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Number of transistors per chip continues to follow Moore’s Law; but the transistors are going into multiple cores, bigger cache, bridge technology, etc.

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Presentation on theme: "Number of transistors per chip continues to follow Moore’s Law; but the transistors are going into multiple cores, bigger cache, bridge technology, etc."— Presentation transcript:

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2 Number of transistors per chip continues to follow Moore’s Law; but the transistors are going into multiple cores, bigger cache, bridge technology, etc But in 2003, clock speed hit a wall- Where are the 32GHz processors?

3  Parallel & Distributed Computing (PD) is now its own Knowledge Area (KA).  Architecture and Organization (AR) is 16 Tier 2 core hours  PD is 5 Tier 1 hours, and 10 Tier 2 hours.  PD is seen as critical to many other KA’s, and can properly be placed there; Strawman seems to indicate a stand- alone course is fastest way to get up and running now.

4   Using a Bloom’s taxonomy of Know- Comprehend-Apply this group proposes some 90 hours of instruction in PD  Only about 15 hours at the “Apply” level  They see 5 core courses involved, and a larger list of upper division electives › CS1, CS2, Architecture, DataStruc/Algor, Discrete

5  ACM SIGCSE 2009 (Chattanooga)  Dr. Michael Wrinn (Intel Senior Course Architect) : › Intel is not going to be making any single core processors. The world has already gone parallel and you better start teaching parallel computing to your students.

6  The Free Lunch is over  Professional societies have served notice that our curricula must include PD.  The world in which our students will live their careers will be a parallel world. It is already a multicore world.  It must begin as early as CS1/CS2

7  Intel microgrant program to develop PD curricular material  ACM SIGCSE “Nifty Programming Assignments” › Nick Parlante at nifty.stanford.edu  If we want to target CS1/CS2, why not begin with those assignments the SIGCSE community has already endorsed as nifty?  My thanks to the 2011 Nifty Authors for their gracious permission to use their work › DO NOT MAKE SOURCE CODE AVAILABLE

8  Take students’ serial solution to a problem and add parallelism by “grabbing the low-hanging fruit”.  We use C++ in the MS Visual Studio.NET IDE  The Intel Parallel Studio, especially the Parallel Advisor, is a natural choice. › Parallel Studio is also available for Unix as a stand-alone product, both as command line & GUI

9  Trapezoidal Numerical Integration › Not a “nifty”, but is embarrassingly parallel  BMP puzzles: who done it? › David Malan, Harvard University  Book Recommendations: look out Netflix › Michele Craig, University of Toronto  Hamming Codes: old school error correction › Stuart Hansen, University of Wisconsin-Parkside  Evil Hangman: cheating is a strategy! › Keith Schwarz, Stanford University

10  A classic Numerical Methods technique  Improves upon Riemann sums by adding area of trapezoids rather than simple rectangles.  N = number of subintervals  Obviously a big for-loop to add the f(x)’s

11  Mr. Body has met foul play. But he left a “Clue” to his murderer’s identity by coding the green & blue pixel values in a harmless-looking BMP image. The red pixels merely obscure the message  So process the 2D array of pixels in the BMP, suppressing red and enhancing green/blue to find the killer.  2D arrays & parallel for loops is a natural

12  Given a list of books, have a set of readers give their rating of the book [-5 to +5]. Then take a new reader and comparing her ratings to your total population, make the appropriate book recommendations to her.  Computing dot products (actually just about any array processing) in an O(n 2 ) environment is a parallel dream

13  Prof Hansen uses this assignment to teach: › Error correcting codes › Matrix multiplication › Binary representations of data › Binary I/O (optional)  Use a generator matrix and a parity check matrix to manipulate Hamming(7,4)  Matrix multiplication (even tiny matrices) is bull's-eye parallel fodder.

14  Find a big list of words. Challenge a human to solve Hangman. The wrinkle is that the computer never actually picks a word till it is forced to do so.  For every human guess, find the largest subset of words possible  For example: HEAR_ (can be HEARD, HEART, HEARS)  Essentially impossible to beat in 10 guesses.


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