Introduction of Cluster and (KBRIN) Computational Cluster Facilities

Xiaohui Cui

CECS Department

University of Louisville

X0cui001@uofl.edu

09/03/2003

Introduction to Cluster Technology

What is a Beowulf ?

Kentucky Biomedical Research Infrastructure Network

MPI Programming on KBRIN cluster

People always wants to have faster computer

• Normal users

– Play game faster

– Play music better, watch movies better

• Science and Engineer

– Solving larger and more complex science and engineering problems using computer modeling, simulation and analysis

How to make a computer faster?

• Make a faster chip!

– reduce feature size

– better architecture, better memory subsystem

• VLIW , Super scalar, vector support, DSP instruction (MMX, 3DNow)

• SDRAM, NVRAM

• Uni-processor speed is still limited by speed of light

• Alternate technologies

– Optical

– Bio

– Molecular

How to make a computer faster?

• Using multiple processors to solve a single problem

– Divide problem into many small pieces

– Distributed these small problems to be solved by multiple processors simultaneously

• This technique is called Parallel ProcessingParallel Processing

Parallel computer

• Parallel computer is a special computer with

– High Speed I/O , Large memory, multiple processing units, fast communication network

• Every modern supercomputer is also a parallel computer

CPU CPU CPU CPU

High Speed Network

Fastest Supercomputer in the world

• Intel ASCI Red at Sandia National Laboratory

• 9216 Pentium Pro Processors

• 2.3 Teraflops performance

But Supercomputer will cost you 100 millions, how to get enough money to buy one?

• Using PC Cluster is a low cost solution to this problem

• A Cluster system is

– Parallel multi-computer built from high-end PCs and conventional high-speed network.

Introduction to Cluster Technology

Why cluster computing?

• Scalability

– Build small system first, grow it later.

• Low-cost

– Hardware based on COTS model (Component off-the-shelf)

– Software based on freeware from research community

• Easier to maintain

• Vendor independent

Different kinds of PC cluster

• High Performance Computing Cluster

• Load Balancing

• High Availability

The Beginning

• Thomas Sterling and Donald Becker CESDIS, Goddard Space Flight Center, Greenbelt, MD

• Summer 1994: built an experimental cluster

• Called their cluster Beowulf

• 16 x 486DX4, 100MHz processors

• 16MB of RAM each, 256MB in total

• Channel bonded Ethernet (2 x 10Mbps)

• Not that different from our Beowulf

Current Beowulfs

• Faster processors, faster interconnect, but the idea remains the same

• Cluster database: http://clusters.top500.org/

• Super cluster: 2300 processors, 11 TFLOPS peak

What is a Beowulf ?

• Runs a free operating system (not Wolfpack, MSCS)

• Connected by high speed interconnect

• Compute nodes are dedicated (not Network of Workstations)

•It’s cheap!

•Our Beowulf, 32 processors, 32GB RAM: $50,000

•The IBM SP2 cluster cost many millions

•Everything in a Beowulf is open-source and open standard - easier to manage/upgrade

Why Beowulf?

Essential Components of a Beowulf

• Processors

• AMD and Intel

• Memory

• DDR RAM

• RDRAM

• Interconnect

• Fast Ethernet

• Gigabit Ethernet

• Myrinet

• Software

Free cluster OS and management software

• OS• Linux• FreeBSD

• Cluster Management • Oscar: http://oscar.sourceforge.net/• Rocks: http://rocks.npaci.edu• MOSIX: http://www.mosix.org/

DIY Cluster

White Box Desktop

Cheap 2.8 GHz Pentium 4 for $1000 Very low margins

Expandable 4-6 PCI slots 3-5 disk drives

Low density 16 processor in on rack (on shelves)

Quality 90 - 365 day warrantees

Commercial designed Cluster

Brand Name Servers

Expensive Up to double equivalent desktop hardware

High density Rack mountable 64 processors in one rack Blades

Quality 3 year warrantee

Throw away machine when out of warrantee Good thermal design

Minimum Components

x86 server

Local HardDrive

Power

Ethernet

Cluster Advantages

• Error isolation: separate address space limits contamination of error

• Repair: Easier to replace a machine without bringing down the system than in an shared memory multiprocessor

• Scale: easier to expand the system without bringing down the application that runs on top of the cluster

• Cost: Large scale machine has low volume => fewer machines to spread development costs vs. leverage high volume off-the-shelf switches and computers

• Amazon, AOL, Google, Hotmail, Inktomi, WebTV, and Yahoo rely on clusters of PCs to provide services used by millions of people every day

Cluster Drawbacks

• Cost of administering a cluster of N machines ~ administering N independent machines vs. cost of administering a shared address space N processors multiprocessor ~ administering 1 big machine

• Clusters usually connected using I/O bus, whereas multiprocessors usually connected on memory bus

• Cluster of N machines has N independent memories and N copies of OS, but a shared address multi-processor allows 1 program to use almost all memory

Google company 2001 cluster reliability statistic

• For 6000 PCs, 12000 HD, 200 EN switches

• ~ 20 PCs will need to be rebooted/day• ~ 2 PCs/day hardware failure, or 2%-3% / year

– 5% due to problems with motherboard, power supply, and connectors

– 30% DRAM: bits change + errors in transmission (100 MHz)– 30% Disks fail– 30% Disks go very slow (10%-3% expected BW)

• 200 EN switches, 2-3 fail in 2 years

Kentucky Biomedical Research Infrastructure Network (KBRIN) Computational Cluster Facilities

Dual AMD 2400 Workstation2 GB memory80 GB hard drive100 mb and 1 gb NIC

Dual AMD 2400 Workstation2 GB memory80 GB hard drive100 mb and 1 gb NIC

Dual AMD 2400 Workstation2 GB memory80 GB hard drive100 mb and 1 gb NIC

Dual AMD 2400 Workstation2 GB memory80 GB hard drive100 mb and 1 gb NIC

HPLaser Printer

KBRIN Supercomputer

(16) Compute Nodes

Dual AMD 2400 2 GB memory40 GB hard drive1 gb NIC

KVM Switch

Monitor, Keyboard

Master NodeDual AMD 2400 2 GB memory CD RW drive(4) 70 GB hard drives100 mb and 1 gb NIC

24 Port Gb Ethernet Switch

Master Backup System

Dual AMD 2400 Workstation2 GB memory(4) 70GB hard drives100 mb NIC

Campus Ethernet Network

100

mb

Eth

ern

et

100 mb Ethernet

Gig

abit

Eth

ern

et

CECS DepartmentBioinformatics Laboratory

KBRIN Computational ClusterDahlem Supercomputer

Laboratory

KBRIN Project Office

Elb 4/1/2003

Web Srvr / Bkp masterDual AMD 2400 2 GB memory CD RW drive(4) 70 GB hard drives100 mb and 1 gb NIC

Programming a Cluster

• Cluster power comes from parallel processing

– Large task is decomposed to a set of small tasks

– These tasks are executed by a set of processes on multiple nodes

• Programming model based on Message Passing

– These process communicate by exchanging message which consists of data and synchronization information

Programming environments

• Threads (PCs, SMPs, NOW..)

– POSIX Threads

– Java Threads

• MPI

– http://www-unix.mcs.anl.gov/mpi/mpich/

• PVM

– http://www.epm.ornl.gov/pvm/

MPI

Message Passing Interface v1.1, v2.0 Standard for high performance message passing on

parallel machines http://www-unix.mcs.anl.gov/mpi/

Supports GNU C, Fortran 77 Intel C, Fortran 77, Fortran 90 Portland Group C, C++, Fortran 77, Fortran 90

Requires site license

PVM

Parallel Virtual Machines v3.4.3 Message passing interface for heterogeneous

architectures Supports over 60 variants of UNIX Supports Windows NT

Resource control and meta computing Fault tolerance http://www.csm.ornl.gov/pvm/

MPI Programming

243

5 5

687

8 1

2 3 4 6

8 9 0 2

2 4 8 9

556

6 8

6 7 8 9

5 6 6 71 5 6 9

2 3 8 9

2 3 5 5

4 9 1 1

4 3 4 1

2 8 8 9

4 4 3 1

1 2 7 8

Sum(A)=?A

MPI Programming

243

5 5

687

8 1

2 3 4 6

8 9 0 2

2 4 8 9

556

6 8

6 7 8 9

5 6 6 71 5 6 9

2 3 8 9

2 3 5 5

4 9 1 1

4 3 4 1

2 8 8 9

4 4 3 1

1 2 7 8

Sum(A1,A2,A3,A4)=?

A1 A2

A3 A4

MPI Programming

Master Slaves

Master

Request

Output

MPI Programming

/* Algorithm for the master program */

initialize the array `items'.

 /* send data to the slaves */

for i = 0 to 3

Send items[25*i] to items[25*(i+1)-1] to slave Pi

end for

 /* collect the results from the slaves */

for i = 0 to 3

Receive the result from slave Pi in result[i]

end for

 /* calculate the final result */

sum = 0

for i = 0 to 3

sum = sum + result[i]

end for

 print sum

MPI Programming

/* Algorithm for the slave program */

 

Receive 25 elements from the master in some array say `items'

 

/* calculate intermediate result */

sum = 0

for i = 0 to 24

sum = sum + items[i]

end for

 

send `sum' as the intermediate result to the master

Run MPI Program on the Cluster

• MPI C compiler: mpicc

• MPI job submit software: PBS

• The PBS command used for submit MPI job:

• Mkpbs

• Qsub

• Rps

Portable Batch System

Three standard components to PBS MOM

Daemon on every node Used for job launching and health reporting

Server On the frontend only Queue definition, and aggregation of node

information Scheduler

Policies for what job to run out of which queue at what time

Connect cluster from your computer

Free X-windows Server on windows

http://www.cygwin.com/