'Pipeline depth' presentation slideshows

The Big Picture

f1. f2. f5. f3. f4. i. p. s. q. j. fp. f3. The Big Picture. SPEC. Requirements. Problem Focus. Algorithms. f2() { f3(s2, &j, &i); *s2->p = 10; i = *s2->q + i; }. Prog. Lang./OS. i1: ld r1, b <p1> i2: ld r2, c <p1> i3: ld r5, z <p3>

700 views • 60 slides

The Big Picture

f1. f2. f5. f3. f4. i. p. s. q. j. fp. f3. The Big Picture. SPEC. Requirements. Problem Focus. Algorithms. f2() { f3(s2, &j, &i); *s2->p = 10; i = *s2->q + i; }. Prog. Lang./OS. i1: ld r1, b <p1> i2: ld r2, c <p1> i3: ld r5, z <p3>

674 views • 60 slides

Computer Architecture

Computer Architecture. Appendix A Pipelining Prof. Jerry Breecher CSCI 240 Fall 2003. Introduction.

691 views • 58 slides

Computer Architecture

Computer Architecture. Appendix A Pipelining Prof. Jerry Breecher CSCI 240 Fall 2003. Introduction.

710 views • 58 slides

Computer Architecture

Computer Architecture. Lecture 4 Pipelining. Introduction. A.1 What is Pipelining? A.2 The Major Hurdle of Pipelining-Structural Hazards Data Hazards Control Hazards A.3 How is Pipelining Implemented A.4 What Makes Pipelining Hard to Implement?

798 views • 56 slides

Appendix A - Pipelining

608 views • 43 slides

Appendix C: Pipelining

628 views • 43 slides

Appendix A: Pipelining

548 views • 43 slides

Appendix C: Pipelining

583 views • 43 slides

CS136, Advanced Architecture

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586 views • 40 slides

Lecture 2 Performance, Instruction Set Principles, Pipeline Hazards

CS 203A Advanced Computer Architecture. Lecture 2 Performance, Instruction Set Principles, Pipeline Hazards. Instructor: L.N. Bhuyan. RISC Vs CISC. CISC (complex instruction set computer) VAX, Intel X86, IBM 360/370, etc. RISC (reduced instruction set computer)

445 views • 26 slides

CS 230: Computer Organization and Assembly Language

CS 230: Computer Organization and Assembly Language. Aviral Shrivastava. Department of Computer Science and Engineering School of Computing and Informatics Arizona State University. Slides courtesy: Prof. Yann Hang Lee, ASU, Prof. Mary Jane Irwin, PSU, Ande Carle, UCB. Announcements.

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Lecture 24: Instruction Level Parallelism

Lecture 24: Instruction Level Parallelism. Computer Engineering 585 Fall 2001. Limits to Multi-Issue Machines. Inherent limitations of ILP: 1 branch in 5 : How to keep a 5-way VLIW busy? Latencies of units : many operations must be scheduled.

292 views • 21 slides

Forwarding to Avoid Data Hazard Figure 3.10, Page 149 , CA:AQA 2e

Reg. Reg. Reg. Reg. Reg. Reg. Reg. Reg. Reg. Reg. ALU. ALU. ALU. ALU. ALU. Ifetch. Ifetch. Ifetch. Ifetch. Ifetch. DMem. DMem. DMem. DMem. DMem. I n s t r. O r d e r. add r1 ,r2,r3. sub r4, r1 ,r3. and r6, r1 ,r7. or r8, r1 ,r9. xor r10, r1 ,r11.

329 views • 21 slides

Lecture 10: Pipelining

Lecture 10: Pipelining. Computer Engineering 585 Fall 2001. DLX Stages: RTL activities. Pipelining is Not That Easy for Computers. Limits to pipelining: Hazards prevent next instruction from executing during its designated clock cycle

316 views • 16 slides

CS 152, Spring 2010 Section 3

CS 152, Spring 2010 Section 3. Andrew Waterman. University of California, Berkeley. Agenda. Precise Exceptions Problem Set 1 review. Precise Exceptions. Goal: provide illusion of sequential, non-overlapping instruction execution

241 views • 14 slides

Simple Processor Architectures Modeling Using YAPI

Simple Processor Architectures Modeling Using YAPI. Sam Williams EE249 Project December 3, 2002 Mentors: Trevor Meyerowitz & Kees Vissers. Introduction. YAPI is a Philips Kahn process network library Ported to the Metropolis meta model and extended JAVA like interface

268 views • 11 slides

DSP Processors

DSP. y. x. ALU with ADD, MULT, etc. bus. memory. registers. PC. a. b. XTAL. t. c. d. DSP Processors. We have seen that the Multiply and Accumulate (MAC) operation is very prevalent in DSP computation computation of energy MA filters AR filters correlation of two signals FFT

307 views • 9 slides

Accuracy, Cost and Performance Tradeoffs for Floating Point Accumulation

Accuracy, Cost and Performance Tradeoffs for Floating Point Accumulation Krishna K. Nagar & Jason D. Bakos University of South Carolina, Columbia, SC. Objective Achieve high throughput for streaming set-wise floating point summation without sacrificing accuracy. Issues

160 views • 1 slides