Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor Matthew P. Harrigan,1, ⇤ . Sung,1, 2 ,1 . Satzinger,1 ,1 ,1 ,1 . Bardin,1, 3 ,1 ,1 ,1 . Buckley,1 . Buell,1 ,1 ,1 ,1 ,1 ,1, 4 ,1 ,1 ,1 ,1 ,1 ,1 Brooks Foxen,1 ,1 Marissa Giustina,1 ↵,1 ,1 ,1 […]
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Lead Research Scientist, Financial Risk Quantitative Research, SS&C Algorithmics Adjunct Professor, University of Toronto MIE1624H – Introduction to Data Science and Analytics Lecture 3 – Basic Statistics University of Toronto January 25, 2022 Copyright By PowCoder代写 加微信 powcoder Lecture outline Basic statistics ▪ Before you analyze your data ▪ Sources of uncertainty ▪ Summarizing and
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COMP251: Dynamic programming (2) Jérôme Waldispühl School of Computer Science McGill University Based on (Kleinberg & Tardos, 2005) & Slides by K. Wayne SINGLE SOURCE SHORTEST PATHS Modeling as graphs Input: • Directed graph G = (V, E) • Weight function w : E → R Weight of path p = ⟨v0,v1,…,vk⟩ n ∑w(vk−1,vk )
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COMP251: Divide-and-Conquer (2) Jérôme Waldispühl School of Computer Science McGill University Based on (Kleinberg & Tardos, 2005) & (Cormen et al.,2009) How to determine the running time of a divide-and-conquer algorithm? The Master Theorem Number of recursive calls Recursive definition T(n): execution time on an input of size n. MergeSort:𝑇𝑛 =2%𝑇 !” +𝑛 BinarySearch:𝑇 𝑛
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How a GPU Works Kayvon Fatahalian 15-462 (Fall 2011) Today 1. Review: the graphics pipeline 2. History: a few old GPUs 3. How a modern GPU works (and why it is so fast!) 4. Closer look at a real GPU design – NVIDIA GTX 285 2 Part 1: The graphics pipeline (an abstraction) 3 Vertex
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Introduction to OpenCL Cliff Woolley, NVIDIA Developer Technology Group Welcome to the OpenCL Tutorial! OpenCL Platform Model OpenCL Execution Model Mapping the Execution Model onto the Platform Model Introduction to OpenCL Programming Additional Information and Resources OpenCL is a trademark of Apple, Inc. Design Goals of OpenCL Use all computational resources in the system
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CS 341, Fall 2020 A. Lubiw, A. Storjohann ASSIGNMENT 9 DUE: Wednesday November 25, 5 PM. DO NOT COPY. ACKNOWLEDGE YOUR SOURCES. Please read http://www.student.cs.uwaterloo.ca/~cs341 for general instructions and policies. Exercises. The following exercises are for practice only. You may ask about them in office hours. Do not hand them in. 1. Let X, Y,
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COMP251: Dynamic programming (1) Jérôme Waldispühl School of Computer Science McGill University Based on (Cormen et al., 2002) & (Kleinberg & Tardos, 2005) Algorithms paradigms • Greedy: o Build up a solution incrementally. o Iteratively decompose and reduce the size of the problem. o Top-down approach. • Dynamic programming: o Solve all possible sub-problems. o
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COMP251: Divide-and-Conquer (1) Jérôme Waldispühl School of Computer Science McGill University Based on (Kleinberg & Tardos, 2005) and slides by K. Wayne & Snoeyink Divide and Conquer • Recursiveinstructure – Divide the problem into sub-problems that are similar to the original but smaller in size – Conquer the sub-problems by solving them recursively. If they
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An Introduction to Modern GPU Architecture Ashu Rege Director of Developer Technology Agenda • Evolution of GPUs • Computing Revolution • Stream Processing • Architecture details of modern GPUs Evolution of GPUs Evolution of GPUs (1995-1999) • 1995 – NV1 • 1997 – Riva 128 (NV3), DX3 • 1998 – Riva TNT (NV4), DX5 •