Basics of Parallelization CMPSC 450 Why parallelize? • Not enough memory on single system. • More computers scales linearly, easy to predict fix. • Execution time too long on single core serial implementation. • How do we know how much faster more processors will run my code? CMPSC 450 Amdahl’s Law • Slatency is the […]
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CMPSC 450 Concurrent Scientific Programming Dense matrix multiplication CMPSC 450 Parallel dense matrix-matrix multiplication algorithms • SUMMA • Cannon’s algorithm CMPSC 450 2 SUMMA: Scalable Universal Matrix Multiply Algorithm • Each processor owns 2D sub-blocks of matrices A and B, and computes a submatrix of C • Assumes a 2D logical processor topology • Algorithm
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CUDA CMPSC 450 What is CUDA • Compute Unified Device Architecture • An extension of the C programming language created by nVidia. • Enables GPUs to execute programs written in C in an integrated host (CPU) + device (GPU) app C program • Execute “kernels” as a SIMT program • A dedicated hardware solution CMPSC
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THIS PAPER MUST NOT BE Class Test (optimisation), S2 2019 REMOVED FROM THE EXAMINATION ROOM Internal Students Only THE UNIVERSITY OF QUEENSLAND School of Information Technology & Electrical Engineering Class Test (Optimisation), S2 2019 ENGG7302 Advanced Computational Techniques in Engineering (MEngSc) CLOSED BOOK TIME: NINETY minutes for working FIVE minutes for perusal before examination begins
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Distributed-memory Algorithm Design and Analysis slides CMPSC 450 Distributed memory platforms …… Memory . Memory Memory Memory NI NI NI NI CCCC Communication network CMPSC 450 Distributed memory platforms • Explicit communication and synchronization between processors • Message passing • Process/task: software abstraction • No shared resources, need to be explicit about where data resides
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CMPSC 450 definitions CMPSC 450 What is a ‘parallel computer’? • A parallel computer consists of a number of tightly-coupled compute elements that cooperatively solve a problem. • Example of `tight coupling’: shared caches, shared main memory, shared led system, high-speed access to data, high-speed network connecting compute nodes. • Cooperatively solving implies manual or
Prefix Sums and Pointer Jumping CMPSC 450 Doubling • A processing technique in which accesses or actions are governed by increasing powers or 2 • That is, processing proceeds by 1, 2, 4, 8, 16, etc., doubling on each iteration CMPSC 450 2 Prefix Sum by Doubling • Overview • 1. Each a(i) is added
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CMPSC 450 Concurrent Scientific Programming Introduction CMPSC 450 Welcome to the class! • Class meets MWF 8-8:50AM on Zoom • Office hours • Tuesdays and Thursdays 8PM – 9PM. On Zoom. • By appointment • Email: use Canvas • About me: • Master of Engineering, CSE, Penn State 2001 • 20 years industry experience •
CMPSC 450 Concurrent Scientific Programming Locality and Parallelism in Simulations Spring 2016 Kamesh Madduri Sources of Parallelism and Locality in Simulations • Parallelism and locality are both critical to performance – Data movement is expensive • Real-world problems have parallelism and locality – Objects often depend more on nearby than distant objects – Dependence on
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Class Test (optimisation), S1 2020 Internal Students Only STUDENT NAME: ENGG7302 Advanced Computational Techniques in Engineering STUDENT NUMBER: THE UNIVERSITY OF QUEENSLAND School of Information Technology & Electrical Engineering Class Test (Optimisation), S1 2020 ENGG7302 Advanced Computational Techniques in Engineering (MEngSc) OPEN BOOK TIME: NINETY minutes for working ANSWER ALL QUESTIONS IN THIS FILE Page
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