PPT-Performance Problems You Can Fix: A Dynamic Analysis of

Memoization Opportunities Luca Della Toffola ETH Zurich Michael Pradel TU Darmstadt Thomas R Gross ETH Zurich October 30 th 2015 OOPSLA15 1 MemoizeIt 2 Dynamic analysis. In most cases emphasis on one of these parameters adversely impacts the other two For example the wide resolution bandwidths R W used to achieve fast measurement speeds result in higher noise levels and potentially reduce dynamic range Increasing th Our result is modular 1 We describe a carefullychosen dynamic version of set disjointness the multiphase problem and conjecture that it requires 84861 time per operation All our lower bounds follow by easy reduction 2 We reduce 3SUM to the multipha Algorithmic Strategies. Divide and Conquer. Greedy Approach. Dynamic Programming. Algorithm Strategies: Key Differences . Divide and Conquer. Divide a problem into “disjoint” sub-problems. Solve the sub-problems first (bottom-up solution). . Dynamic Programming. CSE 680. Prof. Roger Crawfis. Fibonacci Numbers. . Computing the n. th. Fibonacci number recursively:. F(n) = F(n-1) + F(n-2). F(0) = 0. F(1) = 1. Top-down approach. . F. Tensor Contractions. David . Ozog. *, Jeff R. Hammond. †. , James . Dinan. †. , . Pavan. . Balaji. †. , Sameer . Shende. *, Allen . Malony. * . *University of Oregon . †. Argonne National Laboratory. Paper by John Rink, . Neta. Spiro and Nicolas Gold. Presented by Elaine Chew. QMUL: ELE021/ELED021/ELEM021. 12 March 2012. Gestures. music’s gestural properties are neither captured by nor fully encoded within musical notation, but instead require the agency of performance to achieve their full realization. (but might have been afraid to ask). IEEE S&P 2010. Overview. Two Main Contributions. Precisely describe the algorithms for dynamic taint analysis and forward symbolic execution as extensions to the run-time semantics of a general language. David Devecsery,. Peter M. Chen, Satish Narayanasamy, Jason Flinn. University of Michigan. Motivation – Problem . Dynamic analysis is essential. Memory errors. Buffer overflow, use-after-free, double free, memory leak…. Gift Nyikayaramba. 30 September 2014. Overview. . Key design issues . Code region selection. DVFS decision. Code insertion/transformation. . Platform. Intel PIN . variant. . Deployment environment. . How Unthinkable?. Army Operational Knowledge Management. 21 October 2009. Dr. Mark E. Nissen . Center for Edge Power. US Naval Postgraduate School. http://www.nps.edu/Academics/Centers/CEP/. 2. KM Background. 1. Lecture Content. Fibonacci Numbers Revisited. Dynamic Programming. Examples. Homework. 2. 3. Fibonacci Numbers Revisited. Calculating the n-. th. Fibonacci Number with recursion has proved to be . INTRODUCTION TO NUMERICAL MODELING IN GEOTECHNICAL ENGINEERING WITH EMPHASIS ON FLAC MODELING www.zamiran.net By Siavash Zamiran, Ph.D., P.E. Geotechnical Engineer, Marino Engineering Associates, Inc. Basic Algorithm Design Techniques. Divide and conquer. Dynamic Programming. Greedy. Common Theme: To solve a large, complicated problem, break it into many smaller sub-problems.. Dynamic Programming. CSE 3318 – Algorithms and Data Structures. University of Texas at Arlington. Alexandra . Stefan. 1. 4/7/2022. Approaches for solving DP Problems. Greedy. - typically not optimal solution . (for DP-type problems).