'Reuse distance' presentation slideshows

EE360: Lecture 5 Outline Cellular Systems

EE360: Lecture 5 Outline Cellular Systems. Announcements Project proposals due Feb. 1 (1 week) Makeup lecture Feb 2, 5-6:15, Gates Multiuser OFDM and OFDM/CDMA Cellular System Overview Design Considerations Standards Cellular System Capacity MIMO in Cellular

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MAC schemes

MAC schemes. Prof. Malathi Veeraraghavan. Endpoint. Endpoint. Endpoint. Consider wireless links. Naturally broadcast medium One transmitter sends data; multiple receivers can receive the signal and obtain the data

627 views • 49 slides

Counter Stacks: Storage Workload Analysis via Streaming Algorithms

Counter Stacks: Storage Workload Analysis via Streaming Algorithms. Nick Harvey University of British Columbia and Coho Data Joint work with Zachary Drudi , Stephen Ingram, Jake Wires, Andy Warfield. Caching What data to keep in fast memory?. Fast, Low-Capacity Memory.

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EE360: Lecture 7 Outline Cellular System Capacity and ASE

EE360: Lecture 7 Outline Cellular System Capacity and ASE. Announcements Summary due next week Capacity Area Spectral Efficiency Dynamic Resource Allocation. Review of Cellular Lecture. Design considerations: Spectral sharing, reuse, cell size Evolution: 1G to 2G to 3G to 4G and beyond

566 views • 41 slides

Class Discussion prepared by Bumyong Choi

Locality Phase Prediction Xipeng Shen, Yutao Zhong, Chen Ding Computer Science Department, University of Rochester. Class Discussion prepared by Bumyong Choi. Memory Adaptation . Programs exhibit dynamic locality

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Channel Assignment in Cellular Networks

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Performance Visualizations using XML Representations

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FRD: A Filtering based Buffer Cache Algorithm that Considers both Frequency and Reuse Distance

FRD: A Filtering based Buffer Cache Algorithm that Considers both Frequency and Reuse Distance. 33rd International Conference on Massive Storage Systems and Technology ( MSST 2017). OUTLINE. INTRODUCTION BACKGROUND & MOTIVATION PROPOSED FRD ALGORITHM EVALUATION CONCLUSION. OUTLINE.

338 views • 32 slides

The Cellular Concept

The Cellular Concept. 1. Cell Shape. R. R. R. Cell. R. R. (c) Different cell models. (a) Ideal cell. (b) Actual cell. 2. Impact of Cell Shape and Radius on Service Characteristics. 3. Signal Strength. Signal strength (in dB). Cell i. Cell j. -60. -60. -70. -70. -80. -80.

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CS612 Term Projects

CS612 Term Projects. http://www.cs.cornell.edu/courses/ cs612/2002sp/projects/. Expectations. picking a partner(s) that you can work with, meeting with your project supervisor at least once a week. doing background reading on the topic,

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Visualization Enables the Programmer to Reduce Cache Misses

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Sampling-based Program Locality Approximation

Sampling-based Program Locality Approximation. Yutao Zhong, Wentao Chang Department of Computer Science George Mason University June 8th,2008. Outline. Background information Motivation Our sampling approach Experimental results. 2. Starting Point. Ending Point. 2.

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A Component-based Definition of Spatial Locality

A Component-based Definition of Spatial Locality. Traditional Definition of Spatial locality. When a data element is accessed, the nearby data elements will also be accessed Overall miss rate The fewer the misses, the better the layout. Questions.

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Scheduling for Multithreaded Chip Multiprocessors (Multithreaded CMPs)

Scheduling for Multithreaded Chip Multiprocessors (Multithreaded CMPs). CMTs (Chip multithreaded processors). CMP plus hardware multithreading Supports a large number of thread contexts Can hide memory latency by spawning multiple threads High contention for shared resources

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Using the Compiler to Improve Cache Replacement Decisions

Using the Compiler to Improve Cache Replacement Decisions. Zhenlin Wang, UMass Amherst Kathryn S. McKinley, UT Austin Arnold L. Rosenberg, UMass Amherst Charles C. Weems, UMass Amherst. Motivation and Background. LRU is not always effective

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A Shifting Strategy for Dynamic Channel Assignment under Spatially Varying Demand

A Shifting Strategy for Dynamic Channel Assignment under Spatially Varying Demand. Harish Rathi Advisors: Prof. Karen Daniels, Prof. Kavitha Chandra Center for Advanced Computation and Telecommunications University of Massachusetts Lowell. Problem Statement.

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Performance Modeling

Performance Modeling. Cross-Architecture Performance Predictions for Scientific Applications Using Parameterized Models Marin and Mellor-Crummey. The Gist. Performance modeling is important (theoretical problem sizes on theoretical machines). Build a model that will: Create execution graph

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SCOPE

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