Data Parallel Languages ASC multiC Fortran 90 & HPF

Parallel & Distributed Computing2 Contents ASC Programming Language Multi-C Language Fortran 90 and High Performance Fortran

Parallel & Distributed Computing3 References for Data Parallel Languages 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 Ian Foster, Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering, Addison Wesley, 1995, Online at “The multiC Programming Language”, Preliminary Documentation, WaveTracer, PUB , Jan “The multiC Programming Language”, User Documentation, WaveTracer, PUB , June 1992.

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

Parallel & Distributed Computing5 Contents 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 Software Location and Installation Basic Program Structure

Parallel & Distributed Computing6 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 Computing7 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 understanding.

Parallel & Distributed Computing8 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 Computing9 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 Computing10 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 Computing11 The IF-THEN-ELSE Control Structure Scalar version similar to sequential programming languages.  Either the body of the IF or the body of the ELSE is executed, but not both. 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.  The PEs that are idle when the IF statement is encountered remain idle during this execution This control structure is also a masking operation.

Parallel & Distributed Computing12 Parallel IF-THEN-ELSE Example if A[$].eq. 2 then A[$] = 5; else A[$] = 0; endif; A[$] BEFORE MASK BEFORE A[$] AFTER THEN MASK ELSE MASK

Parallel & Distributed Computing13 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 of ASC Primer for more information

Parallel & Distributed Computing14 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 of ASC Primer.

Parallel & Distributed Computing15 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

Parallel & Distributed Computing16 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  Active responders are evaluated one after another until all have been processed. Parallel For Loop

Parallel & Distributed Computing17 For Loop Example  Example: sum = 0; for x in A[$].eq. 2 sum = sum + B[$]; endfor x;  Trace for example: A[$]B[$]xloopsum st nd rd10

Parallel & Distributed Computing18 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 and can be changed within the body. The iteration is terminated when there are no responders when the conditional statement is tested.  That is, 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 Computing19 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. Example: associate tail[$], head[$], weight[$] with graph[$] See page 8 of ASC primer for more information.

Parallel & Distributed Computing20 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; The index variable x in example will identify one active responder (if one exists) that satisfies conditional test. The index x is available to use within body of “get”. Study the trace of this example in on page 24 of ASC Primer to make sure its action is clear.

Parallel & Distributed Computing21 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 of ASC Primer for more details.

Parallel & Distributed Computing22 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 Computing23 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 Computing24 Parallel read Statement Works only with parallel variables. Must have an ASSOCIATE statement. Input is from “interactive user input” or from a data file identified in the command line. Input file must have blank line at end of data set to terminate input. Format:  read tail[$], head[$], weight[$] in graph[$];  Read (file) Tail[$], Head[$], Weight[$] in Graph[$]; See page of ASC Primer for example Also, see examples from class notes and primer

Parallel & Distributed Computing25 Parallel print Statement Must have an associate statement. Does not output user specified strings.  I.e. User text messages.  Only outputs the values of parallel variables. Example:  print tail[$], head[$], weight[$] in graph[$];  Print (file) tail[$], head[$], weight[$] in graph[$];  Provides a dump of the entire parallel variable “graph” Dump of association results follow: tail, head, weight ab40 ac30 See page 13 in the ASC primer and examples

Parallel & Distributed Computing26 Input / Output The msg command  Used to display user text messages.  Used to display values of scalar variables.  Used to display a dump of the parallel variables. The entire parallel variable contents printed Status of active responders or association variables ignored Format: msg “string” list’ Msg “The answers are” max BB[X] B[$] See Page of ASC Primer

Parallel & Distributed Computing27 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 Computing28 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: 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 Computing29 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 parallel and scalar operations are” PA_PERFORM SC_PERFORM; The ASC Monitor is important for evaluation and comparison of various ASC algorithms and software.  It can also be used to estimate or determine running time. See Pg of ASC Primer for more information

Parallel & Distributed Computing30 Additional Features Restricted subroutine capability is currently available  See call and include on pg 25-7 of ASC Primer.  ASC has a rather simple subroutine capability.  While not difficult, the subroutine details will not be covered in slides.  Assignment will not require use of subroutines. Use of personal pronouns and articles in ASC make code easier to read and shorter.  See page 29 of ASC Primer.  Again, the details are not covered in slides.

Parallel & Distributed Computing31 Software Compiler and Emulator  DOS/Windows, UNIX (Linux)  WaveTracer  Connection Machine PACL/downloads.htm PACL/downloads.htm Use any text editor.  Careful on moving files between DOS and UNIX! ASC Compiler ASC Emulator Anyprog.asc Anyprog.iob Standard I/OFile I/O -e -wt -cm -e -wt -cm

Parallel & Distributed Computing32 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.out

Parallel & Distributed Computing33 Basic Program Structure Main program_name  Constants;  Variables;  Associations;  Body; End;

Parallel & Distributed Computing34 Basic Program Structure Example:  Consider an ASC Program that computes the area of various simple shapes (circle, rectangle, triangle). Here is an example shapes.ascshapes.asc Here is the data shapes.datshapes.dat Here is the shapes.outshapes.out NOTE: Above links are only active during the “slide show”.