRAM-Based Hybrid Memory Architecture.- Efficiency-First Fault

-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application.- Towards an Optimized Containerization of HPC Job Schedulers based on Namespaces.- Architecture of an On-time Data Transfer Framework in Cooperation with Scheduler System.- Storage.- Data Delta Based Hybrid Writes f

or Erasure-Coded Storage Systems.- BDCuckoo: An Efficient Cuckoo Hash for BlockDevice.- A Two Tier Hybrid Metadata Management mechanism for NVM Storage System.- A Novel CFLRU-based Cache Management Approach for NAND-based SSDs.- Networks and Communications.- Taming Congestion and Latency in Low-Diam

eter High-Performance Datacenters.- Evaluation of Topology-Aware All-reduce Algorithm for Dragon y Networks.- MPICC: Multi-Path INT-based Congestion Control in Datacenter Networks.

Latency time進入發燒排行的影片

應用於脈衝神經元之閥門開關選擇器： 元件特性分析與模型開發

為了解決Latency time的問題，作者葉淑銘 這樣論述：

隨著這個世代對數據存儲與處理的需求不斷增長，使用傳統馮諾依曼(von-Neumann)架構的計算系統面臨著速度上的限制。這是因爲傳統馮諾依曼架構中分離的處理器和記憶體單元之間頻繁的數據傳輸使得計算效率無法提升。近年來，受人類大腦運作模式啟發的類神經計算(brain-inspired computing)成為一個引人注目的話題。與傳統計算系統不同的是，類神經計算(neuromorphic computing)通過使用交錯式記憶體陣列(crossbar memory array)實現記憶體內計算(in-memory computing)，進而縮短了數據傳輸的時間延遲。因此，類神經計算被視為非常有

潛力成為非馮諾依曼架構之候選人。為了開發具有高性能、低功耗特性的類神經計算硬體，使用元件為基礎(device-based)之人工突觸(synapse)和神經元(neuron)受到廣泛的研究。其中，利用閾值切換(threshold switching，TS)選擇器(selector)所構建之人工神經元有著比傳統以CMOS所建構之神經元電路面積小40倍的優勢，因此被認為是前景看好的候選人之一。因此，學術界提出了一個電路層級之模型來進一步研究 TS 神經元的行爲。此模型透過考慮神經元電路中的電阻電容延遲(RC delay) 來執行 TS 神經元之行為。然而，該模型並沒有考慮 TS 神經元中 TS 選

擇器的實際元件特性。因此，目前還缺乏一個有綜合考慮TS 神經元元件特性以及電路RC 延遲的模型。在本論文中，我們構建了一個以成核理論(nucleation theory)爲基礎的電壓-時間轉換模型(V-t transition model)來預測和模擬 TS 神經元的行為。據我們所知，這是第一個詳細考慮了 TS 選擇器中元件成核條件的 TS 神經元模型。模擬結果也顯示了 TS 選擇器的元件特性與 TS 神經元行為之間存在很強的相關性。最后，此V-t 模型為 TS 神經元的未來發展提供了一個良好的設計方針：即具有低 τ0 的 TS 選擇器是首選。因此，模擬結果顯示，與IMT (insulator

-metal-transition) 和Ag-based神經元相比，具有極小τ0的OTS (ovonic threshold switching) 神經元擁有最理想的特性。

Stream Processing Pocket Reference: Real-Time Any-Scale Data Processing

為了解決Latency time的問題，作者Akidau, Tyler,Chernyak, Slava,Lax, Reuven 這樣論述：

Tyler Akidau is principal software engineer at Snowflake. Previously senior staff software engineer at Google, he was the technical lead for the Data Processing Languages & Systems group, responsible for Google’s Apache Beam efforts, Google Cloud Dataflow, and internal data processing tools like Goo

gle Flume, MapReduce, and MillWheel. His also a founding member of the Apache Beam PMC. Though deeply passionate and vocal about the capabilities and importance of stream processing, he is also a firm believer in batch and streaming as two sides of the same coin, with the real endgame for data proce

ssing systems the seamless merging between the two. He is the author of the 2015 Dataflow Model paper and the Streaming 101 and Streaming 102 articles on the O’Reilly website. His preferred mode of transportation is by cargo bike, with his two young daughters in tow.Slava Chernyak is a senior softwa

re engineer at Google Seattle. Slava spent over five years working on Google’s internal massive-scale streaming data processing systems and has since become involved with designing and building Windmill, Google Cloud Dataflow’s next-generation streaming backend, from the ground up. Slava is passiona

te about making massive-scale stream processing available and useful to a broader audience. When he is not working on streaming systems, Slava is out enjoying the natural beauty of the Pacific Northwest.Reuven Lax is a senior staff software engineer at Google Seattle, and has spent the past nine yea

rs helping to shape Google’s data processing and analysis strategy. For much of that time he has focused on Google’s low-latency, streaming data processing efforts, first as a long-time member and lead of the MillWheel team, and more recently founding and leading the team responsible for Windmill, t

he next-generation stream processing engine powering Google Cloud Dataflow. He’s very excited to bring Google’s data-processing experience to the world at large, and proud to have been a part of publishing both the MillWheel paper in 2013 and the Dataflow Model paper in 2015. When not at work, Reuve

n enjoys swing dancing, rock climbing, and exploring new parts of the world.Austin Bennett designs data systems to help move, share, gather insights and develop data products efficiently.

平均位移法應用於可微分式架構搜索

為了解決Latency time的問題，作者周承翰 這樣論述：

在神經架構搜索(NAS)中，可微分式架構搜索(DARTS)是一項基於連續鬆弛網路且有效率的搜索方法，同時能在較低的搜索運算資源中實現。DARTS不僅在自動化機器學習(Auto-ML)領域中吸引了很多研究者的關注，且在近期被視為NAS領域的標竿方法之一。儘管DARTS在搜索效率上比傳統的NAS方法更佳，但DARTS在離散轉換到連續的過程卻有著搜索穩定性的問題。經過再現實驗並觀察，發現DARTS的搜索不穩定性會導致驗證階段的準確率急劇降低，為了解決此問題，此篇論文提出將平均位移(Mean-Shift)應用於DARTS上，在搜索時加入此一方法的採樣並配合搜索擾動機制。這方法在物理意義上，藉由適當的

Mean-Shift參數平滑化連續情況下搜索時的損失，來提升DARTS的搜索穩定性和驗證階段的準確率。而平均位移應用於DARTS上的收斂性，可以依據平均位移法的半徑長度等參數來做調整。最終我將此一方法驗證在諸個公開的資料集上，如：Cifar10、Cifar100、ImageNet，且在這些資料集上的準確率都達到相關方法的最佳。