experiencing cluster computing

Experiencing Experiencing Cluster ComputingCluster Computing

Class 1

Introduction to Introduction to ParallelismParallelism

OutlineOutline

• Why Parallelism

• Types of Parallelism

• Drawbacks

• Concepts

• Starting Parallelization

• Simple Example

Why ParallelismWhy Parallelism

Why Parallelism – PassivelyWhy Parallelism – Passively

Suppose you are using the most efficient algorithm with an optimal implementation and the program still takes too long or does not even fit onto your machine?

Parallelization is the last chance.

Why Parallelism – InitiativelyWhy Parallelism – Initiatively

• Faster– Finish the work earlier

• Same work in shorter time

– Do more work• More work in the same time

• Most importantly, you want to predict the result before the event occurs

ExamplesExamples

Many of the scientific and engineering problems require enormous computational power. Following are the few fields to mention:

– Quantum chemistry, statistical mechanics, and relativistic physics

– Cosmology and astrophysics– Computational fluid dynamics and turbulence– Material design and superconductivity– Biology, pharmacology, genome sequencing, genetic

engineering, protein folding, enzyme activity, and cell modeling

– Medicine, and modeling of human organs and bones– Global weather and environmental modeling– Machine Vision

ParallelismParallelism

• The upper bound for the computing power that can be obtained from a single processor is limited by the fastest processor available at any certain time.

• The upper bound for the computing power available can be dramatically increased by integrating a set of processors together.

• Synchronization and exchange of partial results among processors are therefore unavoidable.

Computer ArchitectureComputer Architecture

4 categories:

SISD: Single Instruction Single Data

SIMD: Single Instruction Multiple Data

MISD: Multiple Instruction Single Data

MIMD: Multiple Instruction Multiple Data

Computer ArchitectureComputer Architecture

SISD SIMD MISD MIMD

Uniprocessor(Single processor

computer)

Vectorprocessor

Arrayprocessor

Shared Memory(Microprocessors)

Distributed Memory

(Microcomputers)

ClusterSMP NUMA

Processor Organizations

Multiprocessing Clustering

IS

CU CU CU CU

PU PU PU PU

Shared Memory

1 n-1 n2

21 n-1 n

ISISIS

DSDSDSDS

DS

LM LM LM LM

CPU CPU CPU CPU

Interconnecting Network

1 n-1 n2

21 n-1 n

DSDSDS

Distributed Memory – Cluster

Shared Memory –

Symmetric multiprocessors (SMP)

Parallel Computer ArchitectureParallel Computer Architecture

Types of ParallelismTypes of Parallelism

Parallel Programming ParadigmParallel Programming Paradigm

• Multithreading – OpenMP

• Message Passing– MPI (Message Passing Interface)– PVM (Parallel Virtual Machine)

Shared memory, Distributed memory

Shared memory only

ThreadsThreads

• In computer programming, a thread is placeholder information associated with a single use of a program that can handle multiple concurrent users.

• From the program's point-of-view, a thread is the information needed to serve one individual user or a particular service request.

• If multiple users are using the program or concurrent requests from other programs occur, a thread is created and maintained for each of them.

• The thread allows a program to know which user is being served as the program alternately gets re-entered on behalf of different users.

ThreadsThreads

• Programmers view:– Single CPU– Single block of memory– Several threads of action

• Parallelization– Done by the compiler

ParallelRegion

FORK

JOIN

Master Thread

Team of parallel threads

Thread 1 2 3 4

Fork-Join Model

Shared MemoryShared Memory

• Programmers view:– Several CPUs– Single block of memory– Several threads of action

• Parallelization– Done by the compiler

• Example– OpenMP

time

P1

P1 P2 P3Single threadedSingle threaded

Multi-threadedMulti-threaded

Process

Process P2

P3

Threads

Data exchange via shared memory

Parallel Region 1

!$OMP PARALLEL

!$OMP END PARALLEL

!$OMP PARALLEL

!$OMP END PARALLEL

Parallel Region 2

Master Thread

Team of parallel threads

Multithreaded ParallelizationMultithreaded Parallelization

Distributed MemoryDistributed Memory

• Programmers view:– Several CPUs– Several block of memory– Several threads of action

• Parallelization– Done by hand

• Example– MPI

time

P1

P1 P2 P3

P2

P3

Process 0

Process 1

Process 2

SerialSerial

Data exchange viainterconnection

Process

MessageMessagePassingPassing

DrawbacksDrawbacks

Drawbacks of ParallelismDrawbacks of Parallelism

• Traps– Deadlocks– Process Synchronization

• Programming Effort– Few tools support for automated parallelization and

debugging

• Task Distribution (Load balancing)

DeadlockDeadlock

• The earliest computer operating systems ran only one program at a time.

• All of the resources of the system were available to this one program.

• Later, operating systems ran multiple programs at once, interleaving them.

• Programs were required to specify in advance what resources they needed so that they could avoid conflicts with other programs running at the same time.

• Eventually some operating systems offered dynamic allocation of resources. Programs could request further allocations of resources after they had begun running. This led to the problem of the deadlock.

DeadlockDeadlock

• Parallel tasks require resources to accomplish their work. If the resources are not available, the work cannot be finished. Each resource can only be locked (controlled) by exactly one task at any given point in time.

• Consider the situation:– Two tasks need both the same two resources.– Each task manages to gain control over just one resource, but

not the other.– Neither task releases the resource that it already holds.

• It is called deadlock and the program will not terminate.

DeadlockDeadlock

Process

Process

ResourceResource

Dining PhilosophersDining Philosophers

• Each philosopher either thinks or eats.

• In order to eat, he requires two forks.

• Each philosopher tries to pick up the right fork first.

• If success, he waits for the left fork to become available.

Deadlock

Dining Philosophers DemoDining Philosophers Demo

• Problem– http://www.sci.hkbu.edu.hk/tdgc/tutorial/ExpClusterCo

mp/deadlock/Diners.htm

• Solution– http://www.sci.hkbu.edu.hk/tdgc/tutorial/ExpClusterCo

mp/deadlock/FixedDiners.htm

ConceptsConcepts

SpeedupSpeedup

Given a fixed problem size.

TS: sequential wall clock execution time (in seconds)

TN: parallel wall clock execution time using N processors (in seconds)

Ideally, speedup = N Linear speed up

N

S

T

Tspeedup

SpeedupSpeedup

• Absolute SpeedupSequential time on 1 processor/parallel time on N processors

• Relative SpeedupParallel time on 1 processor/parallel time on N processors

• Different because parallel code on 1 processor has unnecessary MPI overhead

–It may be slower than sequential code on 1 processor

Parallel EfficiencyParallel Efficiency

Effciency is a measure of process utilization in a parallel program, relative to the serial program.

Parallel Efficiency E: Speedup per processor

Ideally, EN = 1.

N

S

TN

TE

N

SpeedupE or

Amdahl’s LawAmdahl’s Law

It states that potential program speedup is defined by the fraction of code (f) which can be parallelized

If none of the code can be parallelized, f = 0 and the speedup = 1 (no speedup). If all of the code is parallelized, f = 1 and the speedup is infinite (in theory).

fspeedup

1

1

SNP

speedup

1

Amdahl’s LawAmdahl’s Law

Introducing the number of processors performing the parallel fraction of work, the relationship can be modeled by the equation where:

P: parallel fraction S: serial fraction N: number of processors

Amdahl’s LawAmdahl’s Law

When N ∞, Speedup = 1/S

Interpretation:

No matter how many processors are used, the upper bound for the speed up is determined by the sequential section.

SSpeedup

N

1lim

Amdahl’s Law – ExampleAmdahl’s Law – Example

If the sequential section of a program amounts 5% of the run time, then S = 0.05 and hence:

2005.0

1Speedup

Behind Amdahl’s LawBehind Amdahl’s Law

1. How much faster can a given problem be solved?

2. Which problem size can be solved on a parallel machine in the same time as on a sequential one? (Scalability)

Starting Starting ParallelizationParallelization

Parallelization – Option 1Parallelization – Option 1

• Starting from an existing, sequential program– Easy on shared memory architectures (OpenMP)– Potentially adequate for small number of processes (m

oderate speed-up)– Does not scale to large number of processes– Restricted to trivially parallel problems on distributed m

emory machines

Parallelization – Option 2Parallelization – Option 2

• Starting from scratch– Not popular, but often inevitable– Needs new program design– Increase complexity (data distribution)– Widely applicable– Often the best choice for large scale problems

Goals for ParallelizationGoals for Parallelization

• Avoid or reduce– synchronization– communication

• Try to maximize computational intensive sections.

Simple ExampleSimple Example

SummationSummation

Given an N-dimensional vector of type integer.

// Initialization //

for (int i = 0; i<len; i++)

vec[i] = i*i ;

// Sum Calculation //

for (int i = 0; i<len; i++)

sum += vec[i];

Parallel AlgorithmParallel Algorithm

1. Divide the vector in certain parts

2. In each CPU, initialize their own parts

3. Use global reduction to calculate the sum of the vector

OpenMPOpenMP

Compiler directives (#pragma omp) are inserted to tell the compiler to perform parallelization.

The compiler would be responsible for automatically parallelizing certain types of loops.

#pragma omp parallel for

for (int i=1; i<len; i++)

vec[i] = i*i;

#pragma omp parallel for reduction(+: sum)

for (int i=0; i<len; i++)

sum += vec[i];

MPIMPI

// in each process, do the initializationfor(int i=rank; i<len; i+=np)

vec[i] = i*i;// calculate the local sumfor(int i=rank; i<len; i+=np)

localsum += vec[i];// perform global reductionMPI_Reduce(&localsum, &sum, 1, MPI_INT, MPI_SUM, 0, MPI_COMM_WORLD);

no. of processors, np = 3

rank

0

1

2

sum

sum

sum

localsum

MPI_Reduce sum

vec

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