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Introduction to the Beowulf Design
Robert G. Brown
Duke University Physics Department
13 May 2003
- ``Tasks'' typically have both serial and parallel components.
- Parallel subtask completion time under ideal circumstances scales
like where is the number of parallel tasks undertaken (on e.g.
different processors) at the same time. ``Many hands make light work''.
- Parallel subtasks often (but not always) require interprocessors
communications (IPCs) between the subtasks. This communication time
adds to to the total and can take more or less time than the work
- All this is made formal in Amdahl's Law and quantitatively corrected
in books on parallel computation.
The speedup experienced running a task on processors is less
than or equal to:
where is the time program spends doing ``serial work'' and
is the time spend doing ``parallelizable work'' split up on
Limiting result, not horribly useful quantitatively except to tell you
when there is no point in parallelizing something. Can do much
For example, we can account for the time spent communicating between
processors, the time spent setting things up, and changes in the times
to perform various tasks with different algorithms. Defining things
we can obtain improved estimates of the speedup:
- The original single-processor serial time.
- The (average) additional serial time spent
doing things like IPC's, setup, and so forth, per processor, in all
- The original single-processor parallizable time.
- The (average) additional time spent by each
processor doing just the setup and work that it does in parallel. This
may well include idle time, which is often important enough to be
accounted for separately.
All You Need to Know
About Code Granularity
, (lots of work,
little communication) coarse grained. completely independent jobs
are ``embarrassingly parallel'' (EP). (e.g. Monte Carlo, data field
tremendously so) medium grained. (e.g. problems on a lattice (where the
lattice is partitioned among nodes with short range communications),
lattice gauge theory.)
fine grained. (e.g. - Cosmology, molecular dynamics with long
range interactions, hydrodynamics, computational fluid dynamics.)
Granularity typically is somewhat controllable. Network speed and
latency, scaling of computation to communication as a function of
problem size, CPU/memory speed, program organization all control
Fine grained tasks are ``bad'' for scaling to many nodes . Coarse
grained tasks are ``good''.
Beware nonlinearities! CPU/cache/memory/disk bottlenecks can create
``superlinear speedup'' and violate Amdahl's Law!
In all the figures below, (which sets our basic scale, if you
. In the first three figures
we just vary
for (fixed). Finally, the
last figure is , but this time with a quadratic
Goal is optimizing overall performance per dollar. The following
are appropriate for increasing fineness of program granularity:
- GRID (Network of clusters, supercluster). SGE or shell-level
tools. EP tasks, primarily.
- NOW (Network of Workstations) + e.g. Mosix, master/slave PVM, MPI,
shell-level tools or perl scripts permit double usage of all CPUs.
- COW (Cluster of Workstations) same as NOW but protects the network
a bit and isolates the compute resource from interactive humans and
GUIs. Most common Duke design?
- Beowulf (dedicated, single headed COW) + Scyld/clustermatic and
PVM/MPI. A totally isolated COW with (usually) a private network,
custom OS, and a single head.
These are suitable for increasingly fine granularity, at increasing cost
and decreasing general purpose utility.
Schematics for the general designs follow, first a ``true beowulf'' and
then a workstation cluster.
A True Beowulf
A Workstation Cluster
Node Design and Cost
Examples (Intrex-based estimate, YMMV):
- Tower case ($60), P4 motherboard with onboard PXE 100BT NIC and
video($120), 2.4 GH P4 ($200), 1 GB expensive DDR ($230), ``small''
hard disk ($100) =
- Same, in 1U or 2U rackmount case (add $200) = $910.
- Same in rackmount case, add 64 bit 1000BT NIC ($120) = $1030.
- Add $100 for three year service = $1130.
- Dual P4's in 2U rackmount case (add $330 for motherboard, $380
to go to 2 2.4 GHz P4 Xeons) = $1840.
- Same with Myrinet, 3 GB memory, large hard disk, fastest CPUs
$4000 and rising...
Dells will run perhaps 10% higher plus shipping. Pricewatch min might
save you 20%, BUT see later notes on ``Administrative Infrastructure''!
Per node pricing may have to absorb cost of rack(s) or heavy duty steel
shelving from Home Depot, screws, cable ties, and so forth, so estimate
10% more than your base minimum.
Turnkey clusters can make sense if you are building a very specialized
cluster and need help designing and installing it. A turnkey integrator
will typically resell the hardware components to you pretty much at
standard retail marked up to cover their ``integration fee'' for
designing the cluster, installing the clusterware on it, and so forth.
This ends up being anywhere from a 20% markup of OTC prices on up.
At Duke it will only VERY RARELY make sense to get a turnkey cluster.
That is because:
- We have our own, aggressively maintained, updated and automated
linux repository thanks to the Sethbot (clap, cheer, whistle).
- We have enough local expertise that one can usually get ``as
good'' a cluster setup for many EP to medium grained parallel tasks
using this repository plus some on-campus (free) consultation.
- It isn't that hard...
- Switched 100BT (standard, should be used ALSO in most
- Switched 1000BT. Good bandwidth. Relatively poor latency.
Relatively cheap in SMALL switches, more expensive for large switches.
- Myrinet. Excellent bandwidth. Excellent latency. Expensive as
hell, but cheaper than 1000BT for large networks (where big 1000BT
switches become VERY costly).
- Etc. There are more. I'm ignoring them out of sheer ignorance.
Parallel Program Support
- MPI (Message Passing Interface). API + library for writing
portable parallel programs with a message passing interface for IPC's.
Several versions available, LAM in Red Hat and on repository.
- PVM (Parallel Virtual Machine). API + library for writing
portable parallel programs that run across networks. My personal
favorite API (written as open source effort from beginning, not by a
consortium of massively parallel supercomputer vendors under
- Raw Sockets (yeah!)
- Remote Shells (e.g. rsh, ssh)
- Miscellaneous: Monitors, batch/queue systems, GUI's, scripts,
bproc, scyld, cod, more...
Simple Example: xep (PVM Mandelbrot Set)
- Mandelbrot set is iterated map that either ``escapes'' or doesn't.
- Colors mapped to steps until escape makes pretty pictures.
- Self-referential, fractal, infinitely fine structure as we
rubber-band down into set.
- Easily parallelizeable (coarse grained parallel).
On a good day, this will work as a demo...
Physical Infrastructure Requirements
- Space: Shelfmount ft/node, Rackmount
ft/node, blades ``different''. 1-2 CPUs/node, maybe
UPS. Heavy! Strong floors!
- Power: Guestimate 100W/CPU, better to measure.
Special wiring requirements for switching power supplies! Overwire!
- A/C: All power IN turns to heat and must come OUT.
1 Ton of A/C removes W. Again, need surplus to keep room
COOL, plus specific delivery/circulation/return design. Thermal kill?
- Network: Cable trays, patch panels, backbone ports on
copper or fiber. BOTH local network(s) for cluster AND connection to
- Etc: Decent lighting. Work bench and tools! Chairs
and carts. Monitor, keyboard. KVM switch? Jackets and ear protectors
or noise-reduction headphones plus music. Phone.
Physical Infrastructure Costs
- Anywhere from $400 to $5000 per node straight compute hardware
cost. Typically $1000/CPU ``reasonable'' memory non-bleeding edge
- Anywhere from $30 to $1000 (or more?) per node for network. In
some designs network will cost more than CPU!
- Amortized renovation costs. For example, $100,000 for space to
hold 100 nodes, over 10 years, is ballpark $150/node/year (including
cost of money).
- Recurring costs. $1 W/year for power/cooling, maybe rent or
physical space maintenance. 100 nodes at 100 W each cost at least
$10,000 year to run 24x7 for the year!
Note well that recurring costs for operating a node can compete with the
cost of the node! This favors getting relatively expensive nodes and
dumping nodes quickly when obsolete!
- Installation: Min: 15 min TOTAL/node (unpacking it
and racking it plus e.g. kickstart. Max: Any nightmarish thing you can
- Operational maintenance: Min: 1 hour per node per
year (OS upgrades, fixing ``rare'' hardware failures, new software).
Presumes automation of nearly everything (yum) and preexisting LAN (with
accounts, fileservers, etc.). Max: Any nightmarish thing you can
- LARGE Monitoring: Min: 20-30 minutes/day per cluster
Presumes syslog-ng, monitoring tools like ganglia or xmlsysd/wulfstat,
alert users. Max: A couple hours a day.
- LARGE User support: Min: 0 minutes a day if you have smart
users and a sucker rod handy to school the lazy. Max: Arrrrrggghh!
In summary, Min: 1 hour a day, on average, for a ``good'' 100+
node cluster; Max: full time job and then some for a ``bad'' cluster
(depending on luck, hardware reliability, your general admin skills,
your cluster admin skills, user support requirements, and the
availability of cluster expertise in a distributed support environment).
Total Cost of Ownership (TCO) can range from:
Wide range, provokes TCO fistfights in bars.
- $1000 (node) + $300 (power and A/C) + $100 (3 hours sysadmin
time) = $1400 per node for a three year expected lifetime; to
- $3000 (node) + $600 (power and A/C) + $450 (amortized share of
expensive renovation) + $800 (24 hours sysadmin time) $150 (amortized
share of four post smoked glass rack, UPS, = $5000 for the same three
Still, beowulfish clusters often yield staggering productivity
efficiency. Generally 3-10x more cost/benefit than comparable power
``big iron''. SO, literally everybody is buying or building them.
References and Resources
- http://www.phy.duke.edu/rgb/beowulf.php (see especially
my book on cluster engineering).
- http://www.phy.duke.edu/rgb/beowulf_intro_2003 (this
- http://www.phy.duke.edu/brahma/ (lots of resources, including
images of this talk)
- http://www.beowulf_underground.org/ (lots of resources)
- ``How To Build a Beowulf'', by Sterling, Becker, et. al.
- Online book on designing parallel programs by Ian Foster
at Argonne National Labs, http://www-unix.mcs.anl.gov/dbpp/
- ``Highly Parallel Computing'', by Amalsi and Gottlieb.
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Robert G. Brown