1. 14.1 “Grid-enabling” applications Copyright B. Wilkinson, 2008. This material is the property of Professor Barry Wilkinson (UNC-Charlotte) and is for the sole and exclusive use of the students enrolled in the Fall 2008 Grid computing course broadcast on the North Carolina Research and Education Network (NCREN) to universities across North Carolina. Oct 29, 2008

2. Grid-enabling an application A poorly defined and understood term. In my opinion, it does NOT mean simply executing a job of a Grid platform! Almost all computer batch programs can be shipped to a remote Grid site and executed in little more that was possible with a remote ssh connection. Grid-enabling should include utilizing the unique distributed nature of the Grid platform. 14.2

3. Grid-enabling an application With that in mind, a simple definition is: Being able to execute an application on a Grid platform, using the distributed resources available on that platform. However, even that simple definition is not agreed upon by everyone! 14.3

4. A broad definition that matches our view of Grid enabling applications is: “Grid Enabling refers to the adaptation or development of a program to provide the capability of interfacing with a grid middleware in order to schedule and utilize resources from a dynamic and distributed pool of “grid resources” in a manner that effectively meets the program’s needs” 2 2 Nolan, K., “Approaching the Challenge of Grid-Enabling Applications.,” Open Source Grid & Cluster Conf., Oakland, CA, 2008. 14.4

5. 14.5 How does one do “Grid-enabling”? Still an open question and in the research domain without a standard approach. Here will describe various approaches.

6. We can divide the use of the computing resources in a Grid into two types: Using multiple computers separately to solve multiple problems Using multiple computers collectively to solve a single problem 14.6

7. Using Multiple Computers Separately Parameter Sweep Applications In some domains areas, scientists need to run the same program many times but with different input data. “Sweep” across parameter space with different values of input parameter values in search of a solution. Many cases, no readily computed answer and human intervention involved search or design space. 14.7

8. Parameter Sweep Applications Examples A scientist might wish to search for a new drug and needs to try different formulations that might best fit with a particular protein. A design engineer might be studying effects of different aerodynamic designs on performance of an aircraft. Sometimes aesthetic design process and many possible alternative designs and a human has to choose. Sometimes, a learning process - design engineer wishes to understand effects of changing various parameters. 14.8

9. Parameters in Parameter Sweep Typically, many parameters that can be altered. Might be a vast combination of parameter values. Ideally, some automated way of doing parameter sweep needed that includes both specifying parameter sweep and a way of scheduling individual sweeps across Grid platform. 14.9

10. Implementing Parameter Sweep Can be simply achieved by submitting multiple job description files, one for each set of parameters but that is not very efficient. Parameter sweep applications so important that research projects devoted to making them efficient on a Grid. Appears explicitly in job description languages. 14.10

11. RSL-2/JDD Example 5 causes five instances of job to be submitted. Simply cause five identical executables submitted. Four would be pointless unless either: Code selected actions for each instance, or different inputs and output files selected for each instance in job description file. Job description elements usually can be specified to change for each instance. 14.11

12. JSDL (version 1) Originally did not have parameter sweep. Has been (unofficially) extended to incorporate features for parameter sweep. Two forms of parameter sweep creation identified: Enumeration in a list, and Numerically related arguments. 14.12

13. Arguments Enumerated in a List Two additional elements: To specify selection of parameters To list the values contained within an element for each assignment. Multiple/nested assignments for various scenarios: Single substitution or Multiple simultaneous substitutions in different combinations. 14.13

14. 14.14

15. Parameter sweep element selection and substitution 14.15

16. Selecting XML Element Expression needed that selects an XML element. XPath expression -- provides a way to select an XML element in a XML document. 14.16

17. XPath Suppose XML document has form: XPath expression to identify element :... would be /a/b/c 14.17

18. XPath allows for much more expressive forms. For example suppose multiple tags called :. Expression to select 3rd element is /a/b/c[3] 14.18

19. To take an example for parameter sweep, consider JSDL job: /bin/echo Hello Fred 14.19

20. To alter second argument to be Bob, Alice, and Tom (3 sweeps) : /bin/echo Hello Fred //jsdl- posix:Argument[2] Bob Alice Tom 14.20

21. Question What is the output form the echo programs? 14.21

22. Using multiple computers collectively to solve a single problem 1.Existing and legacy programs 2.Wrapping program components as services 3.Modifying programs to use Grid API’s 4.Using parallel programming techniques 14.22

23. 1. Using existing and legacy programs Data partitioning Perhaps easiest way to use multiple computers together. Divide data into parts. Each computer works on each part. 14.23

24. Example BLAST algorithm used in bioinformatics to find statistical matches between gene sequences. User might submit sequence query that is compared to a very large database of known sequences in order to discover relationships or to match sequence to a gene family. Databases extremely large (100’s MBytes). 14.24

25. Partitioning BLAST database 14.25 If just one sequence from user, database partitioned into parts and different computers work on different parts.

26. Alternatively, if user(s) submitting many queries, submit each query to a different computer having access to whole database 14.26

27. Legacy Code 14.27 In many cases, Grid users want to re-use their existing programs written in C, C++ or even Fortran if really old. Documented source code may not be available. May be pre-packaged by manufacturer so rewriting not an option.

28. 14.28 Grid Enabling Legacy Software (GriddLeS) One project that addresses porting legacy code onto a Grid. Focuses on file handling Overloads existing file handling routines and redirects requests to remote locations if required.

29. 14.29 Grid Enabling Legacy Software (GriddLeS) Derived from: http://www.csse.monash.edu.au/~davida/griddles/

30. 2. Exposing an Application as a Service Grid computing has embraced Web service technology so natural to consider its use for accessing applications. “Wrap” application code to produce a Web service “Wrapping” means application not accessed directly but through service interface 14.30

31. Web Service Wrapper Approach 14.31

32. 3. Using Grid Middleware API’s Incorporate Grid middleware APIs in application code for operations such as: File input/output Starting and monitoring jobs Monitoring and discovery of Grid resources. 14.32

33. Using Globus API’s Globus provides suite of services that have APIs (C and Java interfaces) that could be called from the application. Extremely steep learning curve!! Literals hundreds, if not thousands, of C and Java routines listed at the Globus site. No tutorial help and sample usage. 14.33

34. Code using Globus APIs to copy a file (C++) Directly from (van Nieuwpoort) Also in (Kaiser 2004) (Kaiser 2005). 14.34

35. Using CoG kit API’s Using CoG kit API’s is at slightly higher level. Not too difficult but still requires setting up the Globus context. 14.35

36. CoG Kit program to transfer files 14.36

37. Higher Level Middleware-Independent APIs Higher level of abstraction than Globus middleware API’s desirable because: Complexity of Globus routines Grid middleware changes very often Globus not only Grid middleware 14.37

38. Other Grid middleware Includes: UNICORE (Uniform Interface to Computing Resources) gLite (Lightweight Middleware for Grid computing) – part of EGEE (Enabling Grids for E-sciencE) collaborative. To give an indication of the rapid changes that occur: gLite 3.0.2 Update 43 released May 22, 2008. gLite 3.1 Update 27 released July 3, 200 6 weeks later. 14.38

39. Concept of higher-level API’s above Grid middleware 14.39 Higher-level API’s should expose simple interface not tied to specific version of Grid middleware or even Grid middleware family at all.

40. 14.40 Grid Application Toolkit (GAT) APIs for developing and executing portable Grid applications that are independent of the underlying Grid infrastructure and available services. Developed in 2003-2005 time frame.

41. 14.41

42. 14.42 Copy a file in GAT/C++ ( Kaiser, H. 2005)

43. 14.43 SAGA (Simple API for Grid Applications) A subsequent effort made by Grid community to standardize higher level API’s

44. 14.44 SAGA Reading a file (C++) (Kielmann 2006)

45. 14.45 4. Using Parallel Programming Techniques Grid computing offers possibility of using multiple computers on a single problem to decrease execution time. Potential of using multiple computers collectively well known (at least since 1950’s) and obvious. Suppose a problem divided into p equal parts, with each part executing on a separate computer at same time. Overall time to execute problem using p computers simultaneously would be 1/p th of time on a single computer. Ideal situation - problems cannot usually be divided into equal parts that can be executed simultaneously.

46. 14.46 Message Passing Programming Computers on a Grid connected through Internet or a high performance distributed network. Suggests programming model similar to that often used in cluster computing. In cluster computing, computers connected through a local network with computers physically nearby. For such a structure, a convenient approach - have processes on different computers communicate through message passing. Message passing generally done using library routines. Programmer inserts message-passing routines into their code

47. 14.47 Message passing concept using library routines

48. 14.48 History of Message Passing Routines PVM (Parallel Virtual Machine) First highly successful message passing suite of libraries developed at Oak Ridge National Laboratories in late 1980’s by Sunderam Became widely used in the early 1990’s. Provided a complete fully implemented set of library routines for message passing.

49. 14.49 Subsequent standard specification for message- passing API’s. MPI developed by the MPI forum, a collective with more that 40 industrial and research organizations. Whereas PVM was an implementation of message- passing libraries, MPI only specified API interface. Implementation left to others. Now many implementations of MPI, most free. MPI (Message Passing Interface)

50. 14.50 MPI version 1 finalized in 1994. Version 2 of MPI introduced in 1997 with a greatly expanded scope. MPI version 1 has about 126 routines MPI version 2 has to about 156 routines. MPI-2 standard daunting. However, in most applications only a few MPI routines actually needed. Suggested that many MPI programs can be written using only about six different routines.

51. 14.51 Basic MPI Programming Model Each process executes same code User writes a single program compiled to suit processors and all processes started together. That does not mean that all necessarily do exactly the same actions. Processes have IDs. IF statements direct processes to perform specific actions e.g.: if (procID == 0)... /* do this */; if (procID == 1)... /* do this */;. Usually computation constructed as master-slave model: if (procID == 0)... /* master do this */; else... /* all slaves do this */;

52. 14.52 #include #include #include "mpi.h" main(int argc, char **argv ) { char message[20]; int i,rank, size, type=99; MPI_Status status; MPI_Init(&argc, &argv); MPI_Comm_size(MPI_COMM_WORLD,&size); MPI_Comm_rank(MPI_COMM_WORLD,&rank); if(rank == 0) { strcpy(message, "Hello, world"); for (i=1; i<size; i++) MPI_Send(message,13,MPI_CHAR,i,type,MPI_COMM_WORLD); } else MPI_Recv(message,20,MPI_CHAR,0,type,MPI_COMM_WORLD,&status); printf( "Message from process =%d : %.13s\n", rank,message); MPI_Finalize(); } Sample MPI “Hello world” program Will look at routines in more detail later

53. 14.53 Grid-enabling MPI programs Globus version of MPI available to run MPI jobs across a grid (MPICH-G2). http://www.globus.org/grid_software/computation/mpich-g2.php Message passing can cross sites:

54. 14.54 http://www.ngpp.ngp.org.sg/

55. 14.55 MPICH-G2 programs Ideally one can simply run the MPI job unmodified across the grid. However not that simple

56. 14.56 Problems: Firewalls: Need to accommodate firewalls by opening up ports Job Schedulers: Each site will have a separate independent local job scheduler, which will mean can guarantee all MPI processes will be operating at different sites at same time to communicate. Latency: The delays in messages in transit are much larger and variable between sites (Internet)

57. Questions 14.57