Optimizations PowerPoint Presentations - PPT

Performance Optimizations
Performance Optimizations - presentation

luanne-sto

in . Dyninst. Andrew . Bernat. , Matthew Legendre. Instrumentation is Complicated. User perspective: . “Insert some new code here, here, and here.”. Dyninst’s. perspective:. Relocation. – Move code to make space for instrumentation.

Automatic Generation of Inputs of Death
Automatic Generation of Inputs of Death - presentation

karlyn-boh

and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs.

Weakest Precondition Synthesis for Compiler Optimizations
Weakest Precondition Synthesis for Compiler Optimizations - presentation

cheryl-pis

Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities.

Zhelong Pan [1] This presentation as
Zhelong Pan [1] This presentation as - presentation

stefany-ba

.pptx: http://. tinyurl.com/6y7gy8x (or scan QR code). The . paper: http. ://dl.acm.org/citation.cfm?id=1122414. [1] . http://. www.nic.uoregon.edu/iwomp2005/IWOMP_Photos_Day1/IWOMP_Photos-Images/7.jpg.

7. Optimization
7. Optimization - presentation

karlyn-boh

Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations.

Yogi  –  Part 3 Engineering the
Yogi – Part 3 Engineering the - presentation

aaron

tool . and parallelization. do {. //get the write lock. . KeAcquireSpinLock. (&. devExt. ->. writeListLock. );. . nPacketsOld. = . nPackets. ; . request = . devExt. ->. WriteListHeadVa.

Memory Optimizations
Memory Optimizations - presentation

celsa-spra

Research at UNT. . . Krishna . Kavi. Professor. Director of NSF Industry/University Cooperative Center . for Net-Centric Software and Systems (Net-Centric IUCRC). Computer Science and Engineering. The University of North Texas.

Generating Compiler Optimizations from Proofs
Generating Compiler Optimizations from Proofs - presentation

lois-ondre

Ross Tate. Michael Stepp. Sorin Lerner. University of California, San Diego. Optimizing by Hand. Original. for (i = 0; i < 50; i ). . for (j = 0; j < 50; j ). . img[i*50 j] = f(i, j);.

NUMA I/O Optimizations
NUMA I/O Optimizations - presentation

conchita-m

Bruce Worthington. Software Development Manager. Microsoft Corporation. Key Takeaways. Be a leader in advancing 64-bit computing. Adopt best practices and new tools. Let’s partner on new hardware directions.

Generating Compiler Optimizations from Proofs
Generating Compiler Optimizations from Proofs - presentation

tatyana-ad

Ross Tate. Michael Stepp. Sorin Lerner. University of California, San Diego. Optimizing by Hand. Original. for (i = 0; i < 50; i ). . for (j = 0; j < 50; j ). . img[i*50 j] = f(i, j);.

Generating Compiler Optimizations from Proofs
Generating Compiler Optimizations from Proofs - presentation

lindy-duni

Ross Tate. Michael Stepp. Sorin Lerner. University of California, San Diego. Optimizing by Hand. Original. for (i = 0; i < 50; i ). . for (j = 0; j < 50; j ). . img[i*50 j] = f(i, j);.

Parallel Programming in Chess Simulations
Parallel Programming in Chess Simulations - presentation

pasty-tole

Tyler Patton. Discussion:. Background. Sequential Optimizations. Parallelization of chess. Backgroun. d. : What is Chess?. Strategic 2 player game. 64 tiles. 16 pieces per player. Objective to capture the.

RDMA optimizations on top of 100 Gbps Ethernet for the upgraded data acquisition system of LHCb
RDMA optimizations on top of 100 Gbps Ethernet for the upgra - presentation

myesha-tic

Balazs Voneki. CERN/EP/LHCb Online group. TIPP2017, Beijing. 23.05.2017. LHCb Upgrade. Early results of RDMA optimizations on top of 100 Gbps . Ethernet, TIPP2017, Beijing, 23.05.2017 . – Balazs Voneki.

Machine Learning in Compiler Optimization
Machine Learning in Compiler Optimization - presentation

myesha-tic

By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization .

Proving Optimizations Correct
Proving Optimizations Correct - presentation

alexa-sche

using Parameterized Program Equivalence. University of California, San Diego. Sudipta. . Kundu. Zachary . Tatlock. Sorin. Lerner. Compiler Correctness. Difficult to develop reliable compilers:. large, complex programs.

Tuning Compiler Optimizations for Simultaneous Multithreading Jack L
Tuning Compiler Optimizations for Simultaneous Multithreadin - pdf

conchita-m

Lo Susan J Eggers Henry M Levy Sujay S Parekh and Dean M Tullsen Dept of Computer Science and Engineering Box 352350 University of Washington Seattle WA 981952350 jlo sparekh eggers levycswashingtonedu Dept of Computer Science and Engineering Univer

Runtime organizationImprovements to stack machine code generationOur c
Runtime organizationImprovements to stack machine code gener - pdf

celsa-spra

1 When to perform optimizations: Machine independent: Too high levelOn assembly language: Exposes optimization opportunities: Machine dependent: Must re-implement optimizations when re-targettingOn an

Generating Compiler Optimizations from Proofs Ross Tate Michael Stepp Sorin Lerner University of California San Diego rtatemstepplerner cs
Generating Compiler Optimizations from Proofs Ross Tate Mich - pdf

lois-ondre

ucsdedu Abstract We present an automated technique for generating compiler op timizations from examples of concrete programs before and after improvements have been made to them The key technical insight of our technique is that a proof of equivalenc

Data Locality Optimizations
Data Locality Optimizations - presentation

tatyana-ad

for NUMA Systems. Zoltán Majó. Thomas R. Gross. Department of Computer Science. ETH Zurich, Switzerland. 1. A Library for. Portable and Composable. NUMA. . systems. Key to good performance: data locality.

lib flame optimizations
lib flame optimizations - presentation

trish-goza

with. . BLIS. Kiran . varaganti. 19 September 2016. Contents. Introduction. libFLAME. Baseline Performance. Cholesky. QR. LU factorization. Analysis. Optimizations . Summary. Introduction. AMD provides high-performance computing libraries for various verticals:.

Static Optimizations
Static Optimizations - presentation

briana-ran

(aka: the complier). Dr. Mark . Brehob. EECS 470. Announcements. Milestone 2 due . t. oday. 1 page memo, don’t spend more than . 30 minutes . (sum of everyone) on it. . Not graded. Meet on Friday. Most already signed up.

1 CS 260 Winter 2014
1 CS 260 Winter 2014 - presentation

conchita-m

Eamonn Keogh’s Presentation of. Thanawin. . Rakthanmanon. , . Bilson. . Campana. , Abdullah . Mueen. , Gustavo Batista, Brandon Westover, . Qiang. Zhu, . Jesin. . Zakaria. , Eamonn Keogh (2012). .

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors
Accelerating Applications with Pattern-specific Optimization - presentation

olivia-mor

Linchuan. . Chen. Advisor: Dr. . Gagan. Agrawal. Motivation - Platforms. Accelerators are Evolving. General Purpose Graphics Processing Units (GPGPU). Extreme-scale, cost-effective, power efficient.

Memory Optimizations for Graphics Processing Units
Memory Optimizations for Graphics Processing Units - presentation

tatyana-ad

The material in these slides has been taken from the NVIDIA manuals (Best Practices Guide & Optimizing Matrix Transpose in CUDA), and from a paper by . Ryoo. et al [Ryoo12]. See "A bit of History" in the last slide.

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