Description

A2 – Message Passing Interface (MPI)
Contents
1 Hello World Example 1
Q1. Hello World Program – Version 1 2
Q2. DAXPY Loop 2
Q3. Hello World Program – Version 2 2
Q4. Calculation of π – MPI Bcast and MPI Reduce 3
Q5. Reduction operation 3
Q6. Collective Communication – Scatter – Gather 5
Q7. MPI Derived Datatypes 6
Q8. Pack and Unpack 8
Q9. Derived Datatype – Indexed 8
Q10.Matrix Multiplication on a Cartesian Grid (2D Mesh) using Cannon’s Algorithm 9

1 Hello World Example
MPI Init(int *argc, char **argv); Initializes the MPI execution environment. Should be the first MPI call. After this call the system is setup to use the MPI library.
MPI Comm rank(MPI Comm comm, int rank); Determines the rank of the calling process in the communicator. The first argument to the call is a communicator and the rank of the process is returned in the second argument. Essentially a communicator is a collection of processes that can send messages to each other. The only communicator needed for basic programs is MPI COMM WORLD and is predefined in MPI and consists of the processees running when program execution begins.
MPI Finalize(); Terminates MPI execution environment
#include <stdio.h>
#include <mpi.h>
//Include user libraries
//Defines
//Global variables int main (int argc, char *argv[]) {
//Declare int variables for Process number, number of processes and length of processor name. int rank, size, namelen;
//Declare char variable for name of processor char name[100];
//Intialize MPI
MPI_Init(&argc, &argv);
//Get number of processes
MPI_Comm_size(MPI_COMM_WORLD, &size);
//Get processes number
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
//Get processesor name
MPI_Get_processor_name(name, &namelen);

Figure 1: Illustration of the Hello World – Version 2 processes communicating with the root process.
printf (“Hello World. Rank %d out of %d running on %s! “, rank, size, name);
//Terminate MPI environment MPI_Finalize(); return 0; }
Installation and Compilation
You will need the MPI compiler, (mpicc), and the MPI runtime, (mpirun) to run MPI programs. On most Ubuntu systems running the following commands will install mpicc and mpirun. MPI Manual pages are installed by the openmpi package. A small installation guide is here[1]. A more detailed installation is here[2]. The MPI complete reference is here[3].
$ sudo apt-get update
$ sudo apt-get install openmpi-bin openmpi-common openmpi-doc Compile and run the Hello World progam.
$ mpicc -o helloworld ./helloworld.c
$ mpirun -n 4 ./helloworld
Q1. Hello World Program – Version 1
Hello World Program – Version 1
Initialize the MPI parallel environment. Each process should identify itself and print out a Hello world message.
Q2. DAXPY Loop
DAXPY Loop
D stands for Double precision, A is a scalar value, X and Y are one-dimensional vectors of size 216 each, P stands for Plus. The operation to be completed in one iteration is X[i] = a*X[i] + Y[i]. Implement an MPI program to complete the DAXPY operation. Measure the speedup of the MPI implementation compared to a uniprocessor implementation.
The double MPI Wtime() function is the equivalent of the double omp get wtime().
Q3. Hello World Program – Version 2
Hello World Program – Version 2
Initialize the MPI parallel environment. Process with rank k(k = 1,2,…,p − 1) will send a “Hello World” message to the master process (has Rank 0). The master process receives the message and prints it. An illustration is provided in Figure 1. Use the MPI point-to-point blocking communication library calls (MPI Send and MPI Recv). Example usages of the calls follow:
• MPI Send(Message, BUFFER SIZE, MPI CHAR, Destination, Destination tag, MPI COMM WORLD);: Send the string of MPI CHAR of size BUFFER SIZE to Message to Process with Rank Destination in MPI COMM WORLD;
• Example: MPI Recv(Message, BUFFER SIZE, MPI CHAR, Source, Source tag, MPI COMM WORLD, &status);: Receive the string Message of MPI CHAR of size BUFFER SIZE from Source belonging to MPI COMM WORLD. Execution status of the function is stored in status.
Q4. Calculation of π – MPI Bcast and MPI Reduce
Calculation of π – MPI Bcast and MPI Reduce
This is the first example where many processs will cooperate to calculate a computational value. The task of this program will be arrive at an approximate value of π. The serial version of the code follows.
static long num_steps = 100000; double step; void main ()
{ int i; double x, pi, sum = 0.0; step = 1.0/(double)num_steps; for (i=0; i<num_steps; i++) { x = (i+0.5)*step; sum = sum + 4.0/(1.0+x*x);
}
pi = step * sum;
}
Your task is to create a parallel MPI version of the π program. For this version, use the MPI Bcast and MPI Reduce functions.
• Broadcast the total steps value (num steps) to all the processs (process 0 could broadcast). The syntax and usage of MPI Bcast follow:
– MPI Bcast(void *message, int count, MPI Datatype datatype, int root, MPI Comm comm);: Broadcast a message from the process with rank “root” to all other processes of the group.
– Example: MPI Bcast(&n, 1, MPI INT, 0, MPI COMM WORLD);. process 0 broadcasts n, of type MPI INT, to all the processs MPI COMM WORLD.
• Each process calculates its partial π value. Partial step).
• Use MPI Reduce to reduce the partial π values generated by each process. MPI Reduce is executed by every process. Syntax and an example follow:
– MPI Reduce(void *operand, void *result, int count, MPI Datatype datatype, MPI Operator op, int root, MPI Comm comm);: Reduce values on all processes to a single value. Count refers to the number of operand and result values.
– Example: MPI Reduce(&mypi, &pi, 1, MPI DOUBLE, MPI SUM, 0, MPI COMM WORLD);: Perform MPI SUM on mypi from each process into a single pi value and store it in process 0.
Q5. Reduction operation
Reduction operation
The basic idea is to bisect the available processors into two sets, say (P0, …, P ) and (P ). One half sends its number to its peer in the other half – Process P sends its integer to Pi. The receiving process computes the partial sum – process Pi calculates (P ). This process is repeated till the last process (P0 in the pseudocode) remains with the accumulated sum. Point to note – what if there are an odd number of processors at the beginning of this step? (the pseudocode handles this situation too).

Figure 2: The last four levels of a reduction that sums results from each processor, from bottom to top. For all processors whose number i is less than half, add the sum produced by processor number (i + half) to its sum (Figure 6.8 from Patterson and Hennessy, Computer Organization and Design. MK. 2014.).
half = numprocs; do
synch(); /*wait for partial sum completion*/
/* Conditional sum needed when half is odd;
Processor0 gets missing element. */ if (half%2 != 0 && Pn == 0) sum[0] += sum[half1]; half = half/2; /*dividing line on who sums */
if (Pn < half) sum[Pn] += sum[Pn+half];
while (half > 1); /*exit with final sum in Sum[0] */
Implement two versions of this program.
Ver. 1. Use blocking point-to-point communication API – MPI Send, MPI Recv.
Ver. 2. Use non-blocking point-to-point communication API – MPI ISend, MPI IRecv. MPI Wait or MPI Waitall is useful in this scenario.
• MPI ISend(void* buf, int count, MPI Datatype datatype, int dest, int tag, MPI Comm comm, MPI Request *request);: Begins a non-blocking send.
• MPI IRecv(void* buf, int count, MPI Datatype datatype, int source, int tag, MPI Comm comm, MPI Reques *request);. Both ISend and Irecv return a request parameter that can be used in the Wait and Waitall functions.
• MPI Wait(MPI Request *request, MPI Status *status);: Waits for a non-blocking MPI send or receive to complete. The request parameter corresponds to the request parameter returned by MPI Isend or MPI Irecv.
• MPI Waitall(int count, MPI Request *array of requests, MPI Status *array of statuses);: Waits for all given communications to complete and to test all or any of the collection of nonblocking operations. Example usage:
…
MPI_Status *status; MPI_Request request[200]; …
/* Receiver – Non Blocking calls */
MPI_Irecv(&value_to_recv, 1, MPI_INT, Source, Source_tag, MPI_COMM_WORLD, &request[i]); { t MPI_Waitall(1,&request[i],status);} …
/* Sender – Non Blocking calls */
MPI_Isend(&value_to_send, 1, MPI_INT, Destination, Destination_tag, MPI_COMM_WORLD,&request[i]);

(a) Root process decides non-uniform segments of Array to(b) All processes execute MPI Scatterv(), receive segments be sent to processes P0, P1, P2, and P3. of Array.

(c) Each process the computation step. Each process executes MPI Gatherv() to send its segment to the Root pro- (d) Root process receives segments of the processed array cess. (including its own).
Figure 3: The Scatter-Gather sequence for Q6..
/* wait for the Isend corresponding to request[i] to complete */
/* alternatively MPI_Wait could have been used here */ MPI_Waitall(1,&request[i],status); …
Q6. Collective Communication – Scatter – Gather
Collective Communication – Scatter – Gather
The task for this question follows:
• The root process (Rank 0) distributes an array in a non-uniform manner to all the processes in the communicator (including itself). This step is called Scatter.
• Each process (including the root) finds the square root of each element it receives.
• After the computation, all the processes return the modified array back to the root process. This step is called Gather.
The sequence of operations are illustrated in Figure 3.
MPI provides MPI Scatter, MPI Scatterv, MPI Gather, and the MPI Gatherv primitives for use in situations where scatter-gather sequences appear.
• MPI Scatterv: Scatters a buffer in parts to all tasks in a group.
int MPI Scatterv(void *sendbuf, int *sendcounts, int *displs, MPI Datatype sendtype, void *recvbuf, int recvcount, MPI Datatype recvtype, int root, MPI Comm comm)
– The root process scatters the array sendbuf.
– Size of each segment is recorded in the array sendcounts. sendcounts[0] is the number of elements to be sent to P0, sendcounts[1] is the number of elements to be sent to P1, and so on.
– displs: Integer array (of length group size). Entry i specifies the displacement (relative to sendbuf) from which to take the outgoing data to process i. sendbuf[ displs[0] ], sendbuf[ displs[0]+1 ], …, sendbuf[ displs[0]+ sendcounts[0]-1 ] are sent to P0, sendbuf[ displs[1] ], sendbuf[ displs[1]+1 ], …, sendbuf[ displs[1]+ sendcounts[1]-1 ] are sent to P1, and so on.
– Each receiving process is returned its segment in the recvcount array.
– root: The sending processes in the communicator (comm).
Note: The sendbuf, sendcounts, displs parameters are meaningful only in the root process.
• MPI Gatherv:Gathers varying amounts of data from all processes to the root process.
int MPI Gatherv(void *sendbuf, int sendcount, MPI Datatype sendtype, void *recvbuf, int *recvcounts, int *displs, MPI Datatype recvtype, int root, MPI Comm comm)
Each of the parameters in the MPI Gatherv function have similar meaning as in the MPI Scatterv function.
– Each sending process populates its sendbuf with sendcount elements of type MPI INT.
– The root process receives recvbuf array which is the union of all the sendbuf arrays. The elements are filled in recvbuf in the order of ranks of the subprocesses.
Note: The recvbuf, recvcounts, displs parameters are meaningful only in the root process.
Example usage:
MPI_Gatherv(My_gatherv, Send_Count, MPI_INT,
Total_gatherv, recv_count, Displacement, MPI_INT, Root, MPI_COMM_WORLD);
Each subprocess has its copy of My gatherv which contains Send Count elements. The Root process receives Total gatherv. Each subprocess’s segments start from the indices recorded int he Displacement array.
• MPI Scatter, MPI Gather: These primitives are used to scatter (gather) arrays uniformly (all segments of the array have equal number of elements) between subprocesses.
Q7. MPI Derived Datatypes
MPI Derived Datatypes
The task to complete:
• Build a custom datatype (it is called a derived datatype) in the MPI program.
• The example derived datatype for this question is a struct. The struct contains a char, an int array with 2 elements, and a float array with 4 elements.
• The root process broadcasts one data item of the derived datatype (the struct in this case) to all other subprocesses in the communicator. • The root process sends the data item of the derived datatype to each subprocesses through a point-to-point communication call.
The sequence of steps to be followed to implement this MPI program are shown in Figure Q7.. The steps are described below.
• To create a derived datatype that mirrors a struct, use the MPI Type create struct function.
Prototype: int MPI Type create struct(int count, int array of blocklengths[], MPI Aint array of displacement MPI Datatype array of types[], MPI Datatype *newtype)
The following example creates a derived datatype of the structure shown below.
struct dd{ char c; int i[2]; float f[4];
};

(a) Root process creates a new
(b) After the MPI Commit, all processes (c) Root sends data in the structure to
MPI datatype reflecting the mirror of register the derived datatype. all the subprocesses. The subprocesses
the struct.
now identify the data as an item of the derived datatype.
Figure 4: The sequence of steps in the MPI program for the derived datatype question (Q7.).
– count indicates the number of blocks – 3 for the structure above (char, int, float).
– array of blocklengths: Number of elements in each block. [1, 2, 4] for the structure above. Indicates that first block contains one element, the second block has 2 elements and the third block has 4 elements. The code snippet:
array_of_block_lengths[0] = 1; array_of_block_lengths[1] = 2; array_of_block_lengths[2] = 4;
– array of displacements: Byte displacement of each block (array of integers). The first value is always 0. The second displacement value is the difference between the addresses of the second block and the first. The third displacement value is the difference between the addresses of the third block and the first. The array of displacements is of type MPI Aint. Use the MPI Get address function to retrieve the addresses of type MPI Aint. The following snippet shows the second displacement calculation.
MPI_Get_address(&s.c, &block1_address); MPI_Get_address(s.i, &block2_address);
array_of_displacements[1] = block2_address-block1_address;
block1 address and block2 address are of type MPI Aint. array of displacements is an array of size total blocks and is of type MPI Aint. s is of type struct dd(shown above).
Prototype of MPI Get address: int MPI Get address(void *location, MPI Aint *address);
– array of types: Type of elements in each block. For the structure above, the first block contains characters, the second is integer and the third block contains floating point numbers. The array of types is of type MPI Datatype. The values will be MPI CHAR, MPI INT, MPI FLOAT.
– MPI Type create struct returns handle to the derived type in newtype.
• Commit the newtype using the MPI Type commit call. Prototype:
int MPI Type commit(MPI Datatype *datatype)
• Collective communication: The root broadcasts the filled structure to all the processes in its communicator. Print out the structure in all the processes.
• Point-to-point communication: The root sends the filled structure to each process in its communicator. The receiving process prints out the structure.
Q8. Pack and Unpack
Pack and Unpack
The previous question illustrated the creation of a derived datatype to help communicate compound datastructures. The same objective can be achieved by packing elements of different types at the sender process and unpacking the elements at the receiver process. For this question, achieve the same effect as the previous question using MPI Pack and MPI Unpack routines. Prototypes follow:
int MPI_Pack(void *inbuf, int incount, MPI_Datatype datatype, void *outbuf, int outsize, int *position, MPI_Comm comm)
incount number of items from inbuf, each of type datatype are stored contiguously in outbuf. The input/output parameter position mentions the offset to start writing into (0 for the first pack). On return, the next write can begin from position. outsize is the size of outbuf. The following code shows packing a char, an int array and a float array in the sender process.
MPI_Pack(&c, 1, MPI_CHAR, buffer,100,&position,MPI_COMM_WORLD);
MPI_Pack(iA, 2, MPI_INT, buffer,100,&position,MPI_COMM_WORLD);
MPI_Pack(fA, 4, MPI_FLOAT,buffer,100,&position,MPI_COMM_WORLD);
To send (receive) the packed buffer use the datatype MPI PACKED in MPI Send(MPI Recv). The prototype for MPI Unpack, to be used by the receiver process, follows.
int MPI_Unpack(void *inbuf, int insize, int *position, void *outbuf, int outcount, MPI_Datatype datatype,
MPI_Comm comm)
inbuf is the packed buffer of size insize. Unpacking starts from position (semantics of position are the same as MPI Pack. outcount number of elements of type datatype are read from inbuf and placed in outbuf.
Q9. Derived Datatype – Indexed
Derived Datatype – Indexed
The objective of this question is to create an indexed derived datatype. The task of this question is to create an indexed derived datatype that stores the upper triangle of a 2 dimensional matrix and sends it to another process. Use the MPI Type indexed call to create the “upper triangle” derived type. Prototype:
int MPI_Type_indexed(int count, int *array_of_blocklengths, int *array_of_displacements, MPI_Datatype oldtype, MPI_Datatype *newtype)
The MPI Type indexed creates an indexed derived datatype with the handle newtype. newtype contains count number of blocks. Number of items of type oldtype in each block is stored in array of blocklengths. array of displacements stores the displacement for each block, in multiples of oldtype extent. An example is shown below.
Consider A = , populated by the Root process. An indexed datatype has to be created that stores
the elements in the upper triangular matrix. The upper triangular matrix A upper = has to be indexed
and sent to process with rank 1. In each row, the upper triangular matrix elements start from the (N × row) + row position in a matrix of size N×N. For this example, array of displacements = [0,5,10,15]. There are as many blocks as there are a rows in the matrix (count = N. A block from a row contains (N – row) elements (where row is the row index of the matrix) array of blocklengths=[4,3,2,1]. oldtype is MPI INT. The call could look like this:
MPI_Type_indexed(N, blocklen, displacement, MPI_INT, &upper_triangle);
The task of this program is to create this MPI Type indexed derived datatype, commit it, and send an upper triangular matrix to the Rank 1 process. Note that blocks in MPI Type indexed could be from any arbitrary row in any sequence. The MPI Type indexed facilitates sending an irregular arrangement of elements from multi-dimensional matrices. To communicate a regular section of the matrix MPI Type vector is used. An example wouldbe to distribute columns of an N×N array to N processes.

Figure 5: For this question, assume that the processors are organized in the form of a 2D Mesh. The addressess of the processors and the coordinates are also shown.
Q10. Matrix Multiplication on a Cartesian Grid (2D Mesh) using Cannon’s Algorithm
Matrix Multiplication on a Cartesian Grid (2D Mesh) using Cannon’s Algorithm
For this question, we will assume that the multiprocessor is interconnected in the form of a grid (a 2-dimensional Mesh; Dimensions are X-axis and Y-axis). An example 4×4 Mesh is shown in Figure 5. For this questin, the mesh contains equal processors in both dimensions as shown. The 16 processors shown in the figure can be thought of being arranged in a Cartesian grid – the coordinates of such a grid are also shown in the figure.
Assume that one process is executing on one processor. For the purpose clarity the ranks of the processes equals the identies of the processor it is executing on. The objectives of this question are:
• Create N× N processes. Arrange the processors in the grid fashion.
• The input arrays are distributed equally amongst all processes (scatter operation). The product array calculated using Cannon’s multiplication algorithm[4]. The root process gathers the product array and displays it on the output.
Read the details of Cannon’s multiplication algorithm here: Parallel Matrix Multiplication page. An example is provided in the Cannon’s Matrix Multiplication section (below).
Details of the program implementation follow. The program can be divided into two stages.
Stage 1 Creation of a Grid of processes.
Stage 2 Cannon’s Matrix Multiplication.
Creation of a Grid of processes
• Create 16 processes (-np 16 in mpirun).
• Create a new communicator to which a Cartesian 4× 4 grid topology is attached. The API for this in MPI is MPI Cart create. Prototype:
int MPI_Cart_create(MPI_Comm comm_old, int ndims, int *dims, int *periods, int reorder, MPI_Comm *comm_cart)
– comm old is the communicator whose processes are to be arranged in a grid. If all the processes in the MPI program are to be put in the grid, comm old is equal to MPI COMM WORLD (the global communicator). The grid is now attached to the new communicator pointed to by comm cart.
– ndims: The grid contains ndims dimensions. For this program ndims=2. The X and Y dimensions.
– dims is an array. Each element records the number of processes per dimension. dims[0] is the number of processes in the X dimension. dims[1] is the number of processes in the Y dimension. Both values are 4 for this question.
– periods is the logical array of size ndims specifying whether the grid is periodic (true) or not (false) in each dimension. Both values are True for this question.
– reorder is a flag that indicates if the process ranks can be reordered in the new communicator. The newly created grid should have the same ordering. This value is false for this question.
• Example:
MPI_Cart_create(MPI_COMM_WORLD, 2, dims, periods, 0, &grid_comm)); grid comm is the handle to the new communicator.
• For each process in the grid, the following data will be useful to record.
– Grid info: Size of the grid, No. of processors per dimension.
– Rank in the grid.
– Position in the grid – row and column (X and Y coordinates).
– All communicator handles to which the process belongs.
Storing the data in a struct similar to the one below is suggested.
typedef struct{
int N; /* The number of processors in a row (column). */
int size; /* Number of processors. (Size = N*N */
int row; /* This processor’s row number. */
int col; /* This processor’s column number. */
int MyRank; /* This processor’s unique identifier. */
MPI_Comm comm; /* Communicator for all processors in the grid.*/
MPI_Comm row_comm; /* All processors in this processor’s row . */
MPI_Comm col_comm; /* All processors in this processor’s column. */ }grid_info;
• The coordinates of the current process can be obtained using the following API call. These are needed during multiplication.
MPI_Cart_coords(grid->comm, grid->MyRank, dims, Coordinates);
Coordinates is the array that records the position of the process in the grid. Coordinates[0] stores the X position and Coordinates[1] stores the position in the Y axis.
Note: You can verify that the grid has been created by printing out the coordinates of each process.
• Create communicators for individual rows and columns. This will ease the implementation of the algorithm (in the skewing, and rotating steps).
Cannon’s Matrix Multiplication
Before explaining the implementation details for the MPI program, an example Multiplication of two 3×3 matrices using Cannon’s algorithm is shown.
• Input Matrices are A and B. C is the product matrix.

• In A, left rotate (left shift with wrap around) elements in row i by i positions. In B, north rotate (up shift with wrap around) elements in column i by i positions. Eg. A[2][1] will be shifted to A[2][2]. B[2][1] will be shifted to B[1][1]. This step is called skewing. The rowwise and columnwise skewing steps are demonstrated in Figure 6. The skewed matrices are:

(a) Row-wise Skewing. Each rectangle represents a block of the
input matrix. (b) Column-wise Skewing. Each rectangle represents a block
of the input matrix.
Figure 6: Skewing illustrations for Q10. and ??.
• Multiply elements of A and B in position (elementwise) and add elementwise to C. This operation: C[i][j] +=A[i][j]*B[i][j]. The new C matrix is shown.

• Left rotate every element in A. North rotate every element in B. Repeat the multiplication step. The status of Aand B after the rotate steps are shown in the first line. The status of C after the multiplication is shown next. 7 8 9 1 5 9 63 14 48
2 3 1 4 8 3 1 10 27
A =6 4 5 B = 7 2 6 C = 20 48 12 7 8 9 1 5 9 70 54 129
2 3 1 4 8 3 9 34 30
A =6 4 5 B = 7 2 6 C = 62 56 42
• Repeat the previous step till all the multiplications are done. In this example there are 3 multiplications to complete.The next is the last step. C is the product matrix.

The core of the algorithm is the element wise multiplication and addition step made possible by skewing and rotating steps. Consider that each element from A and B were stored in a single processor. The skewing and rotating steps correspond to communication of elements between the processors. The multiplication and accumulation step is completed inside the processor. In other words, Process (i,j) in the grid, calculates the element C[i][j]. This idea can be scaled for larger arrays – divide the large arrays into submatrices (blocks) such that each processor now contains a submatrix. Every processor now computes the submatrix of the product instead of a single element.
The details of the implementation of this stage are presented below. The matrices are populated and multiplication algorithm is completed in this stage.
• In the root process, populate the multiplicand and multiplier arrays (A and B) with random numbers. Assume4×4 arrays for now.
• Divide the array into equal sized blocks and scatter to each process in the grid. For this example we have aconvenient 4×4 array and a 4×4 grid. Each process will get one element each of arrays A and B corresponding to
its position in the grid. (Process (2,1) in the grid will get A[2][1] and B[2][1]), etc.
• Create communicators containing processes from a each row and each column. Use the MPI Cart sub call for this purpose. The call partitions a communicator into subgroups. A subgroup in a grid is a lower-dimensional cartesian subgrid. In the case of a 2-dimensional grid the subgroups are groups of process from the same row or the same column. Prototype and example: int MPI_Cart_sub(MPI_Comm comm, int *remain_dims, MPI_Comm *comm_new)
The ith entry of remain dims specifies whether the ith dimension is kept in the subgrid (true) or is dropped (false). comm new is the handle of the new subgrid that contains the calling process. Example below shows the call that returns the communicator handle (row comm) to which all the processes in the same row as the calling process are attached. This is stored in the struct (grid) defined earlier.
remain_dims[0] = 1; remain_dims[1] = 0;
MPI_Cart_sub(grid->Comm, remain_dims, &(grid->row_comm));
• Skew the input arrays A and B. Skewing involves left rotating each element in the ith row by i positions in Matrix A. Skewing north rotates each element in the ith column by i positions in Matrix B. One way to implement would be to use MPI Send and MPI Recv calls. If you choose to do this, extra care should be taken to avoid deadlocks. The easier and better way of doing this would be to use the MPI Sendrecv replace call. The call is used to send data in a buffer to a sender process and receive data into the same buffer from another process. Prototype and example:
int MPI_Sendrecv_replace(void *buf, int count, MPI_Datatype datatype, int dest, int sendtag, int source, int recvtag, MPI_Comm comm, MPI_Status *status)
The array buf containing count items of type datatype. The contents of this buffer are sent to the process dest and is tagged with sendtag. The same buffer, buf, is filled with a max of count elements from the process source tagged with recvtag. An example:
MPI_Sendrecv_replace(item, 1, MPI_FLOAT, destination, send_tag, source, recv_tag, grid.row_comm, &status);
The processes in a single row of the grid are attached to the row comm communicator. The current process sends 1 item of type MPI FLOAT to destination and receives 1 MPI FLOAT item from destination.
• Perform the Cannon’s Multiplication algorithm. The rotation steps can be implemented in the same manner as the skewing step.
• At the end of the multiplication, gather the product matrix in the root process. Root prints out the result matrix.
Epilogue
This document borrows heavily from the excellent hyPACK 2013 workshop by CDAC, Pune. The MPI Quick Reference Guide[3] will be useful in implementing the questions in this assignment. The manual pages for MPI are an excellent source of syntax and semantics of the API calls. The recommended MPI Tutorial website[5]. An excellent collention of online books and tutorials[6]. MPI Wikibooks page[7]. Beginning MPI page is a good source[8]. The list of recommended books on MPI is maintained by the MPITutorial website[9]. Other references[10],.
References
[1] A. Dashin, “MPI Installation Guide,” https://jetcracker.wordpress.com/2012/03/01/ how-to-install-mpi-in-ubuntu/, Jetcracker.
[2] D. G. Martnez and S. R. Lumley, “Installation of MPI – Parallel and Distributed Programming,” http://lsi.ugr.es/ ∼jmantas/pdp/ayuda/datos/instalaciones/Install OpenMPI en.pdf.
[3] M. Snir, S. Otto, S. Huss-Lederman, D. Walker, and J. Dongarra, “MPI Quick Reference Guide,” http://www. netlib.org/utk/people/JackDongarra/WEB-PAGES/SPRING-2006/mpi-quick-ref.pdf, netlib.org.
[4] J. Demmel, “Parallel Matrix Multiplication,” http://www.cs.berkeley.edu/∼demmel/cs267/lecture11/lecture11. html, CS267.
[5] “MPI Tutorial Webpage,” http://mpitutorial.com.
[6] S. Baden, “MPI Online books and Tutorials Page,” https://cseweb.ucsd.edu/∼baden/Doc/mpi.html.
[7] “Message Passing Interface,” https://en.wikibooks.org/wiki/Message-Passing Interface.
[8] “Beginning MPI,” http://chryswoods.com/beginning mpi/.
[9] “Recommended MPI Books,” http://mpitutorial.com/recommended-books/.
[10] B. Barney, “MPI,” https://computing.llnl.gov/tutorials/mpi, Lawrence Livermore National Laboratory.