'Instruction scheduling' diaporamas de présentation

<number>

CS3014: Concurrent Systems. Static & Dynamic Instruction Scheduling. Slides originally developed by Drew Hilton, Amir Roth, Milo Martin and Joe Devietti at University of Pennsylvania. <number>. Instruction Scheduling & Limitations. <number>. Instruction Scheduling. <number>.

981 views • 98 slides

Advanced Microarchitecture

Advanced Microarchitecture. Prof. Mikko H. Lipasti University of Wisconsin-Madison Lecture notes based on notes by Ilhyun Kim Updated by Mikko Lipasti. Outline. Instruction scheduling overview Scheduling atomicity Speculative scheduling Scheduling recovery

621 views • 50 slides

Efficient Partitioning of Fragment Shaders for Multiple-Output Hardware

Efficient Partitioning of Fragment Shaders for Multiple-Output Hardware. Tim Foley Mike Houston Pat Hanrahan Computer Graphics Lab Stanford University. Motivation. GPU Programming Interactive shading Offline rendering Computation physical simulations numerical methods

555 views • 43 slides

Instruction Selection and Scheduling

Instruction Selection and Scheduling. Front End. Middle End. Back End. Infrastructure. The Problem. Writing a compiler is a lot of work Would like to reuse components whenever possible Would like to automate construction of components Front end construction is largely automated

765 views • 31 slides

MacSim Simulator

MacSim Simulator. HPArch Research Group. MacSim Tutorial. Part 2. Overview of MacSim Introduction For b lack box approach users Part 3: Details of MacSim For computer architecture researchers Part 4. MacSim -SST case studies Ocelot-MacSim case studies Research using Ocelot

690 views • 30 slides

What Programming Language/Compiler Researchers should Know about Computer Architecture

What Programming Language/Compiler Researchers should Know about Computer Architecture. Lizy Kurian John Department of Electrical and Computer Engineering The University of Texas at Austin. Somebody once said.

425 views • 30 slides

Introduction to Instruction Scheduling

466 views • 29 slides

Optimization software for apeNEXT Max Lukyanov, 12.07.05

Optimization software for apeNEXT Max Lukyanov, 12.07.05. apeNEXT : a VLIW architecture Optimization basics Software optimizer for apeNEXT Current work. Generic Compiler Architecture. Front-end: Source Code ? Intermediate Representation (IR) Optimizer: IR ? IR

443 views • 29 slides

Optimizing Compilers CISC 673 Spring 2009 Instruction Scheduling

Optimizing Compilers CISC 673 Spring 2009 Instruction Scheduling. John Cavazos University of Delaware. Instruction Scheduling. Reordering instructions to improve performance Takes into account anticipated latencies Machine-specific Performed late in optimization pass

441 views • 25 slides

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

360 views • 25 slides

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

307 views • 25 slides

Optimizing Compilers CISC 673 Spring 2009 Instruction Scheduling

Optimizing Compilers CISC 673 Spring 2009 Instruction Scheduling. John Cavazos University of Delaware. Instruction Scheduling. Reordering instructions to improve performance Takes into account anticipated latencies Machine-specific Performed late in optimization pass

366 views • 25 slides

Be-Nice Scheduling for embedded SMT processors

Be-Nice Scheduling for embedded SMT processors. Handong Ye. Apr 6 th , 2008 Boston. Be-Nice Scheduling. ITS (Inter-Thread Stall) Introduction Be-Nice Scheduling Some experimental results. Be-Nice Scheduling. ITS Introduction ITS in Out-Of-Order processor ITS in In-Order processor

342 views • 21 slides

Steganography for Executables and Code Transformation Signatures

Steganography for Executables and Code Transformation Signatures. Bertrand Anckaert, Bjorn De Sutter, Dominique Chanet and Koen De Bosschere. Alice. Bob. Wendy. Embedder. Extractor. Problem. Location of the Secret Message. Media human senses redundant bits. Executables processors

356 views • 19 slides

POSA: Perl Objects * for DNA Sequencing Data Analysis

POSA: Perl Objects * for DNA Sequencing Data Analysis. ??? ???. Background. Capillary Electrophoresis. Background. Pipeline : Instruction scheduling on Intel Pentium 4. Implementation. Results:Functionality. to design and implement automated sequencing data analysis .

328 views • 17 slides

Optimizing the Performance of Streaming Numerical Kernels on the IBM Blue Gene/P PowerPC 450

Optimizing the Performance of Streaming Numerical Kernels on the IBM Blue Gene/P PowerPC 450. Tareq Malas Advisors: Prof. David Keyes, Dr. Aron Ahmadia Collaborators: Jed Brown, Dr. John Gunnels. King Abdullah University of Science and Technology November 2011. Motivation.

240 views • 16 slides

Machine Learning in Compiler Optimization

Machine Learning in Compiler Optimization. By Namita Dave. Overview. What are compiler optimizations? Challenges with optimizations Current Solutions Machine learning techniques Structure of Adaptive compilers. Introduction.

430 views • 15 slides

Code generation

Code generation. Our book's target machine (appendix A): opcode source1 , source2 , destination add r1, r2, r3 addI r1, c, r2 loadI c, r2 load r1, r2 loadAI r1, c, r2 // r2 := *(r1+c) loadAO r1, r2, r3 i2i r1, r2 // r2 := r1 (for integers)

370 views • 13 slides

Bypass Aware Instruction Scheduling for Register File Power Reduction

Bypass Aware Instruction Scheduling for Register File Power Reduction. Sanghyun Park , Aviral Shrivastava Nikil Dutt , Alex Nicolau Yunheung Paek Eugene Earlie

201 views • 13 slides

Review for the Course

Review for the Course. Iterative Dataflow Analysis. Reaching definitions Live variables. Iterative Dataflow Analysis. Foundations of Data Flow Analysis . Correctness Equations will be satisfied when the program terminates. Is this the solution that we want?

247 views • 12 slides