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Using PC clusters for scientific computing: do they really work?

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Migration of conventional supercomputer users (Cray, etc.) to less expensive platforms ... ASUS KN97X motherboard (440FX PCI chipset) 266MHz Pentium IIs (512KB cache) ... – PowerPoint PPT presentation

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Title: Using PC clusters for scientific computing: do they really work?

1
Using PC clusters for scientific computing do
they really work?

Roldan Pozo
Mathematics and Computational Sciences Division
NIST

2
Alternate Title

Supercomputing the view from below

3
NIST Activities

Fire Dynamics
Applied Economics
Polymer Combustion Research
DNA Chemistry
Applied Computational Chemistry
Reacting Flow Simulation
Microbeam analysis
Atmospheric and Chemometric Research

Analytical Mass Spectrometry for Biomolecules
Trace-Gas analysis
Neutron Activation Analysis
Plasma Chemistry
Thin-Film Process Metrology
Nanoscale Cryoelectronics
Computer Security
Computer-aided Manufacturing
Polymer Characterization
...

4
NIST Computing

Cray C-90
IBM SP2
SGI Origin 2000
Convex C3820
small workstation (Alpha, RS6K, etc.) clusters
small PC clusters

5
Parallel / Scalable / Distributed / Computing?

Thanks, but parallel computing is (still) hard...
dont have the
time
resources
development cycle
economic justfication
dont really need it

6
Flops is not the issue...

Development time
Turn-around time

7
The Big Supercomputing Maxim

The bigger the machine, the more you share it

8
From Big Iron to clusters...

Migration of conventional supercomputer users
(Cray, etc.) to less expensive platforms
are small clusters the answer?
care to parallelize your apps?

9
User Responses

Go away.
DM Been there, done that.
Parallel Computing failed.
Cant the compiler do that?

10
Alternate approach...

MASSIVELY SEQUENTIAL COMPUTING
personal compute server
mainly sequential jobs
some occasional small (2-8 processor) parallel
jobs

11
Sequential rules

Big applications are hardly ever run once.
Most simulations consist of many runs of the same
code with different input data.
Memory constraints? Buy more memory!

12
Benefits of a personal supercomputer

Dont have to share it with anyone!
Often reduced turn-around time
No batch queues, CPU limits, disk quotas, etc.
direct control over the resource
You get to decide how to best use it

JazzNet I
9 processors
Intel BaseTX Express Hub
JazzNet II
18 processors
Myrinet Gigabit network
8-port 3Com SuperStackII 3000TX fast ethernet
switch
16-port Bay Networks BayStack 350T fast ethernet
switch

14
Parallel adaptive multigrid (PHAML)(William F.
Mitchell, MCSD)

Adaptive multigrid for finite element modeling
2D elliptic partial differential equations
uses Fortran 90 and PVM/MPI
originally developed on the IBM SP2

15
PHAML performance
16
3D Helmholtz equation solver(Karin A. Remington,
MCSD)

Fast, direct method for solving elliptic PDEs via
matrix decomposition
Handles Dirichlet, Neumann, or periodic boundary
conditions

17
Helmoltz solver implementation

1D decomposition, f77/C, PVM MPI
FFT across processors
personalized all-to-all communication

18
3D Helmholtz performance
19
Optimal wing shape in viscous flows(Anthony J.
Kearsley, MCSD)

Optimization problem to minimize vorticity
CFD around trial shapes with constrained shape
methods
uses domain decomposition and domain embedding
hybrid constrained direct search method

20
Optimal wing shape performance
21
Phase-field algorithm for solidification
modeling(Bruce Murray, NIST/SUNY)

set of two time-dependent, nonlinear parabolic
PDEs
Fortran 77 Cray application
finite difference / ADI method

22
solidification modeling performance(1200x600
grid, 50 steps)
23
solidification modeling performance(1200x600
grid, 50 steps)
24
JazzNet Pentium II nodes