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Flexible Control of Data Transfer between Parallel ProgramsPowerPoint Presentation

Flexible Control of Data Transfer between Parallel Programs

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### Flexible Control of Data Transfer between Parallel Programs

Joe Shang-chieh Wu

Alan Sussman

Department of Computer Science

University of Maryland, USA

Corona and solar wind

Rice convection model

Global magnetospheric MHD

Thermosphere-ionosphere model

Grid 2004

What is the problem?

- Coupling existing (parallel) programs
- for physical simulations more accurate answers can be obtained
- for visualization, flexible transmission of data between simulation and visualization codes

- Exchange data across shared or overlapped regions in multiple parallel programs
- Couple multi-scale (space & time) programs
- Focus on multiple time scale problems (when to exchange data)

Grid 2004

Roadmap

- Motivation
- Approximate Matching
- Matching properties
- Performance results
- Conclusions and future work

Grid 2004

Is it important?

- Petroleum reservoir simulations – multi-scale, multi-resolution code
- Special issue in May/Jun 2004 of IEEE Computing in Science & Engineering
“It’s then possible to couple several existing calculations together through an interface and obtain accurate answers.”

- Earth System Modeling Framework
several US federal agencies and universities. (http://www.esmf.ucar.edu)

Grid 2004

Solving multiple space scales

- Appropriate tools
- Coordinate transformation
- Domain knowledge

Grid 2004

Matching is OUTSIDE components

- Separate matching (coupling) information from the participating components
- Maintainability – Components can be developed/upgraded individually
- Flexibility – Change participants/components easily
- Functionality – Support variable-sized time interval numerical algorithms or visualizations

- Matching information is specified separately by application integrator
- Runtime match via simulation time stamps

Grid 2004

Ap1.Sr0

Ap0.Sr4

Ap2.Sr0

Ap0.Sr5

Ap4.Sr0

Separate codes from matchingExporter Ap0

Configuration file

Importer Ap1

Grid 2004

Matching implementation

- Library is implemented with POSIX threads
- Each process in each program uses library threads to exchange control information in the background, while applications are computing in the foreground
- One process in each parallel program runs an extra representative thread to exchange control information between parallel programs
- Minimize communication between parallel programs
- Keep collective correctness in each parallel program
- Improve overall performance

Grid 2004

Approximate Matching

- Exporter Ap0 produces a sequence of data object A at simulation times 1.1, 1.2, 1.5, and 1.9
- Importer Ap1 requests the same data object A at time 1.3
- Is there a match for [email protected]? If Yes, which one and why?

Grid 2004

Supported matching policies

<importer request, exporter matched, desired precision> = <x, f(x), p>

- LUB minimum f(x) with f(x) ≥ x
- GLB maximum f(x) with f(x) ≤ x
- REG f(x) minimizes |f(x)-x| with |f(x)-x| ≤ p
- REGU f(x) minimizes f(x)-x with 0 ≤ f(x)-x ≤ p
- REGL f(x) minimizes x-f(x) with 0 ≤ x-f(x) ≤ p
- FASTR any f(x) with |f(x)-x| ≤ p
- FASTU any f(x) with 0 ≤ f(x)-x ≤ p
- FASTL any f(x) with 0 ≤ x-f(x) ≤ p

Grid 2004

Experimental setup

Question : How much overhead introduced by runtime matching?

- 6 PIII-600 processors, connected by channel-bonded Fast Ethernet
- utt = uxx + uyy + f(t,x,y), solve 2-d diffusion equation by the finite element method.
- u(t,x,y) : 512x512 array, on 4 processors (Ap1)
- f(t,x,y) : 32x512 array, on 2 processors (Ap2)
- All data in Ap2 is sent (exported) to Ap1 using matching criterion <REGL,0.05>
- Ap1 receives (imports) data with 3 different scenarios. 1001 matches made for each scenario (results averaged over multiple runs)

Grid 2004

Experiment result 3

- Fastest process (P11)
- - high cost, remote match

- Slowest process (P13)
- - low cost, local match

- High cost match can be hidden

Comparison of matching time

Grid 2004

Conclusions & Future work

- Conclusions
- Low overhead approach for flexible data exchange between different time scale e-Science components

- Ongoing & future work
- Performance experiments in Grid environment
- Caching strategies to efficiently deal with slow importers
- Real applications – space weather is the first one

Grid 2004

Main components

Grid 2004

Local and Remote requests

Grid 2004

Space Science Application

Grid 2004

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