lecture 8,9,10 [Autosaved].ppt

Message-Passing Computing 2.1  Message-Passing Programming using User-level Message Passing Libraries Two primary mechanisms needed: 1. A method of creating separate processes for execution on different computers 2. A method of sending and receiving messag...

Message-Passing Computing 2.1  Message-Passing Programming using User-level Message Passing Libraries Two primary mechanisms needed: 1. A method of creating separate processes for execution on different computers 2. A method of sending and receiving messages 2.2 Multiple program, multiple data (MPMD) model Source Source file file Compile to suit processor Executables Processor 0 Processor p - 1 2.3  Single Program Multiple Data (SPMD) model. Different processes merged into one program. Control statements select different parts for each processor to execute. All executables started together - static process creation Source file Basic MPI way Compile to suit processor Executables Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, Processor 0 © 2004 Pearson Education Inc. All rights reserved. Processor p 1  2.4  Multiple Program Multiple Data (MPMD) Model Separate programs for each processor. One processor executes master process. Other processes started from within master process - dynamic process creation. Process 1 Start execution spawn(); of process 2 Process 2 Time Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.5  Basic “point-to-point” Send and Receive Routines Passing a message between processes using send() and recv() library calls: Process 1 Process 2 x y Movement send(&x, 2); of data recv(&y, 1); Generic syntax (actual formats later) Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.6  Synchronous Message Passing Routines that actually return when message transfer completed. Synchronous send routine Waits until complete message can be accepted by the receiving process before sending the message. Synchronous receive routine Waits until the message it is expecting arrives. Synchronous routines intrinsically perform two actions: They transfer data and they synchronize processes. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.7 Synchronous send() and recv() using 3-way protocol Process 1 Process 2 Time send(); Request to send Suspend Acknowledgment process recv(); Both processes Message continue (a) When send() occurs before recv() Process 1 Process 2 Time recv(); Request to send Suspend send(); process Both processes Message continue Acknowledgment (b) When recv() occurs before send() Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.8  Asynchronous Message Passing Routines that do not wait for actions to complete before returning. Usually require local storage for messages. More than one version depending upon the actual semantics for returning. In general, they do not synchronize processes but allow processes to move forward sooner. Must be used with care. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.9  MPI Definitions of Blocking and Non-Blocking Blocking - return after their local actions complete, though the message transfer may not have been completed. Non-blocking - return immediately. Assumes that data storage used for transfer not modified by subsequent statements prior to being used for transfer, and it is left to the programmer to ensure this. These terms may have different interpretations in other systems. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.10  How message-passing routines return before message transfer completed Message buffer needed between source and destination to hold message: Process 1 Process 2 Time Message buffer send(); Continue recv(); process Read message buffer Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.11  Asynchronous (blocking) routines changing to synchronous routines Once local actions completed and message is safely on its way, sending process can continue with subsequent work. Buffers only of finite length and a point could be reached when send routine held up because all available buffer space exhausted. Then, send routine will wait until storage becomes re-available - i.e then routine behaves as a synchronous routine. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.12  Message Tag Used to differentiate between different types of messages being sent. Message tag is carried within message. If special type matching is not required, a wild card message tag is used, so that the recv() will match with any send(). Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.13  Message Tag Example To send a message, x, with message tag 5 from a source process, 1, to a destination process, 2, and assign to y: Process 1 Process 2 x y Movement send(&x,2,5); of data recv(&y,1,5); Waits for a message from process 1 with a tag of 5 Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.14  “Group” message passing routines Have routines that send message(s) to a group of processes or receive message(s) from a group of processes Higher efficiency than separate point-to- point routines although not absolutely necessary. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.15  Broadcast Sending same message to all processes concerned with problem. Multicast - sending same message to defined group of processes. Process 0 Process 1 Process p  1 data data data Action buf bcast(); bcast(); bcast(); Code MPI form Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.16  Scatter Sending each element of an array in root process to a separate process. Contents of ith location of array sent to ith process. Process 0 Process 1 Process p  1 data data data Action buf scatter(); scatter(); scatter(); Code MPI form Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.17  Gather Having one process collect individual values from set of processes. Process 0 Process 1 Process p  1 data data data Action buf gather(); gather(); gather(); Code MPI form Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.18  Reduce Gather operation combined with specified arithmetic/logical operation. Example: Values could be gathered and then added together by root: Process 0 Process 1 Process p  1 data data data Action buf + reduce(); reduce(); reduce(); Code MPI form Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.19  PVM (Parallel Virtual Machine) Perhaps first widely adopted attempt at using a workstation cluster as a multicomputer platform, developed by Oak Ridge National Laboratories. Available at no charge. Programmer decomposes problem into separate programs (usually master and group of identical slave programs). Programs compiled to execute on specific types of computers. Set of computers used on a problem first must be defined prior to executing the programs (in a hostfile). Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.20 Message routing between computers done by PVM daemon processes installed by PVM on computers that form the virtual machine. Workstation PVM Can have more than one process daemon running on each computer. Application program (executable) Messages sent through Workstation network Workstation PVM daemon Application program PVM (executable) daemon Application program (executable) MPI implementation we use is similar. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.21  MPI (Message Passing Interface) Message passing library standard developed by group of academics and industrial partners to foster more widespread use and portability. Defines routines, not implementation. Several free implementations exist. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.22  MPI Process Creation and Execution Purposely not defined - Will depend upon implementation. Only static process creation supported in MPI version 1. All processes must be defined prior to execution and started together. Originally SPMD model of computation. MPMD also possible with static creation - each program to be started together specified. 2.23  Communicators Defines scope of a communication operation. Processes have ranks associated with communicator. Initially, all processes enrolled in a “universe” called MPI_COMM_WORLD, and each process is given a unique rank, a number from 0 to p - 1, with p processes. Other communicators can be established for groups of processes. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.24  Using SPMD Computational Model main (int argc, char *argv[]) { MPI_Init(&argc, &argv);//argc : argument count // argv : argument value which is an array of string.. MPI_Comm_rank(MPI_COMM_WORLD, &myrank); if (myrank == 0) master(); else slave();.. MPI_Finalize(); } where master() and slave() are to be executed by master process and slave process, respectively. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.25 Example 1 The following is a sample MPI program that prints a greeting message. At run time, the MPI program creates four processes, in which each process prints a greeting message including its process id. #include #include int main(int argc, char *argv[]) { int rank, size, len; char name[MPI_MAX_PROCESSOR_NAME]; MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &size); MPI_Get_processor_name(name, &len); printf(“Salam! I'm %d of %d on %s\n",rank, size, name); MPI_Finalize(); return 0; } Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.26  Unsafe message passing Example Process 0 Process 1 Destination send(…,1,…); lib() send(…,1,…); Source (a) Intended behavior recv(…,0,…); lib() recv(…,0,…); Process 0 Process 1 send(…,1,…); lib() send(…,1,…); (b) Possible behavior recv(…,0,…); lib() recv(…,0,…); Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.27  MPI Solution “Communicators” Defines a communication domain - a set of processes that are allowed to communicate between themselves. Communication domains of libraries can be separated from that of a user program. Used in all point-to-point and collective MPI message-passing communications. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.28  Default Communicator MPI_COMM_WORLD Exists as first communicator for all processes existing in the application. A set of MPI routines exists for forming communicators. Processes have a “rank” in a communicator. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.29  MPI Point-to-Point Communication Uses send and receive routines with message tags (and communicator). Wild card message tags available Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.30  MPI Blocking Routines Return when “locally complete” - when location used to hold message can be used again or altered without affecting message being sent. Blocking send will send message and return - does not mean that message has been received, just that process free to move on without adversely affecting message. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.31  Parameters of blocking send MPI_Send(buf, count, datatype, dest, tag, comm) Address of Datatype of Message tag send buffer each item Number of items Rank of destination Communicator to send process Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.32 Parameters of blocking receive MPI_Recv(buf, count, datatype, src, tag, comm, status) Status Address of Datatype of Message tag after operation receive buffer each item Maximum number Rank of source Communicator of items to receive process Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.33  MPI Basic Types MPI Datatype C datatype MPI_CHAR signed char MPI_SHORT signed short int MPI_INT signed int MPI_LONG signed long int MPI_UNSIGNED_CHAR unsigned char MPI_UNSIGNED_SHORT unsigned short int MPI_UNSIGNED unsigned int MPI_UNSIGNED_LONG unsigned long int MPI_FLOAT float MPI_DOUBLE double MPI_LONG_DOUBLE long double MPI_BYTE MPI_PACKED Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.34  Example 2 #include #include int main(int argc, char *argv[]) { int myrank; MPI_Status status; MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &myrank); if (myrank == 0){ int x = 3, y = 7; MPI_Send(&x,1,MPI_INT,1,0,MPI_COMM_WORLD); MPI_Send(&y,1,MPI_INT,1,1,MPI_COMM_WORLD); } else if (myrank == 1){ int x,y; MPI_Recv(&x,1,MPI_INT,0,0,MPI_COMM_WORLD,&status); printf("Received %d from %d.\n", x,0); MPI_Recv(&y,1,MPI_INT,0,1,MPI_COMM_WORLD,&status); printf("Sent %d to %d.\n", y,0); } MPI_Finalize(); return 0; } Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.35  Example 3: Communicating with Self #include #include int main(int argc, char *argv[]) { int x =3; MPI_Status status; MPI_Init(&argc, &argv); MPI_Send(&x,1,MPI_INT,0,0,MPI_COMM_WORLD); MPI_Recv(&x,1,MPI_INT,0,0,MPI_COMM_WORLD,&status); MPI_Finalize(); return 0; } Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.36  MPI Nonblocking Routines Nonblocking send - MPI_Isend() - will return “immediately” even before source location is safe to be altered. Nonblocking receive - MPI_Irecv() - will return even if no message to accept. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.37  Nonblocking Routine Formats MPI_Isend(buf,count,datatype,dest,tag,comm,request) MPI_Irecv(buf,count,datatype,source,tag,comm, request) Completion detected by MPI_Wait() and MPI_Test(). MPI_Wait() waits until operation completed and returns then. MPI_Test() returns with flag set indicating whether operation completed at that time. Need to know whether particular operation completed. Determined by accessing request parameter. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.38  Example 4 #include #include int main(int argc, char *argv[]) { int x =3, myrank; MPI_Status status; MPI_Request req1, req2; MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &myrank); MPI_Send(&x,1,MPI_INT,0,0,MPI_COMM_WORLD); printf("Sent %d to %d\n", x, myrank); MPI_Irecv(&x,1,MPI_INT,0,0,MPI_COMM_WORLD,&req1); printf("Received %d to %d\n", x, myrank); MPI_Finalize(); return 0; } 2.39 Four Send Communication Modes Standard Mode Send - Not assumed that corresponding receive routine has started. Amount of buffering not defined by MPI. If buffering provided, send could complete before receive reached. Buffered Mode - Send may start and return before a matching receive. Necessary to specify buffer space via routine MPI_Buffer_attach(). Synchronous Mode - Send and receive can start before each other but can only complete together. Ready Mode - Send can only start if matching receive already reached, otherwise error. Use with care. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.40  Each of the four modes can be applied to both blocking and nonblocking send routines. Only the standard mode is available for the blocking and nonblocking receive routines. Any type of send routine can be used with any type of receive routine. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.41  Collective Communication Involves set of processes, defined by an intra-communicator. Message tags not present. Principal collective operations: MPI_Bcast() - Broadcast from root to all other processes MPI_Gather()- Gather values for group of processes MPI_Scatter() - Scatters buffer in parts to group of processes MPI_Alltoall() - Sends data from all processes to all processes MPI_Reduce()- Combine values on all processes to single value MPI_Reduce_scatter() - Combine values and scatter results MPI_Scan() - Compute prefix reductions of data on processes Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.42  Example 5 #include #include #include "mpi.h" int main( int argc, char* argv[]){ int myid,numprocs,itag; int i,ip; float x,y; MPI_Init(&argc,&argv); MPI_Comm_rank(MPI_COMM_WORLD,&myid); MPI_Comm_size(MPI_COMM_WORLD,&numprocs); if (myid == 0) x=2; MPI_Bcast(&x,1,MPI_FLOAT,0,MPI_COMM_WORLD); y=pow(x,2+myid); printf("On process %d y= %.1f\n",myid,y); MPI_Finalize(); return 0; } Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.43  MPI_Gather() To gather items from group of processes into process 0, using dynamically allocated memory in root process: int data; MPI_Comm_rank(MPI_COMM_WORLD, &myrank); if (myrank == 0) { MPI_Comm_size(MPI_COMM_WORLD, &grp_size); buf = (int *)malloc(grp_size*10*sizeof (int)); } MPI_Gather(data,10,MPI_INT,buf,grp_size*10,MPI_INT,0,MPI_COMM_WORLD) ; MPI_Gather() gathers from all processes, including root. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.44  Barrier As in all message-passing systems, MPI provides a means of synchronizing processes by stopping each one until they all have reached a specific “barrier” call. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.45  #include “mpi.h” #include #include #define MAXSIZE 1000 void main(int argc, char *argv) { Sample MPI program int myid, numprocs; int data[MAXSIZE], i, x, low, high, myresult, result; char fn; char *fp; MPI_Init(&argc,&argv); MPI_Comm_size(MPI_COMM_WORLD,&numprocs); MPI_Comm_rank(MPI_COMM_WORLD,&myid); if (myid == 0) { strcpy(fn,getenv(“HOME”)); strcat(fn,”/MPI/rand_data.txt”); if ((fp = fopen(fn,”r”)) == NULL) { printf(“Can’t open the input file: %s\n\n”, fn); exit(1); } for(i = 0; i < MAXSIZE; i++) fscanf(fp,”%d”, &data[i]); } MPI_Bcast(data, MAXSIZE, MPI_INT, 0, MPI_COMM_WORLD); x = n/nproc; low = myid * x; high = low + x; for(i = low; i < high; i++) myresult += data[i]; printf(“I got %d from %d\n”, myresult, myid); MPI_Reduce(&myresult, &result, 1, MPI_INT, MPI_SUM, 0, MPI_COMM_WORLD); if (myid == 0) printf(“The sum is %d.\n”, result); MPI_Finalize(); } Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.46  Evaluating Parallel Programs We have introduced two idealized laws for analyzing parallelism in Chapter 1 A good performance model is, while abstracting unimportant details, able to – Explain available observation – Predict future circumstances Amdahl’s law, empirical observations and asymptotic analysis do not satisfy the first of these requirements On the other hand, conventional modeling not practical – Typically involves detailed simulations of hardware components – Thus, too detailed for practical parallel programmers We introduce performance modeling technique with intermediate-level detail Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.47 Sequential execution time, ts: Estimate by counting computational steps of best sequential algorithm. Parallel execution time, tp: In addition to number of computational steps, tcomp, need to estimate communication overhead, tcomm: tp = tcomp + tcomm Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.48  Computational Time Count number of computational steps. When more than one process executed simultaneously, count computational steps of most complex process. Generally, function of n and p, i.e. tcomp = f (n, p) Often break down computation time into parts. Then tcomp = tcomp1 + tcomp2 + tcomp3 + … Analysis usually done assuming that all processors are same and operating at same speed. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.49  Communication Time Many factors, including network structure and network contention. As a first approximation (e.g., ignores routing), use tcomm = tstartup + ntdata tstartup is startup time, essentially time to send a message with no data. – Assumed to be constant. – Includes msg packing and unpacking tdata is transmission time to send one data word – Also assumed constant, – There are n data words. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.50  Communication Time (cont’d) Final communication time, tcomm, the summation of communication times of all sequential messages from a process, i.e. tcomm = tcomm1 + tcomm2 + tcomm3 + … Typically, communication patterns of all processes same and assumed to take place together so that only one process need be considered. Both startup and data transmission times, tstartup and tdata, measured in units of one computational step, so that can add tcomp and tcomm together to obtain parallel execution time, tp. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.51  Benchmark Factors With ts, tcomp, and tcomm, can establish speedup factor and computation/communication ratio for a particular algorithm/implementation: Both functions of number of processors, p, and number of data elements, n. Will give indication of scalability of parallel solution with increasing number of processors and problem size. Computation/communication ratio will highlight effect of communication with increasing problem size and system size. Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.52  Debugging and Evaluating Parallel Programs Empirically Low-level debugging Getting a parallel program to work properly can be a significant intellectual challenge Sequential programs are debugged, usually, by adding instrumentation code Like sequential code, instrumentation code can slowdown a parallel program More seriously, instrumentation can cause instructions of a parallel program to be executed in a different interleave order – Assumed to be constant.Can cause a nonworking program to work! Traditional debugging tools as in sequential programs of little use – Varying orders of execution possible Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.53  Debugging and Evaluating Parallel Programs Empirically Visualization Tools Programs can be watched as they are executed in a space-time diagram (or process-time diagram): Process 1 Process 2 Process 3 Computing Time Waiting Message-passing system routine Message Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.54  Debugging and Evaluating Parallel Programs (cont’d) Implementations of visualization tools are available for MPI. An example is the Upshot program visualization system. Visualization tools imply software probes into the execution – May alter characteristics of the computations Hardware performance monitors (which do not usually affect performance are) also available Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.55  Evaluating Programs Empirically Measuring Execution Time To measure the execution time between point L1 and point L2 in the code, we might have a construction such as. L1: time(&t1);.. L2: time(&t2);. elapsed_time = difftime(t2, t1); printf(“Elapsed time = %5.2f seconds”, elapsed_time); MPI provides the routine MPI_Wtime() for returning time (in seconds). Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd Edition, by B. Wilkinson & M. Allen, © 2004 Pearson Education Inc. All rights reserved. 2.56