NORA/Clusters

AMANO, Hideharu

Textbook pp. １４０－１４７

NORA (No　 Remote　Access　Memory　Model)

No hardware shared memory Data exchange is done by messages (or

packets) Dedicated synchronization mechanism is

provided. High peak performance Message passing library （MPI,PVM) is

provided.

Message passing（ Blocking: randezvous ）

Send Receive Send Receive

Message passing（with buffer）

Send Receive Send Receive

Message passing（ non-blocking）

Send ReceiveOther Job

PVM (Parallel　 Virtual　Machine) A buffer is provided for a sender. Both blocking/non-blocking receive is

provided. Barrier synchronization

MPI(Message　 Passing　Interface) Superset of the PVM for 1 to 1

communication. Group communication Various communication is supported. Error check with communication tag. Detail will be introduced later.

Programming style using MPI SPMD (Single Program Multiple Data

Streams) Multiple processes executes the same program. Independent processing is done based on the

process number. Program execution using MPI

Specified number of processes are generated. They are distributed to each node of the NORA

machine or PC cluster.

Communication methods

Point-to-Point communication A sender and a receiver executes function for

sending and receiving. Each function must be strictly matched.

Collective communication Communication between multiple processes. The same function is executed by multiple

processes. Can be replaced with a sequence of Point-to-Point

communication, but sometimes effective.

Fundamental MPI functions Most programs can be described using six fu

ndamental functions MPI_Init() … MPI Initialization MPI_Comm_rank() … Get the process # MPI_Comm_size() … Get the total process # MPI_Send() … Message send MPI_Recv() … Message receive MPI_Finalize() … MPI termination

Other MPI functions

Functions for measurement MPI_Barrier() … barrier synchronization MPI_Wtime() … get the clock time

Non-blocking function Consisting of communication request and check Other calculation can be executed during waiting.

An Example1: #include <stdio.h>2: #include <mpi.h>3:4: #define MSIZE 645:6: int main(int argc, char **argv)7: {8: 　　 char msg[MSIZE];9: 　　 int pid, nprocs, i;10: 　 MPI_Status status;11:12: 　 MPI_Init(&argc, &argv);13: 　 MPI_Comm_rank(MPI_COMM_WORLD, &pid);14: 　　 MPI_Comm_size(MPI_COMM_WORLD, &nprocs);15:16: if (pid == 0) {17: 　 for (i = 1; i < nprocs; i++) {18: 　　 MPI_Recv(msg, MSIZE, MPI_CHAR, i, 0, MPI_COMM_WORLD, &status);19: 　　 fputs(msg, stdout);20: 　　 }21: 　 }22: 　 else {23: 　　 sprintf(msg, "Hello, world! (from process #%d)\n", pid);24: 　　 MPI_Send(msg, MSIZE, MPI_CHAR, 0, 0, MPI_COMM_WORLD);25: 　 }26:27: 　 MPI_Finalize();28:29: 　 return 0;30: }

Initialize and Terminate

int MPI_Init(

　　 int *argc, /* pointer to argc */

　　 char ***argv /* pointer to argv */ );

mpi_init(ierr)

　　 integer ierr 　　　　　 ! return code

The attributes from command line must be passed directly to argc and argv.

int MPI_Finalize();

　　 mpi_finalize(ierr)

　　 integer ierr 　　　　　 ! return code

Commincator functionsIt returns the rank (process ID) in the communicator comm.int MPI_Comm_rank(　　 MPI_Comm comm, /* communicator */　　 int *rank /* process ID (output) */ );mpi_comm_rank(comm, rank, ierr)　　 integer comm, rank　　 integer ierr 　　　　　 ! return code

It returns the total number of processes in the communicator comm.int MPI_Comm_size(　　 MPI_Comm comm, /* communicator */　　 int *size /* number of process (output) */ );mpi_comm_size(comm, size, ierr)　　 integer comm, size　　 integer ierr 　　　　　　 ! return code

Communicators are used for sharing commnication space among a subset of processes. MPI_COMM_WORLD is pre-defined one for all processes.

MPI_Send

It sends data to process “dest”.int MPI_Send( void *buf, /* send buffer */ int count, /* # of elements to send */ MPI_Datatype datatype, /* datatype of elements */ int dest, /* destination (receiver) process ID */ int tag, /* tag */ MPI_Comm comm /* communicator */ );

mpi_send(buf, count, datatype, dest, tag, comm, ierr) <type> buf(*) integer count, datatype, dest, tag, comm integer ierr ! return code

Tags are used for identification of message.

MPI_Recv

int MPI_Recv( void *buf, /* receiver buffer */ int count, /* # of elements to receive */ MPI_Datatype datatype, /* datatype of elements */ int source, /* source (sender) process ID */ int tag, /* tag */ MPI_Comm comm, /* communicator */ MPI_Status /* status (output) */ );

mpi_recv(buf, count, datatype, source, tag, comm, status, ierr) <type> buf(*) integer count, datatype, source, tag, comm, status(mpi_status_size) integer ierr ! return code

The same tag as the sender’s one must be passed to MPI_Recv. Set the pointers to a variable MPI_Status. It is a structure with three members:

MPI_SOURCE, MPI_TAG and MPI_ERROR, which stores process ID of the sender, tag and error code.

datatype and count

The size of the message is identified with count and datatype. MPI_CHAR char MPI_INT int MPI_FLOAT float MPI_DOUBLE double … etc.

Compile and Execution

% icc –o hello hello.c -lmpi

% mpirun –np 8 ./hello

Hello, world! (from process #1)

Hello, world! (from process #2)

Hello, world! (from process #3)

Hello, world! (from process #4)

Hello, world! (from process #5)

Hello, world! (from process #6)

Hello, world! (from process #7)

Shared memory model vs ． Message passing model

Benefits Distributed OS is easy to implement. Automatic parallelize compiler. OpenMP

Message passing Formal verification is easy (Blocking) No-side effect (Shared variable is side effect itsel

f) Small cost

OpenMP

#include <stdio.h>int main(){pragma omp parallel{ int tid, npes; tid = omp_get_thread_num(); npes = omp_get_num_threads(); printf(“Hello World from %d of %d\n”, tid, npes)}return 0;} Multiple threads are generated by using pragma. Variables declared globally can be shared.

Convenient pragma for parallel execution#pragma omp parallel {#pragma omp for for (i=0; i<1000; i++){ c[i] = a[i] + b[i]; }} The assignment between i and thread is automatical

ly adjusted in order that the load of each thread becomes even.

Automatic parallelizing Compilers Automatically translating a code for uniprocessors in

to multiprocessors. Loop level parallelism is main target of parallelizing. Fortran codes have been main targets

No pointers The array structure is simple

Recently, restricted C becomes a target language Oscar Compiler (Waseda Univ.), COINS

PC Clusters Multicomputers

Dedicated hardware (CPU, network) High performance but expensive Hitachi’s SR8000, Cray T3E, etc.

WS/PC Clusters Using standard CPU boards High Performance/Cost Standard bus often forms a bottleneck

Beowluf Clusters Standard CPU boards, Standard components LAN+TCP/IP Free-software A cluster with Standard System Area Network(SAN) like Myr

inet is often called Beowulf Cluster

PC clusters in supercomputingClusters occupies more than 80% of top 500

supercomputers in 2008/11Let’s check http://www.top500.org

SAN (System Area Network) for PC clusters Virtual Cut-through routing High throughput/Low latency Out of the cabinet but in the floor Also used for connecting disk subsystems

Sometimes called System Area Network Infiniband Myrinet Quadrics

GBEthernet: 10GB EthernetStore & ForwardTree based topologies

SAN vs. Ethernet

Infiniband Point-to-point direct serial interconnection. Using 8b/10b code. Various types of topologies can be supported. Multicasting/atomic transactions are supported. The maximum throughput

SDR DDR QDR

1X 2Gbit/s 4Gbit/s 8Gbit/s

4X 8Gbit/s 16Gbit/s 32Gbit/s

12X 24Gbit/s 48Gbit/s 96Gbit/s

Remote DMA (user level)

User

Kernel

Host I/F

Sender

Buffer

Data Source

KernelAgent

ProtocolEngine

Local Node

Network Interface

Buffer

Data Sink

ProtocolEngine

Remote Node

Network Interface

System Call

RDMAUserLevel

PC 　 Clusters with Myrinet (RWCP, using Myrinet)

RHiNET Cluster

NodeNode CPU: Pentium III 933MHzCPU: Pentium III 933MHz Memory: 1GbyteMemory: 1Gbyte PCI bus: 64bit/66MHzPCI bus: 64bit/66MHz OS: Linux kernel 2.4.18OS: Linux kernel 2.4.18 SCore: version 5.0.1SCore: version 5.0.1

RHiNET-2 with 64 nodesRHiNET-2 with 64 nodesNetworkNetworkOptical →Optical → 　　 Gigabit Gigabit EthernetEthernet

Cluster of supercomputers

Recent trend of supercomputers is connecting powerful components with high speed SANs like Infiniband

Roadrunner by IBM General purpose CPU + Cell BE

Grid Computing

Supercomputers which are connected with Internet can be treated virtually as a single big supercomputer.

Using middleware or toolkit to manage it. Globus toolkit

GEO (Global Earth Observation Grid) AIST Japan

Contest/Homework

Assume that there is an array x[100]. Write the MPI code for computing sum of all elements of x with 8 processes.