Advance Computing for Electrical Engineers An Introduction - PowerPoint PPT Presentation

Advance Computing for Electrical Engineers An Introduction Virendra Singh Associate Professor Computer Architecture and Dependable Systems Lab Department of Electrical Engineering Indian Institute of Technology Bombay http://www.ee.iitb.ac.in/~viren/ E-mail: viren@ee.iitb.ac.in EE-717/453:Advance Computing for Electrical Engineers Lecture0

EE 717/453 • Pre-requisite ● Knowledge of programming (Any language C/C+ +/Java, ..) • Attendance – Not mandatory (except first week) – Submission of assignments (firm deadlines) • Timing – M Th (Slot 13): 6:30 pm to 8:00 pm – Office Hours: Wed 4 pm to 6 pm 18 July 2013 EE-717/453@IITB 2

Course Outline • Data Structures and Algorithm – About 6 - 8 classes – Cover basic data structures (Stack, Queue, Linked List, Tree, Graph, Hashing – Basic Algorithm Design • Computer Architecture and Compiler Design – About 2-4 lectures – Basic architectures and compiler design phases 18 July 2013 EE-717/453@IITB 3

Course Outline • Operating System – About 6-8 classes – Basics of operating system – PintOS – Implementation of basic OS functions (Threads, user program, virtual memory, file system)  Parallel Programming – About 6 – 8 lectures – Basics of parallel programming – CUDA 18 July 2013 EE-717/453@IITB 4

Course Outline • Miscellaneous – About 1-2 lectures • Summary 18 July 2013 EE-717/453@IITB 5

Books • Data Structures, Algorithms, and its applications in C++ – Sartaj Sahni • Introduction to Algorithms – Cormen et al. • Modern Operating Systems – Tanenbaum • Introduction to parallel programming – Thomas Rauber 18 July 2013 EE-717/453@IITB 6

Evaluation • Mid semester Examination – 15 Marks • Final Examination – 30 Marks • Assignments – 40 Marks • Continuous Evaluation – 15 Marks 18 July 2013 EE-717/453@IITB 7

Grades Absolute • AA: > 94 • AB : 85 - 94 • BB : 75 - 84 • BC : 65 – 74 • CC : 55 – 74 • CD : 45 – 54 • DD : 40 – 45 • FR: < 40 18 July 2013 EE-717/453@IITB 8

Problem Solving: Main Steps 1. Problem definition 2. Algorithm design/ Algorithm specification 3. Algorithm analysis 4. Implementation 5. Testing 6. [Maintenance] 18 July 2013 EE-717/453@IITB 9

Running Program on Processor Time Processor Performance = --------------- Program Instruction Tim = X X s Cycles e Instructio Cycl n Program e (code (cycle size) (CPI) time) Architecture --> Implementation --> Realization mpiler Designer Processor Designer Chip Designer EE-717/453@IITB 10 18 July

From Source to Executable Machine memory source object program modules other foo.c foo.o programs main main() load ... sub1 ? module sub1() Compiler data a.out data main (system sub1 main calls) data sub1 (Run Time) static printf data library exit printf Loader libc.a data exit printf data Linkage scanf other Editor gets ... fopen ... exit data “Load time” ... kernel Dynamic library case not shown 18 July 2013 EE-717/453@IITB 11

Problem Definition • What is the task to be accomplished? – Calculate the average of the grades for a given student – Understand the talks given out by politicians and translate them in Chinese  What are the time / space / speed performance requirements ? 18 July 2013 EE-717/453@IITB 12

Algorithm Design/Specifications • Algorithm: Finite set of instructions that, if followed, accomplishes a particular task. • Describe: in natural language / pseudo-code / diagrams / etc • Criteria to follow: – Input: Zero or more quantities (externally produced) – Output: One or more quantities – Definiteness: Clarity, precision of each instruction – Finiteness: The algorithm has to stop after a finite (may be very large) number of steps – Effectiveness: Each instruction has to be basic enough and feasible ● Understand speech ● Translate to Chinese 18 July 2013 EE-717/453@IITB 13

Algorithm Design/Specifications • Algorithm: Finite set of instructions that, if followed, accomplishes a particular task. • Describe: in natural language / pseudo-code / diagrams / etc • Criteria to follow: – Input: Zero or more quantities (externally produced) – Output: One or more quantities – Definiteness: Clarity, precision of each instruction – Finiteness: The algorithm has to stop after a finite (may be very large) number of steps – Effectiveness: Each instruction has to be basic enough and feasible ● Understand speech ● Translate to Chinese 18 July 2013 EE-717/453@IITB 14

Implementation, Testing, and Maintenance • Implementation – Decide on the programming language to use ● C,C++,Lisp,Java,Perl,Prolog,Assembly etc. – Write clean, well documented code • Test, test, test • Integrate feedback from users, fix bugs, ensure compatibility across different versions  Maintenance 18 July 2013 EE-717/453@IITB 15

Algorithm Analysis • Space complexity – How much space is required  Time complexity – How much time does it take to run the algorithm  Often, we deal with estimates! 18 July 2013 EE-717/453@IITB 16

Space Complexity (1/3) • Space complexity = The amount of memory required by an algorithm to run to completion – [Core dumps = the most often encountered cause is “memory leaks” – the amount of memory required larger than the memory available on a given system]  Some algorithms may be more efficient if data completely loaded into memory – Need to look also at system limitations – E.g. Classify 10 GB of text in various categories [politics, tourism, sport, natural disasters, etc.] – can I afford to load the entire collection? 18 July 2013 EE-717/453@IITB 17

Space complexity (2/3 ) • Fixed part: The size required to store certain data/variables, that is independent of the size of the problem: – e.g. name of the data collection – same size for classifying 2GB or 1MB of texts • Variable part: Space needed by variables, whose size is dependent on the size of the problem: – e.g. actual text – load 2GB of text VS. load 1MB of text 18 July 2013 EE-717/453@IITB 18

Space Complexity (3/3) • S(P) = c + S(instance characteristics) • Example: void float sum (float* a, int n){ float s = 0; for (int i = 0; I < n; i++){ s += a[i] } return s; } • Space? One word for n, one for a [passed by reference], one for i  Constant space 18 July 2013 EE-717/453@IITB 19

Time Complexity • Often more important than space complexity – space available (for computer programs!) tends to be larger and larger – time is still a problem for all of us  3-4 GHz multi-core processors are in the market – still researchers estimate that the computation of various transformations for 1 single DNA chain for one single protein on 10 GHz computer would take about 1 year to run to completion  Algorithms running time is an important 18 July 2013 EE-717/453@IITB 20 issue

Running Time • Problem: Prefix average – Given an array X – Compute the array A such that A[i] is the average of elements X[0] ... X[i], for i=0..n-1  Solution 1: – At each step i, compute the element X[i] by traversing the array A and determining the sum of its elements, respectively the average  Solution 2: – At each step i update a sum of the elements in the array A – Compute the element X[i] as sum/I  Big question: Which solution to choose? 18 July 2013 EE-717/453@IITB 21

Running Time Worst case Average case Best case • Suppose the program includes an if-then statement that may execute or not  variable running time • Typically algorithms are measured by their worst case 18 July 2013 EE-717/453@IITB 22

Experimental Approach • Write a program that implements the algorithm • Run the program with data sets of varying size. • Determine the actual running time using a system call to measure time (e.g. system (date) ); • Problems? 18 July 2013 EE-717/453@IITB 23

Experimental Approach • It is necessary to implement and test the algorithm in order to determine its running time • Experiments can be done only on a limited set of inputs, and may not be indicative of the running time of the other inputs • The same hardware and software should be used in order to compare two algorithms – condition very hard to achieve! 18 July 2013 EE-717/453@IITB 24

Theoretical Approach • Based on high-level description of the algorithms, rather than language dependent implementation • Makes possible an evaluation of the algorithms that is independent of the hardware and software environment  Generality 18 July 2013 EE-717/453@IITB 25

Algorithm Description • How to describe algorithms independent of a programming language • Pseudo-Code = a description of an algorithm that more structured than usual prose but – less formal than a programming language – • (Or diagram) • Example: find the maximum element of an array Algorithm ArrayMax (A, n): Input: An array A storing n intergers Output: The maximum element in A currentMax  A[0] for I  1 to n-1 do if currentMax < A[i] then currentMax  A[i] return currentMax 18 July 2013 EE-717/453@IITB 26