COMP3221: Distributed Systems Introduction Dr Nguyen Tran School of Computer Science The University of Sydney Page 1 Outline – Why this course ? – What this course is about? – Definitions, Examples and Challenges of Distributed Systems – Course Logistics – Lectures/Tutorials – Assessments – Expectation and Outcomes – Resources The University of Sydney Page […]
18-646 – How to Write Fast Code II 1 Carnegie Mellon University Important Dates Mini-Project 1 – Important Dates: Mon, March 15th @ 11:59PM EST Thu, March 18th @ 3:00PM EST Term Project – Important Dates: Fri, March 12th @ 11:59PM EST Tue, May 4th Fri, May 14th
18-646 – How to Write Fast Code II 1 Carnegie Mellon University Mini-Project One Mini-Project One Due Monday, 03/08 @ 11:59PM Mini-Project One Review Thursday, 03/11 One person in your team should add the other team members for your final code submission 18-646 – How to Write Fast Code II 2 Mini-Project
18-646 – How to Write Fast Code II 1 Carnegie Mellon University Outline Compilation with CUDA Synchronization Data Parallel Algorithms – Map, Reduce, and Scan Compositions of Data Parallel Algorithms Compact Find Unique Building a Flag Array 18-646 – How to Write Fast Code II 2 18-646 –
18-646 – How to Write Fast Code II 7 Mini-Project 2 The goal of this project is to use your understanding of parallel computing resources in a multicore microprocessor to optimize two fully functional applications. Mini-Project 2 Due Monday, 4/12 @ 11:59PM EST Mini-Project 2 Review Thursday, 4/15 @ 6:00PM EST Canvas:
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All of Statistics: A Concise Course in Statistical Inference Brief Contents 1. Introduction…………………………………………………………11 Part I Probability 2. Probability……………………………………………………………21 3. Random Variables…………………………………………………..37 4. Expectation…………………………………………………………..69 5. Equalities…………………………………………………………….85 6. Convergence of Random Variables…………………………………89 Part II Statistical Inference 7. Models, Statistical Inference and Learning………………………105 8. Estimating the CDF and Statistical Functionals…………………117 9. The Bootstrap………………………………………………………129 10. Parametric Inference……………………………………………..145 11. Hypothesis
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License What is Vectorization/SIMD and Why do We Care? 2 Performance! Many hardware architectures today, both CPU and GPU, allow you to perform arithmetic operations on multiple array elements simultaneously. (Thus the label, “Single Instruction Multiple Data”.) Copyright By PowCoder代写 加微信 powcoder We care
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Computer Graphics gpu101.pptx Copyright By PowCoder代写 加微信 powcoder mjb – March 16, 2022 How Have You Been Able to Gain Access to GPU Power? There have been three ways: 1. Write a graphics display program (≥ 1985) 2. Write an application that looks
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License How Have You Been Able to Gain Access to GPU Power? There have been three ways: 1. Write a graphics display program (≥ 1985) Copyright By PowCoder代写 加微信 powcoder 2. Write an application that looks like a graphics display program, but uses the
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License The OSU College of Engineering DGX System for Advanced GPU Computing Computer Graphics Copyright By PowCoder代写 加微信 powcoder dgx_system.pptx mjb – March 10, 2022 OSU’s College of Engineering has six Nvidia DGX-2 systems Each DGX server: • Has 16 NVidia Tesla V100 GPUs
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