intro parallel computation

7/29/2019 Intro Parallel Computation

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An Overview of Parallel computing

Jayanti Prasad

Inter-University Centre for Astronomy & AstrophysicsPune, India (411007)

May 20, 2011


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Plan of the Talk

Introduction

Parallel platforms

Parallel Problems & Paradigms

Parallel Programming

Summary and conclusions


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High-level view of a computer system


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Why parallel computation ?

The speed with which a single processor can process data

(number of instructions per second, FLOPS) cannot beincreased indefinitely.

It is difficult to cool faster CPUs.Faster CPUs demand smaller chip size which again createsmore heat.

Using a fast/parallel computer :Can solve existing problems/more problems in less time.Can solve completely new problems leading to new findings.

In Astronomy High Performance Computing is used for twomain purposes

Processing large volume of dataDynamical simulations

References :Ananth Grama et. al. 2003, Introduction to Parallel Computing, Addison Wesley

Kevin Dowd & Charles R. Severance, High Performance Computing, OReily


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Performance

Computation time for different problems may depend on thefollowings:

Input-Output:- problems which require a lot of diskread/write.

Communication:- Problems, like dynamical simulations needa lot of data to be communicated among processors. Fast

inter-connects (Gig-bit Ethernet, Infiniband, Myrinet etc.) arehighly recommended.

Memory:- Problems which need a large amount of data tobe available in the main memory (DRAM), demand more

memory. Latency and bandwidth are very important.Processor:- Problems in which a large number or

computations have to be done.

Cache :- Better memory hierarchy (RAM,L1,L2,L3..) andefficient use of that benefits all problems.


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Performance measurement and benchmarking

FLOPS:- Computational power is measured in units ofFloating Point Operations Per Second or FLOPS (Mega, Giga,Tera, Peta,.. FLOPS).

Speed UP:-Speed Up =

T1

TN(1)

where T1 is the time needed to solve a problem on oneprocessor and Tn is the time needed to solve that on N

processors.


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Pi li


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Pipeline

If we want to add two numbers we need five computational steps:

If we have one functional unit for every step then the addition stillrequires five clock cycles, however, all the units being busy at thesame time, one result is produced every cycle. Reference : Tobias Wittwer 2005,An Introduction to Parallel Programming

V t P


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Vector Processors

A processor that performs one instruction on several data setsis called a vector processor.

The most common form of parallel computation is in the form

of Single Instruction Multiple Data (SIMD) i.e., samecomputational steps are applied on different data sets.

Problems which can be broken into small problems forparallelization are called embarrassingly parallel problems e.g.,

SIMD.

M lti P


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Multi-core Processors

That part of a processor which execute instructions in anapplication is called a core.

Note that in a multiprocessor system we can have eachprocessor having one core or more than one cores.

In general the number of hardware threads are equal to thenumber of cores.

In some processors one core can support more than onesoftware threads.

A multi-core processor presents multiple virtual CPUs to the

user and operating system which are not physically countablebut OS can give you clue (check /proc).

Reference : Darryl Gove, 2011, Multi-core Application Programming, Addison-Wesley


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Shared Memory System


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Shared Memory System

Distributed Memory system


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Distributed Memory system

Topology : Star, N-Cube, Torus ...

Parallel Problems


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Parallel Problems

Scalar Product

S =

N

i=1 A

iBi (2)

Linear-Algebra: Matrix multiplication

Cij =

M

k=1 A

ikBkj (3)

Integration

y = 4

10

dx

1 + x2(4)

Dynamical simulations

fi =N

j=1

mj(xj xi)

(|xj xi|)3/2(5)

Models of parallel programming: Fork Join


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Models of parallel programming: Fork-Join

Models of parallel programming: Master-Slave


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Models of parallel programming: Master-Slave

Synchronization


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Synchronization

More than one processors working on a single problem mustcoordinate (if the problem is not embarrassingly parallelproblem).

The OpenMp CRITICAL directive specifies a region of code

that must be executed by only one thread at a time.The BARRIER (in OpenMp, MPI etc.,) directivesynchronizes all threads in the team.

In pthreads Mutexes are used to avoid data inconsistencies

due to simultaneous operations by multiple threads upon thesame memory area at the same time.

art t on ng or ecompos ng a pro em: v e an


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art t on ng or ecompos ng a pro em: v e anconquer

Partitioning or decomposing a problem is related to the

opportunities for parallel execution.On the basis of whether we are diving the executions or datawe can have two type of decompositions:

Functional decompositionDomain decomposition

Load balance

Domain decomposition


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Domain decomposition

Figure: The left figure shows the use of orthogonal recursive bisection

and the right one shows Peano-Hilbert space filling curve for domaindecomposition. Note that parts which have Peano-Hilbert keys close toeach other are physically also close to each other.

Examples: N-body, antenna-baselines, sky maps (l,m)

Mapping and indexing


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Mapping and indexing

Indexing in MPI

MPI Comm rank(MPI COMM WORLD, &id);

Indexing in OpenMP

id = omp get thread num();

Indexing in CUDA

1 t id = t h r e a d Id x . x + b l o ck D i m . x * ( t h re a d I dx . y + b l o c k D im . y * t h r ea d I d x . z );2 b id = b l o ck I dx . x + g r i d Di m . x * b l o ck I dx . y ;3 n t hr e ad s = b l oc k Di m . x * b l oc k Di m . y * b l oc k Di m . z ;4 n bl oc ks = g ri dD im . x * g ri dD im . y ;

5 id = t id + n th re ads * b id ;

1 d i m3 d i m G ri d ( g r sz x , g r sz y ) , d i mB l o ck ( b l sz , b ls z , b l s z ) ; \ \2 V e c Ad d $ < < < d i mG r id , d i m B l oc k > > > $ ( ) ;

Mapping


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Mapping

A problem of size N can be divided among NP processors in

the following ways:A processor with identification number id get the databetween the data index istart and iend where

istart = idN

Np

(6)

iend = (id+ 1) N

Np(7)

Every processor can pick data skipping NP elements

for(i = 0; i< N; i+ = Np) (8)

Note that N/Np may not always an integer so keep loadbalance in mind.

Shared Memory programming : Threading Building Blocks


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S y p g g g g

Intel Threading building blocks (ITBB) is provided in the form

of C++ runtime library which and can run on any platform.

The main advantage of TBB is that it works at a higher levelthan raw threads, yet does not require exotic languages orcompilers.

Most threading packages require you to create, join, andmanage threads. Programming directly in terms of threadscan be tedious and can lead to inefficient programs becausethreads are low-level, heavy constructs that are close to thehardware.

TBB runtime library automatically schedules tasks ontothreads in a way that makes efficient use of processorresources. The runtime is very effective in load-balancing also.

Shared Memory programming : Pthreads


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y p g g

1 #include< p t h r e a d . h >2 void * p r i n t _ h e l l o _ w o r l d ( void * para m ) {3 long t id = (long) p a r a m ;4 p r i n t f ( " He ll o w or ld f ro m % ld ! \ n" , t i d ) ;5 }6

7 int m a i n (int a r g c , char * a r g v [ ] ) {8 p t h r ea d _ t m y t hr e a d [ N T H RE A D S ] ;9 long i ;

10 for( i =0 ; i < N TH RE AD S ; i ++ )11 p t h r e a d _ c r e a t e ( & m y t h r e a d [ i ] , N U L L , & p r i n t _ h e l l o _ wo r l d ,( void * ) i ) ;12 p t h r e a d _ e x i t ( N U L L ) ;13 return(0);14 }

Reference : David R. Butenhof 1997, Programming with Posix threads, Addison-Wesley

Shared Memory programming : OpenMp


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y p g g p p

No need to make any change in the structure of the program.

Need only three things to be done :

#include< omp.h >

#pragma omp parallel for shared () private ()-fopenmp when compiling

In general available on all GNU/Linux system by defaultReferences :Rohit Chandra et. al. 2001, Parallel Programming in OpenMP, Morgan KaufmannBarbara Chapman et. al. 2008, Using OpenMP, MIT Press

http://www.iucaa.ernet.in/jayanti/openmp.html

OpenMP: Example


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p p

1 #include< s t d i o . h >2 #include< o m p . h >3 int m ai n (int a r g c , char* a r g v [ ] ) {4 int n t hr e ad s , t id , n u m th r d ;5 / / s et t he n um be r of t hr ea ds6 o m p _ s e t _ n u m _ t h r e a d s ( a t o i ( a r g v [ 1 ] ) ) ;7

8 #pragma o m p p a r al l e l p r i va t e ( t i d )910 {11 t id = o m p_ g et _ th r ea d _n u m ( ); / / o b ta in t he t h re ad i d1213 n t hr e ad s = o m p_ g et _ nu m _t h re a ds ( ) ; / / f in d n u mb er o f t h re a ds1415 p r i n t f ( " \ tH el lo W or ld f ro m t h re ad % d o f % d \n ", t id , n t h re a d s ) ;16

17 }18 return(0);19 }

Distributed Memory Programming : MPI


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y g g

MPI is a solution for very large problemsCommunication

Point to PointCollective

Communication overhead can dominate computation and itmay be hard to get linear scaling.

Is distributed in the form of libraries e.g., libmpi,libmpich or inthe form of compilers e.g., mpicc,mpif90.

Programs have to be completely restructured.References :Gropp, William et. al. 1999, Using MPI 2, MIT Press

http://www.iucaa.ernet.in/jayanti/mpi.html

MPI : Example


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1 #include< s t d i o . h >2 #include< m p i . h >3 int m a i n (int a r g c , char * a r g v [ ] ) {4 int r an k , s iz e , l en ;5 char n a m e [ M P I _ M A X _ P R O C E S S O R _ N A M E ] ;

6 M P I _I n i t ( & a rg c , & a r g v ) ;7 M P I _ C o m m _ r a n k ( M P I _ C O M M _ W O R L D , & r a n k ) ;8 M P I _ C o m m _ s i z e ( M P I _ C O M M _ W O R L D , & s i z e ) ;9 M P I _ G et _ p r o ce s s o r _n a m e ( n a me , & l e n ) ;

10 p r in tf ( " He ll o w or ld ! I m % d of % d on % s\ n" , r ank , s iz e , n am e );11 M P I _ F i n a l i z e ( ) ;12 return(0);13 }

GPU Programming : CUDA


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Programming language similar to C with few extra constructs.

Parallel section is written in the form of kernel which isexecuted on GPU.

Two different memory spaces : one for CPU and another of

GPU. Data has to be explicitly copied back and forth.A very large number of threads can be used. Note that GPUcannot match the complexity of CPU. It is mostly used forSIMD programming.

References :

David B. Kirk and Wen-mei W. Hwu 2010, Programming Massively Parallel Processors, Morgan KaufmannJason Sanders & Edward Kandrot 2011, Cuda By examples, Addison-Wesley

http://www.iucaa.ernet.in/jayanti/cuda.html

CUDA : Example


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1 _ _ g l o b a l _ _ void f o r c e _ p p (float * p o s _ d , float * a c c _ d , int n ){2

3 int t id x = t h re a dI d x . x;4 int t id y = t h re a dI d x . y;5 int t id z = t h re a dI d x . z;67 int m yi d = ( b lo ck Di m . z * ( ti dy + b lo ck Di m . y * ti dx ) ) + t id z ;8 int n t hr e ad s = b l oc k Di m . z * b l oc k Di m . y * b l oc k Di m . x ;9

10 for(int i = m yid ; i < n ; i + = n th re ad s ){11 for(int l =0 ; l < n di m; l ++)

12 a c c_ d [ l + n d im * i ] = . . .;1314 } //15 _ _ s y n c t h r e a d s ( ) ;16 }17 / / t hi s w as t he d ev ic e p ar t

1 d i m3 d i m Gr i d ( 1 ) ;2 d i m 3 d i m B l o c k ( B L O C K _ S I Z E , B L O C K _S I Z E , B L O C K _ S I Z E ) ;3 c u d aM e m c py ( p o s _d , p os , n p ar t * n d i m *sizeof(float) , c u d a M e m c p y H o s t T o D e v i c e ) ;4 f o r ce _ p p < < < d im G ri d , d i m B l oc k > > > ( p o s_ d , a c c _d , n p a rt ) ;5 c u d a M e m c p y ( a c c , a c c _ d , n p a r t * n d i m * sizeof(float) , c u d a M e m cp y D e v ic e T o H o st ) ;

Nvidia Quadro FX 3700


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General Information for device 0 Name: Quadro FX 3700Compute capability: 1.1Clock rate: 1242000Device copy overlap: EnabledKernel execition timeout : Disabled

Memory Information for device 0 Total global mem: 536150016Total constant Mem: 65536Max mem pitch: 262144Texture Alignment: 256 MP Information for device 0 Multiprocessor count: 14Shared mem per mp: 16384Registers per mp: 8192

Threads in warp: 32Max threads per block: 512Max thread dimensions: (512, 512, 64)Max grid dimensions: (65535, 65535, 1)

Dynamic & static libraries


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Most of the open source software are distributed in the form of

source codes and from which libraries are created for the use.In general libraries are not portable.

One of the most common problems which a user face is dueto not linking libraries.

The most common way to create libraries is: source code object code library.

gcc -c first.cgcc -c second.car rc libtest.a first.o second.ogcc -shared -Wl,-soname, libtest.so.0 -o libtest.so.0 first.o second.o -lc

The above library can be used in the following waygcc program.c -L/LIBPATH -ltest

Dynamic library gets preference over static one.

Hyper threading


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Hyper-Threading Technology used in Intel R XeonTM

and

Intel R PentiumTM

4 processors, makes a single physicalprocessor appear as two logical processors to the operatingsystem.

Hyper-Threading duplicates the architectural state on eachprocessor, while sharing one set of execution resources.

sharing system resources, such as cache or memory bus, maydegrade system performance and Hyper-Threading can

improve the performance of some applications, but not all.

Summary


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It is not easy to make a super-fast single processor somulti-processor computing is the only way to get more

computing power.When more than one processors (cores) share the samememory shared memory programming is uded e.g.,pthread,OpenMp, itbb etc.

Shared memory programming is fast and it easy to get linear

scaling since communication is not an issue.When processors having their own memory are used forparallel computation, distributed memory programming isused e.g., MPI, PVM.

Distributed memory programming is the main way to solvelarge problems (when thousands of processors are needed).

General Purpose Graphical Processing Units (GPGPU) canprovide very high performance at very low cost, however,programming is somewhat complicated and parallelism is

limited to only SIMD.

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Thank You !

intro parallel computation

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