Building a Low-Cost Supercomputer

Dr. Tim McGuireSam Houston State University

ACET 2000Austin, TX

NO

Acknowledgments

Most treatments of cluster computing (including this one) are heavily based on the seminal work of Greg Pfister (IBM Research, Austin,) In Search of Clusters

The concept of Beowulf clusters originated with Donald J. Becker and Thomas Sterling at the Center of Excellence in Space Data and Information Sciences, NASA Goddard Space Flight Center

Introduction

There are three ways to do anything faster:

Work harder "Crunch Time" is familiar to all of us

Work smarter Better to find a way to reduce the work needed

Get help Certainly works, but we all know about committees ...

In a computer ...

Working Harder Get a faster processorWorking Smarter Use a better algorithmGetting Help Parallel processing

Working Harder -- Faster Processors

The effect of faster processors is astonishing The effective speed of the x86 family of

processors has increased nearly 50% per year

RISC architectures have sustained a 60% annual cumulative growth rate

These trends will likely continue for the foreseeable future

Working Smarter -- Better Algorithms

The increases in speed made possible by better algorithms dwarf the accomplishments of faster hardware

Binary search on 1 billion items takes 30 comparisons, versus a maximum of one billion comparisons using linear search

Getting Help -- Parallel Processing

Covert parallel processing pipelining, vector processing, etc.

really equivalent to faster hardware

Overt parallelismDone via software

"Parallelism is the wave of the future -- and always will be"

Early Attempts at Parallelism

Von Neumann thought it was too hard, and gave us the "Von Neumann bottleneck"

60's ILLIAC IV project was the first great attempt at parallel processing (as well as trying to advance circuit and software technology.)

Japanese Fifth Generation Project launched another wave, including the Grand Challenge problems

Microprocessor Revolution

Microprocessors have had a superior price/performance ratio

"All you have to do is gang a whole bunch of them together"

The problem is "All you also have to do is program them to work together"

Programming costs much more than hardware

Highly Parallel Computing

Finally, (early 90's) microprocessors became fast and powerful enough that a practical-sized aggregation of them seemed the only feasible way to exceed supercomputer speeds

Even Cray Research (T3D) got into the act

"Lowly" Parallel Processing

Mid-to-late 90's -- military downsizing (among other things) caused funding to dry up

However … Microprocessors kept getting faster … a lot faster

With overall performance doubling each year, in 4 years what needed 256 processors can be done with 16 instead.

System availability became a mass market issueSince computers are so cheap, buy two (or more) for

redundancy in case one fails and use them both, interconnected by a network

SMP -- One Form of "Cheap" Parallelism

Symmetric multiprocessors have been around for some time and have certain advantages over clusters Typically, these have been shared

memory systems -- few communication problems

The Big Distinction -- Programming

How you program SMP systems is substantially different from programming clusters: Their programming models are different

If you explicitly exploit SMP in an application, it's essentially impossible to efficiently exploit clusters in the same program

Why Clusters?

The Standard LitanyWhy Now ?Why Not Now?

The Standard Litany

PerformanceAvailabilityPrice/Performance RatioIncremental GrowthScalingScavenging

Performance

No matter what form or measure of performance one is seeking -- throughput, response time, turnaround time, etc., it is straightforward to claim that one can get even more of it by using a bunch of machines at the same time.

Only occasionally does one hear the admission that a "tad bid" of new programming will be needed for anything to work correctly.

Availability

Having a computer shrivel up into an expensive paperweight can be a lot less traumatic if it's not unique, but rather one of a herd.

The work done by the dear departed sibling can be redistributed among the others (fail-soft computing)

Price/Performance Ratio

Clusters and other forms of computer aggregation are typically collections of machines that individually have very good performance for their price.

The promise is that the aggregate retains the price/performance of its individual members.

Incremental Growth

To the degree that one really does attain greater performance and availability with a group of computers, one should be able to enhance both by merely adding more machines.

Replacing machines should not be necessary.

Scaling

"Scalable" is, unfortunately, a buzzword

What it does deal with is how big a computer system can usably get.

It is a crucial element in the differentiation between clusters and symmetric multiprocessors.

Scavenging

"Look at all those unused CPU cycles spread across all the desktops in our network…"

Unused cycles are free.However, how do you get and

manage them? -- this complicates cluster support very significantly

The Benefits are Real

But, how does one take advantage of it?

The hardware provides the potential.The fulfillment lies in the software,

and unfortunately, software isn't riding the exponential growth curve.

Why Now?

Three Trends Fat Boxes -- very high performance

microprocessors Fat Pipes -- standard high-speed

communication Thick Glue -- standard tools for

distributed computingOne Market Requirement

High Availability

Fat Boxes

Microprocessors have kept, and will keep getting faster.

Supercomputers in the classic style are extinct for practical purposes

Mass-market, inexpensive microprocessors have crawled up the tailpipe of the workstation market just like workstations crawled up the tailpipe of minicomputers and mainframes earlier.

There are no more supercomputers, there is only supercomputing.

Fat Pipes

Commodity off the shelf (COTS) networking parts have achieved communication performance that was only previously possible with expensive, proprietary techniques

Standardized communication facilities such as ATM - Asynchronous Transmission Mode Switched Gigabit Ethernet FCS -- Fibre Channel Standard

Performance of Gigabytes per second are possible.

Thick Glue

Standard tools for distributed computing such as TCP/IP

Intranets, the Internet, and the World Wide Web

Tool sets for distributed system administration

PVM (Parallel Virtual Machine) and MPI (Message Passing Interface)

Requirement for High Availability

Nobody has ever wanted computers to break.

However, never before has high availability become a significant issue in a mass market computer arena.

Clusters are uniquely capable of answering the need of both sides of the spectrum and are much cheaper than hardware based fault-tolerant approaches.

Why Not Now?

If they're so good, why haven't clusters become the most common mode of computation? Lack of "single system image" software Limited exploitation

Lack of Single System Image Software

Replacing a single large computer with a cluster means that many systems will have to be managed rather than one.

Their distributed management tools are tools, not turnkey systems

50% of the cost of a computer system is staffing, rather than hardware, software, or maintenance

Limited Exploitation

Only relatively few types of subsystems now exploit the ability of clusters to provide both scalable performance and high availability.

This is a direct result of substantial difficulties that arise in parallel programming.

The problem is not hardware, it's software

An Exception

For one kind of parallel system, the software issues have been addressed to a large degree: The symmetric multiprocessor (SMP)

It of necessity requires a single system image

Definitions, Distinctions, and Comparisons

DefinitionDistinction from Parallel SystemsDistinctions from Distributed

SystemsComparisons and Contrasts

Definition

A cluster is a type of parallel or distributed system that: consists of a collection of interconnected

stand-alone computers, and is used as a single, unified computing

resourceWe define them as a subparadigm of

distributed (or parallel) systems

Distinction from Parallel Systems

A useful analogy: This is A Dog (a single

computer)

A Pack of Dogs

And this is a pack of dogs (running in parallel)

(a cluster)

A Savage Multiheaded Pooch

… or, pardon the abbreviation, "SMP" (This pooch is no relation to Kerberos (Cerberus

in Latin) that guards both the gates of Hades and distributed systems -- He only has three heads.)

Dog Packs and SMPs are Similar

Both are more potent than just plain dogs

They can both bring down larger prey than a plain single dog.

They eat more and eat faster than a single dog

Dog Packs and SMPs are Different

ScalingAvailabilitySystem ManagementSoftware Licensing

Scaling Differences

The Savage Multiheaded Pooch can take many bites at once

What happens when it tries to swallow? It needs a larger throat, stomach, intestines, etc. Similarly, to scale SMPs, you must beef up the

entire machineWhen you add another dog to a dog pack,

you add a whole dog. You don't have to do anything to the other dogs. Likewise, clusters

Availability

If an SMP breaks a leg … "that dog won't hunt" … no matter

how many heads it has.If a member of the pack is injured,

the rest of the pack can still bring down prey.

System Management

You only have to walk a SMP once. It takes a good deal more effort to

train a pack of dogs to behave.With the SMP, all you have to do is

get the heads to learn basic cooperation (and that should be built into the operating system.)

Licensing (Dogs or Software)

If you get a license for an SMP, you'll probably only need one license

For an cluster of dogs, you'll need one per dog

Distinctions from Distributed Systems

The distinctions of clusters from distributed systems is not as clear (and a lot of people confuse the two.)

We'll try. The salient points are: Internal Anonymity Peer Relationship Clusters as part of a Distributed System

Internal Anonymity

Nodes in a distributed system necessarily retain their own individual identities

The elements of a cluster are usually viewed from outside the cluster as anonymous Internally, they may be differentiated, but

externally the jobs are submitted to the cluster, not, for example, to cluster node #4

Peer Relationship

Distributed systems use an underlying communication layer that

is peer-to-peer at a higher level, they are often organized

into a client-server paradigmClusters

underlying communication is peer-to peer organization is also peer-to-peer (with some

minor exceptions)

Clusters as part of a Distributed System

Clusters usually exist in the context of a distributed system

In this case, they are viewed by the distributed system as a single node For example, the cluster could server as a

compute engine It also could serve as, say, a DBMS server in

the client-server paradigm (but that's not the organization we want to consider in this presentation)

Beowulf Clusters

The Beowulf project was initiated in 1994 under the sponsorship of the NASA HPCC program to explore how computing could be made "cheaper better faster".

They termed this PoPC -- a Pile of PCs

The "Pile of PCs" Approach

Very similar to COW (cluster of workstations) and shares the roots of NOW (network of workstations,) but emphasizes: COTS (commodity off the shelf)

components dedicated processors (rather than

scavenging cycles from idle workstations) a private system area network (enclosed

SAN rather than exposed LAN)

What Beowulf Adds

Beowulf adds to the PoPC model by emphasizing no custom components easy replication from multiple vendors scalable I/O a freely available software base using freely available distributed

computing tools with minimal changes a collaborative design

Advantages of the Beowulf Approach

No single vendor owns the rights to the product -- not vulnerable to single vendor decisions

Approach permits technology tracking -- using the best, most recent components at the best price

Allows "just in place" configuration -- permits flexible and user driven decisions

Software for Beowulf

Exploits readily available, usually free software systems

These systems are as sophisticated, robust, and efficient as commercial-grade software

Derived from community-wide collaborations in operating systems, languages, compilers, and parallel computing libraries

Operating Systems, etc.

Two of the operating systems used are Linux (Slackware, RedHat, and Debian

distributions are all used) FreeBSD

Both have commercial distributors and support full X Windowing support a variety of shells a variety of quality compilers message passing libraries, such as PVM and MPI

Beowulf Architecture

Beowulf clusters have been assembled around every new generation of commodity CPUs since the first 100 MHz 486DX4 in 1994 The idea here is to use fast but cheap CPUs

We also need to interconnect them with a private network that is fast but cheap Originally used channel-bonded 10Mbit/sec Ethernet

with multiple Ethernet cards per CPU because the 100 MHz processors were faster than the network

When 100Mbit/sec Ethernet cards and switches became available, channel bonding was discarded

Alternatives

Mostly, Intel 80x86 CPUs have been used, but Beowulf-class clusters have been constructed from other chips such as the DEC Alpha

Fast Ethernet is most commonly used, but some use 1Gb/sec Ethernet or Myrinet (about 2.5Gb/sec) where performance is worth the much higher cost

Topology

Small systems (< 24 nodes) have a simple topology -- a single switch

(If price outweighs performance, a hub may be used instead of a switch)

Node

Switch

Connecting to the Outside World

If the switch is smart (read expensive) it may be connected directly to the LAN

Most often, however, one node of the cluster has a second (slower) network card connecting it to the LAN

Larger Systems

Beyond 24 nodes, suitable switches just do not exist for a single-switch solution

A two level tree with (non-leaf) nodes of 16-way Ethernet can handle over 200 processors

If locality can be exploited (big problem) there is no major performance hit

For system wide random communication, the root node switch can be a severe bottleneck

Example of a Larger System -- The Hive at NASA GSFC

http://newton.gsfc.nasa.gov/thehive

Beowulf at SHSU -- Bubbawulf

Bubbawulf consists of 8 nodes Pentium 350 with 64MB RAM Main node has a 4GB disk Other 7 nodes are headless and diskless Interconnected through a Cisco 2900

switch (100Mb full-duplex switched Ethernet network)

The 7 (beowulf2 - beowulf8) mount their file systems off the main node via NFS

Bubbawulf was constructed early in 1999 at a total cost of about $15,000 and will eventually be upgraded to the maximum of 24 nodes

Cost now would be about 1/2 of original

For more on Bubbawulf, see http://beowulf.shsu.edu

Cheaper Clusters

The WTAMU Beowulf Project, 1998 The "Buffalo" CHIP (Cluster of

Hierarchical Integrated Processors) http://wtfaculty.wtamu.edu/~rmashburn Total cost < $2,500

Hardware

16 port Fast Ethernet switch ($1000)Node 0 (Scavenged -- my 1995 desktop)

Intel Pentium 90 processor 16 MB RAM 1.0 GB Hard drive 3COM 3c905b 10Mbs Ethernet card

(connection to outside world) Linksys LNE100TX 100Mbs Ethernet card 8x CD-ROM

Hardware

Nodes 1-3 ($500 each -- mail order)Intel Pentium 200 processors32 MB RAM3.2 GB hard drivesLinksys LNE100TX 100Mbs Ethernet

card40x CD-ROM

Software

Operating System RedHat Linux 6.0

Message Passing Interface LAM MPI version 6.3-3b

Free Clusters

The epitome of clusters is the "Stone Soup-ercomputer" at Oak Ridge National Laboratories

A group of physicists with no budget built a Beowulf cluster from cast-off PCs and outdated network hardware

The Stone Soupercomputer

How to Build a "Free" Beowulf

Gather a bunch of machines that are considered "too slow" to run Microsoft software Typically these might be older Pentiums in the

90 - 233 MHz range -- they need not be identical 16 MB RAM is probably the minimal good

amount, 32 MB is better Hard drives are nice -- diskless stations are

possible, but harder to set up -- 1GB is plenty big A CD-ROM simplifies hardware installation You'll need at least one monitor and keyboard

Gather Network Hardware

Fast Ethernet switches are nice, but not usually available at low cost

Ethernet hubs are inexpensive, possibly free if older 10Mbs technology (10baseT)

10base2 Ethernet is slow, but cheap because it doesn't require a hub or switch You will take a big performance hit with the

slower technology

What Next?

After you get the hardware set up, start installing the software

Usually Linux is the OS of choice Set up each node as a stand-alone systemLet them know about each other by

assigning IP addresses (192.168.0.x is a good choice) in /etc/hosts

Install communication software (MPI or PVM)

How Does One Program a Beowulf?

The short answer is Message Passing, a technique originally developed for distributed computing

The Beowulf architecture means that message passing is more efficient -- it doesn't have to compete with other traffic on the net

Other techniques are being explored -- People are just now looking at Java

Message Passing Software

PVM (parallel virtual machine) was first Developed at Oak Ridge labs Very widely used (free)

Berkeley NOW (network of workstations) project

More Recent Message Passing Work

• MPI (Message-passing Interface)• Standard for message passing

libraries• Defines routines but not

implementation• Very comprehensive• Version 1 released in 1994 with 120+

routines defined• Version 2 now available

IEEE Task Force on Cluster Computing

• Aim to foster the use and development of clusters

• Obtained IEEE approval early 1999

• Main home page: http://www.dgs.monash.edu.au/~rajkumar/tfcc

Conclusions

• Cluster computing offers a very attractive cost effective method of achieving high performance

• Promising future

Quote: Gill wrote in 1958(quoting papers back to 1953):

“ … There is therefore nothing new in the basic idea of parallel programming, but only its application to computers. The author cannot believe that there will be any insuperable difficulty in extending it to computers. It is not to be expected that the necessary programming techniques will be worked out overnight. Much experimenting remains to be done. After all, the techniques that are commonly used in programming today were only won at the cost of considerable toil several years ago. In fact the advent of parallel programming may do something to revive the pioneering spirit in programming which seems at the present to be degenerating into a rather dull and routine occupation.”

Gill, S. (1958), “Parallel Programming,” The Computer Journal (British) Vol. 1, pp. 2-10.