'Vector registers' diaporamas de présentation

Instructor: S. Masoud Sadjadi cs.fiu/~sadjadi/Teaching/

Concurrent Computers. Instructor: S. Masoud Sadjadi http://www.cs.fiu.edu/~sadjadi/Teaching/ sadjadi At cs Dot fiu Dot edu. Acknowledgements.

1.02k views • 89 slides

Instructor: S. Masoud Sadjadi cs.fiu/~sadjadi/Teaching/

Concurrent Computers. Instructor: S. Masoud Sadjadi http://www.cs.fiu.edu/~sadjadi/Teaching/ sadjadi At cs Dot fiu Dot edu. Acknowledgements.

891 views • 89 slides

High-Performance Grid Computing and Research Networking

High-Performance Grid Computing and Research Networking. Concurrent Computers. Instructor: S. Masoud Sadjadi http://www.cs.fiu.edu/~sadjadi/Teaching/ sadjadi At cs Dot fiu Dot edu. Acknowledgements.

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Data-Level Parallelism in Vector and GPU Architectures

Data-Level Parallelism in Vector and GPU Architectures. Muhamed Mudawar Computer Engineering Department King Fahd University of Petroleum and Minerals. Introduction. SIMD architectures can exploit significant data-level parallelism for: matrix-oriented scientific computing

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Vector Computers

Vector Computers. Muhamed Mudawar Computer Engineering Department King Fahd University of Petroleum and Minerals. SCALAR (1 operation). VECTOR (N operations). v2. v1. r2. r1. +. +. r3. v3. vector length. addv v3, v1, v2. add r3, r1, r2. Vector Processing.

832 views • 55 slides

CS252 Graduate Computer Architecture Lecture 20 Vector Processing => Multimedia

CS252 Graduate Computer Architecture Lecture 20 Vector Processing => Multimedia. David E. Culler Many slides due to Christoforos E. Kozyrakis. VECTOR (N operations). SCALAR (1 operation). v2. v1. r2. r1. +. +. r3. v3. vector length. add r3, r1, r2. vadd.vv v3, v1, v2.

542 views • 54 slides

EE (CE) 6304 Computer Architecture Lecture #18 (11/29/17)

EE (CE) 6304 Computer Architecture Lecture #18 (11/29/17). Yiorgos Makris Professor Department of Electrical Engineering University of Texas at Dallas. Course Web-site: http://www.utdallas.edu/~gxm112130/EE6304FA17. Supercomputers. Definition of a supercomputer:

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CS252 Graduate Computer Architecture Lecture 12 Vector Processing

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Implementing Virtual Memory in a Vector Processor with Software Restart Markers

Implementing Virtual Memory in a Vector Processor with Software Restart Markers. Mark Hampton & Krste Asanovic Computer Architecture Group MIT CSAIL. Vector processors offer many benefits. v1[5] v2[5] v1[4] v2[4] v1[3] v2[3] v1[2] v2[2]. v3[1] v3[0].

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CPE 631: Vector Processing

CPE 631: Vector Processing. Electrical and Computer Engineering University of Alabama in Huntsville Aleksandar Milenkovi? milenka@ece.uah.edu http://www.ece.uah.edu/~milenka. Outline. Properties of Vector Processing Components of a Vector Processor Vector Execution Time

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CPE 631 Lecture 24: Vector Processing

CPE 631 Lecture 24: Vector Processing. Aleksandar Milenkovi ? , milenka@ece.uah.edu Electrical and Computer Engineering University of Alabama in Huntsville. Outline. Properties of Vector Processing Components of a Vector Processor Vector Execution Time

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CPE 631 Lecture 23: Vector Processing

CPE 631 Lecture 23: Vector Processing. Aleksandar Milenkovi ? , milenka@ece.uah.edu Electrical and Computer Engineering University of Alabama in Huntsville. Outline. Properties of Vector Processing Components of a Vector Processor Vector Execution Time

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Lecture 9: Branch Prediction, Dependence Speculation, and Data Prediction

Lecture 9: Branch Prediction, Dependence Speculation, and Data Prediction. Prof. John Kubiatowicz Computer Science 252 Fall 1998. Review: Vector Processing. Vector Processing represents an alternative to complicated superscalar processors. Primitive operations on large vectors of data

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Lecture 9: Branch Prediction, Dependence Speculation, and Data Prediction

Lecture 9: Branch Prediction, Dependence Speculation, and Data Prediction. Prof. John Kubiatowicz Computer Science 252 Fall 1998. Review: Vector Processing. Vector Processing represents an alternative to complicated superscalar processors. Primitive operations on large vectors of data

262 views • 26 slides

NCCS Brown Bag Series

NCCS Brown Bag Series. Vectorization Efficient SIMD parallelism on NCCS systems Craig Pelissier* and Kareem Sorathia craig.s.pelissier@nasa.gov, kareem.a.sorathia@nasa.gov SSSO code 610.3 January 28, 2014. Motivation.

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Krste Asanovic Electrical Engineering and Computer Sciences University of California at Berkeley

156 views • 15 slides