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Data Parallel Languages (Chapter 4)

Data Parallel Languages (Chapter 4). ASC Language multiC Fortran 90 and HPF. Contents. ASC Programming Language Multi-C Language Fortran 90 and High Performance Fortran. References for Chapter 4.

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Data Parallel Languages (Chapter 4)

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  1. Data Parallel Languages(Chapter 4) ASC Language multiC Fortran 90 and HPF

  2. Contents • ASC Programming Language • Multi-C Language • Fortran 90 and High Performance Fortran Parallel & Distributed Computing

  3. References for Chapter 4 11. Jerry Potter, Associative Computing - A Programming Paradigm for Massively Parallel Computers, Plenum Publishing Company, 1992 • Jerry Potter, ASC Primer, 42 pages, posted at parallel website under downloads at http://zserver.cs.kent.edu/PACL/downloads. • Ian Foster, Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering, Addison Wesley, 1995, Online at http://www-unix.mcs.anl.gov/dbpp/text/book.html • “The multiC Programming Language”, Preliminary Documentation, WaveTracer, PUB-00001-001-00.80, Jan. 1991. • “The multiC Programming Language”, User Documentation, WaveTracer, PUB-00001-001-1.02, June 1992. Parallel & Distributed Computing

  4. The ASC Language Michael C. Scherger & Johnnie Baker Department of Computer Science Kent State University October 2003

  5. Contents • Software Location and Installation • Basic Program Structure • Data Types and Variables • Operator Notation • Control Structures • Looping • Accessing Values in Parallel Variables • Associations and Setscope • Input and Output • Performance Monitor • Subroutines and other topics Parallel & Distributed Computing

  6. Compiler and Emulator DOS/Windows, UNIX (Linux) WaveTracer Connection Machine http://zserver.cs.kent.edu/PACL/downloads.htm Use any text editor. Careful on moving files between DOS and UNIX! Software Anyprog.asc -e -wt -cm ASC Compiler Anyprog.iob -e -wt -cm ASC Emulator Standard I/O File I/O Parallel & Distributed Computing

  7. Software • Example: • To compile your program… • % asc1.exe –e shapes.asc • To execute your program… • % asc2.exe –e shapes.iob • % asc2.exe –e shapes.iob < shapes.dat • % asc2.exe –e shapes.iob < shapes.dat > shapes.out Parallel & Distributed Computing

  8. Basic Program Structure • Main program_name • Constants; • Variables; • Associations; • Body; • End; Parallel & Distributed Computing

  9. Basic Program Structure • Example: • Consider an ASC Program that computes the area of various simple shapes (circle, rectangle, triangle). • Here is an example shapes.asc • Here is the data shapes.dat • Here is the shapes.out NOTE: Above links are only active during the “slide show”. Parallel & Distributed Computing

  10. Data Types and Variables • ASC has eight data types… • int (i.e., integer), real, hex (i.e., base 16), oct (i.e., base 8), bin (i.e., binary), card (i.e., cardinal), char (i.e., character), logical, index. • Card is used for unsigned integer data. • Variables can either be scalar or parallel. • Scalar variables are in the IS. Parallel variables are in the cells. • Parallel variables have the suffix “[$]” at the end of the identifier. • Can specify the length (in bits) of parallel variables. Default word length works fine for most programs. Parallel & Distributed Computing

  11. Comparison of Logical Parallel and Index Parallel • A index parallel variable selects a single scalar value from a parallel variable. • A logical parallel variable L is normally used to store a logical value resulting from a search such as L[$] = A[$] .eq. B[$] • ASC implementation simplifies usage by not formally distinguishing between the two. • The correct type, based on usage, should be selected to improve readability. Parallel & Distributed Computing

  12. Array Dimensions • A parallel variable can have up to 3 dimensions • First dimension is “$”, the parallel dimension • The array numbering is zero-based, so the declaration int parallel A[$,2] creates the following 1dimensional variables: A[$,0], A[$,1], A[$,2] Parallel & Distributed Computing

  13. Mixed Mode Operations • Mixed mode operations are supported and their result has the “natural” mode. For example, given declarations int scalar a, b, c; int parallel p[$], q[$], r[$], t[$,4]; index parallel x[$], y[$]; then c = a + b is a scalar integer q[$] = a + p[$] is a parallel integer variable a + p[x] is a integer value r[$] = t[x,2]+3*p[$] is a parallel integer variable x[$] = p[$] .eq. r[$] is an index parallel variable • More examples are given on page 9-10 of ASC Primer Parallel & Distributed Computing

  14. Operator Notation • Relational Operators • “less than” is denoted by .lt. or < • “equal” is denoted .eq. or = • “not equal” is denoted by .ne. or != • Logical Operators • “not” is denoted by .not. or ! • “or” is denoted by .or. OR || • “and” is denoted by .and. or && • Arithmetic Operators • Addition is denoted by + • Multiplication is denoted by * • Division is denoted by / • For more information, see page 40 of ASC Primer Parallel & Distributed Computing

  15. The IF-THEN-ELSE Control Structure • Scalar version similar to sequential programming languages. • Either executes either the body of the IF or the body of the ELSE. • Parallel version normally executes both “bodies”. • First finds the responders for the conditional • If there are any responders, the responding PEs execute the body of the IF. • Next identifies the non-responders for the conditional • If there are non-responders, those PEs executes the body of the ELSE. • This control structure is also a masking operation. Parallel & Distributed Computing

  16. Parallel IF-THEN-ELSE Example if A[$] .eq. 2 then A[$] = 5; else A[$] = 0; endif; Parallel & Distributed Computing

  17. IF-THEN-ELSENANY Control Statement • Similar to the IF-THEN-ELSE, except that only one of the two bodies of code is executed. • First the logical expression following the “IF’ is evaluated • If there is at least one responder, then the responding processors execute the body of the IF. • However, if there were no responders to the logical expression, then all processors that were initially active at the start of this command execute the body of the ELSENANY • EXAMPLE IF A[$] .GE. 2 and A[$] .LT. 4 THEN C[$] = 1; ELSENANY C[$] = 9; ENDIF; • See page 16-17 of ASC Primer for more information Parallel & Distributed Computing

  18. ANY-ELSENANY Control Statement • If there is at least one responder, then all active processors (i.e., ones active at the start of this command) execute the statements inside the any-block. • If there are no responders, then all active processors execute the statements inside the elsenany block • any can be used alone (without the elsenany) • Example any A[$] .eq. 10 B[$] =11; elsenany B[$] = 100; endany; • For more information, see pages 17-19 of ASC Primer. Parallel & Distributed Computing

  19. Looping • Sequential looping • Loop-Until • See example on page 20 of ASC Primer • Sequential looping is an infrequently used operation in ASC • Two types of parallel looping constructs: • Parallel For-Loop • Conditional is evaluated only once. • Example on page 21. • Parallel While-Loop • Conditional is evaluated every iteration. • Example on page 21-22. Parallel & Distributed Computing

  20. Parallel For Loop • For construct • Often used when a process must be repeated for each cell that satisfies a certain condition. • The index variable is available throughout the body of the for statement • The index value of for is only evalulated initially Parallel & Distributed Computing

  21. For Loop Example • Example: sum = 0; for x in A[$] .eq. 2 sum = sum + B[$]; endfor x; • Trace for example: Parallel & Distributed Computing

  22. While Loop • Unlike the for statement, this construct re-evaluates the logical conditional statement prior to each execution of the body of the while. • The bit array resulting from the evaluation of the conditional statement is assigned to the index parallel variable on each pass. • The index parallel array is available for use within the body each loop. • The body of the while construct will continue to be executed until there are no responders (i.e., all zeros) in the index parallel variable. • Study example and trace in the ASC Primer carefully to make sure you understand the while construct. Parallel & Distributed Computing

  23. Get Statement • Used to retrieve a value from a specific field in a parallel variable satisfying a specific conditional statement. • Example: get x in tail[$] .eq. 1 val[x] = 0; endget x; • Study the trace of this example in on page 24 of ASC Primer to make sure its action is clear. Parallel & Distributed Computing

  24. Next Statement • Similar to get statement, except next updates the set of responders each time it is called. • Unlike get, two successive calls to next is expected to select two distinct processors (and two distinct association records). • Can be used inside a loop to sequentially process each responder. • See page 22-23 of ASC Primer for more details. Parallel & Distributed Computing

  25. Maximum and Minimum Values • The maxval and minval functions • maxval returns the maximum value of the specified items among the active responders. • Similarly, minval returns the minimum value. • Example: if (tail[$] .neq. 1) then k = maxval( weight[$]); endif; • See trace of example on pg 27 of ASC Primer. • The maxdex and mindex functions • Returns the index of one processor where a maximum or minimum occurs. • If maximum/minimum value occurs at more than one location, an arbitrary selection is made as to which index is returned. Parallel & Distributed Computing

  26. The associate statement • Creates an association between parallel variables and a parallel logical variables. • There are no “structs” or “classes” in ASC. • See earlier example in sample program. • See page 8 of ASC primer for more information. Parallel & Distributed Computing

  27. Setscope – Endsetscope Commands • Resets the parallel mask register • setscope jumps out of current mask setting to another mask setting. • One use is to reactivate currently inactive processors. • Also allow an immediate return to a previously calculated mask, such as an association. • Is an unstructured command such as go-to and jumps from current environment to a new environment. • Use sparingly • endsetscope resets mask to preceding setting. • See example on page 15 of ASC Primer. Parallel & Distributed Computing

  28. Input / Output • Parallel read statement • See page 12-13 of ASC Primer • Illustrated in earlier example program. • Must have an ASSOCIATE statement. • Input file must have blank line at end of data set. • Input is from “interactive user input” or from a data file identified in the command line. Parallel & Distributed Computing

  29. Input / Output • Parallel print statement • See page 13 in the ASC primer • Illustrated in earlier example program. • Must have an ASSOCIATE statement. • Does not output user specified strings. • I.e. User text messages. • Only outputs the values of parallel variables. Parallel & Distributed Computing

  30. Input / Output • The msg command • Page 13-14 of ASC Primer • Used to display user text messages. • Used to display values of scalar variables. • Used to display a dump of the parallel variables. Parallel & Distributed Computing

  31. Scalar variable input • Static input can be handled in the code. • Also, define or deflog statements can be used to handle static input. • Dynamic input is currently not supported directly, but can be accomplished as follows: • Reserve a parallel variable dummy (of desired type) for input. • Reserve a parallel index variable used. • Values to be stored in scalar variables are first read into dummy using a parallel-read and then transferred using get or next to the appropriate scalar variable. • Example: read dummy[$] in used[x]; get x in used[$] scalar-variable = dummy[x]; endget x; • NOTE: Don’t need to use associate statement to associate dummy with used. Omission causes no problems as no check is currently made. Parallel & Distributed Computing

  32. Dynamic Storage Allocation • allocate is used to identify a processor whose association record is currently unused. • Will be used to store a new association record • Creates a parallel index that points to the processor selected • release is used to de-allocate storage of specified records in an association • Can release multiple records simultaneously. • Example: char parallel node[$], parent[$]; logical parallel tree[$]; index parallel x[$]; associate node[$], level[$], parent[$] with tree[$]; ...... allocate x in tree[$] node[x] = ‘B’ endallocate x; release parent[$] .eq. ‘A’ from tree[$]. Parallel & Distributed Computing

  33. Performance Monitor • Keeps track of number of scalar and parallel operations. • It is turned on and off using the perform statement • PERFORM = 1; • PERFORM = 0; • The number of scalar and parallel operations can be printed using the msg command • MSG “Number of scalar and parallel operations are” PA_PERFORM SC_PERFORM; • The ASC Monitor is important for evaluation and comparison of various ASC algorithms and software. • See Pg 30-31 of ASC Primer for more information Parallel & Distributed Computing

  34. Additional Features • Restricted subroutine capability is currently available • See calland includeon pg 25-6 of ASC Primer. • Use of personal pronouns and articles in ASC make code easier to read and shorter. • See page 29 of ASC Primer. Parallel & Distributed Computing

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