'Loop unrolling' diaporamas de présentation

CENG 450 Computer Systems and Architecture Lecture 8

CENG 450 Computer Systems and Architecture Lecture 8. Amirali Baniasadi amirali@ece.uvic.ca. This Lecture. ILP Scheduling. What Is an ILP?. Principle: Many instructions in the code do not depend on each other Result: Possible to execute them in parallel

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Datapath interconnections

Multiplexer-oriented datapath. Bus-oriented datapath. Datapath interconnections. Sequential Execution. Example of three micro-operations in the same clock period. Insertion of Latch (out). Insertion of latches at the output ports of the functional units. Insertion of Latch (in/out).

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Cache Design and Tricks

Cache Design and Tricks. Presenters: Kevin Leung Josh Gilkerson Albert Kalim Shaz Husain. What is Cache ? . A cache is simply a copy of a small data segment residing in the main memory Fast but small extra memory Hold identical copies of main memory Lower latency Higher bandwidth

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Embedded Computer Architectures

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Machine-Dependent Optimization

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High-Level Transformations for Embedded Computing

High-Level Transformations for Embedded Computing. Organization of a hypothetical optimizing compiler. DEPENDENCE ANALYSIS.

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Parallel and Pipeline Programming

Parallel and Pipeline Programming. Super-scalar, pipelined with vector instruction support. Definitions. Super-scalar - multiple integer or floating-point ALUs Pipeline - executes instructions in steps like an assembly line Stall - instruction execution state that delays a pipeline step

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L9 : Low Power DSP

L9 : Low Power DSP. Jun-Dong Cho SungKyunKwan Univ. Dept. of ECE, Vada Lab. http://vada.skku.ac.kr. Low Power DSP. ????? ???? DO-LOOP ?? ????. VSELP Vocoder : 83.4 % 2D 8x8 DCT : 98.3 % LPC computation : 98.0 %. DO-LOOP ? Power Minimization ==> DSP ? Power Minimization.

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SE-292 High Performance Computing Pipelining

SE-292 High Performance Computing Pipelining. IF. IF. IF. IF. ID. ID. ID. ID. EX. EX. EX. EX. MEM. MEM. MEM. MEM. WB. WB. WB. WB. Pipelining. time. i1. i2. i3. i4. Execution time of instruction is still 5 cycles, but throughput is now 1 instruction per cycle

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SE-292 High Performance Computing Pipelining

SE-292 High Performance Computing Pipelining. IF. IF. IF. IF. ID. ID. ID. ID. EX. EX. EX. EX. MEM. MEM. MEM. MEM. WB. WB. WB. WB. Pipelining. time. i1. i2. i3. i4. Execution time of instruction is still 5 cycles, but throughput is now 1 instruction per cycle

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Pipelining and Exploiting Instruction-Level Parallelism (ILP)

Pipelining and Exploiting Instruction-Level Parallelism (ILP). Pipelining and Instruction-Level Parallelism. Definition of basic instruction block Increasing Instruction-Level Parallelism (ILP) & Size of Basic Block: Using Loop Unrolling MIPS Loop Unrolling Example.

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Chapter 4 Advanced Pipelining and Intruction-Level Parallelism

Chapter 4 Advanced Pipelining and Intruction-Level Parallelism. Computer Architecture A Quantitative Approach John L Hennessy & David A Patterson 2 nd Edition , 1996, Morgan Kaufmann. Technique Reduces Loop unrolling => Control stalls Basic pipeline scheduling => RAW stalls

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Static Scheduling Techniques

Static Scheduling Techniques. Local scheduling (within a basic block) Loop unrolling Software pipelining (modulo scheduling) Trace scheduling Predication. 5 cycles/iteration. 14 cycles/4 iterations (3.5 cycles/iteration). Loop Unrolling (1 of 2). Unroll the loop (e.g., four times).

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CS 378 Programming for Performance Single-Thread Performance: Compiler Scheduling for Pipelines

CS 378 Programming for Performance Single-Thread Performance: Compiler Scheduling for Pipelines. Adopted from Siddhartha Chatterjee Spring 2009. Review of Pipelining. Pipelining improves throughput of an instruction sequence but not the latency of an individual instruction

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Multiple Issue Processors: Superscalar and VLIW

Multiple Issue Processors: Superscalar and VLIW. F. D. F. F. F. D. D. D. F. F. D. D. E. E. E. E. E. E. WB. WB. WB. M. M. M. WB. WB. M. M. WB. M. Multiple-Issue Processor Styles. Dynamic multiple-issue processors ( superscalar )

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Lecture 6: Static ILP

Lecture 6: Static ILP. Topics: loop analysis, SW pipelining, predication, speculation (Section 2.2, Appendix G) Please hand in Assignment 1 now Assignment 2 posted; due in a week. Smart Schedule. Loop: L.D F0, 0(R1) stall

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Optimization Of The Quant Component For Speed

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Computer Architecture Principles Dr. Mike Frank

Computer Architecture Principles Dr. Mike Frank. CDA 5155 Summer 2003 Module #26 Software Pipelining. Software Pipelining. Software Pipelining. In hardware pipelining, we overlap the execution of multiple instructions .

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By Sumit Malhotra Computer Science, Florida Tech 767050340 Dr. Charles Fulton

Optimization of Loop Unrolling on dense Vector-matrix multiplication -Parallel Processing. By Sumit Malhotra Computer Science, Florida Tech 767050340 Dr. Charles Fulton. Aim of project.

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