Database Design True / False Multiple choice Copyright By PowCoder代写 加微信 powcoder Fill in the blanks Problem solving questions What to study Reading assignments (Chapter numbers vary based on edition) Additional Videos and Lecture Recordings Homework assignments and solutions Midterm exam Practice review questions and exercises at the end of chapters Project and Checkpoint documents […]
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School of Computing and Information Systems The University of Melbourne SWEN90004 Modelling Complex Software Systems Concurrency Workshop 2 Java monitors and semaphores The focus of this week¡¯s workshop is to explore some more of Java¡¯s concurrent features and experiment with concurrent processes. Copyright By PowCoder代写 加微信 powcoder To begin, download the workshop code from the
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Logic Programming March 17, 2021 COM S 342 Principles of Programming Languages @ Iowa State University 1 COM S 342 Principles of Programming Languages @ Iowa State University 2 ?- loves(X, tom). mary ?- loves(mary, Y). tom ?- loves(mary, jane). fCaOlMseS 342 Principles of Programming Languages @ Iowa State University 3 What is Logic Programming
INFO20003 Database Systems Dr Renata Borovica-Gajic Lecture 14 Query Optimization Part II Copyright By PowCoder代写 加微信 powcoder INFO20003 Database Systems © University of Melbourne Remember this? Components of a DBMS Query processing module Parser/ Compiler Optimizer Executor Plan enumeration Concurrency control module Transaction mgr. Crash recovery module Concurrency control module Storage module File and access
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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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Parallel Memory Models CMPSC 450 Taxonomy of Parallel Computing Paradigms • SIMD – Single Instruction Multiple Data – A single instruction pipeline applied to multiple compute elements. Ex: Vector Processors, GPU Processing, MMX, SSE, AVX instruction sets. • MIMD – Multiple Instruction Multiple Data – Multiple instruction pipelines are working on multiple data streams concurrently.
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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 CMPSC 450 Definition: The all-prefix-sums operation takes a binary associative operator , and an ordered set of n elements and returns the ordered set [a0, a1, …, an−1], [a0,(a0 a1), …,(a0 a1 … an−1)]. CMPSC 450 Serial example • Make binary-associative operator ‘+’ b[0] = a[0]; for (i = 1; i < n;
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Recall: Multithreaded Stack Example • Consider the following code blocks: proc A() { proc B() { Copyright By PowCoder代写 加微信 powcoder while(TRUE) { yield(); } • Suppose we have 2 threads: – Threads S and T run_new_thread run_new_thread Thread S’s switch returns to Thread T’s (and vice versa) Joseph & Kubiatowicz CS162 © UCB Spring
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Recall: Atomic Read-Modify-Write • test&set (&address) { result = M[address]; M[address] = 1; return result; Copyright By PowCoder代写 加微信 powcoder • swap (&address, register) { temp = M[address]; M[address] = register; register = temp; /* most architectures */ // return result from “address” and // set value at “address” to 1 // swap register’s value
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