National Aeronautics and Space Administration Jet Propulsion Laboratory March 31, 2009 CSCI 578...

National Aeronautics andSpace Administration

Jet Propulsion Laboratory

CSCI 578 Course Project: Rearchitecting Scientific Code

March 31, 2009March 31, 2009

David WoollardNASA Jet Propulsion Lab

4800 Oak Grove DrivePasadena, CA 91108

Dept. of Computer ScienceUniversity of Southern California

Los Angeles, CA 90089



Agenda For Today• Introduction

– Scientific Computing Domain

– Workflows & Grid Computing

– Research Challenge

• Project Goals

• Project Details– Extracting Kernels

– Executable Creation

– Workflow Specification

– SWSA – A Domain Specific Software Architecture

• Timeline

CSCI 578 Course Project - March 31, 2009. 2



Computational Sciences Today


Real science is now being conducted via computational experimentation – or “in silico” computing



“in silico” Computing


• Discovery is a phase is which a scientist rapidly prototypes, tests hypotheses, and develops a methodology

Discovery Production

Distribution

Theory

Practice

Development

Execution

Lone Researcher[Kepner 03]





• Production is the engineeringengineering of replicating an experiment on large volumes of data.


Distribution

We will focus on Production SystemsProduction Systems in this research.





• Distribution is a phase in which data is dispersed to peers for review and further experimentation including:PapersPapersFederated DataFederated DataDigital LibrariesDigital Libraries


Distribution



Validating Computational Science

• Computational science, like all science, requires validation

• Validation comes in two forms:– Scaling (in data and computation)– Independent replication

• Both forms require significant computational resources– Grid is a promising resource




Vision of the Grid


Like the power grid, the computational Grid should scale to the demands of

individual users.



• Workflows orchestrate processes on the Grid• Workflows are a processing model that incorporate

tasks, data, and rulestasks, data, and rules.• Workflow management systems execute taskstasks on

the Grid using datadata once the task’s dependencies are satisfied based on rulesrules.

Workflow-Based Specification


Task 1

Task 2

Task 3

Task 4

Task 5



A Plethora of Workflow Languages


• Yu & Buyya presented a taxonomy [Yu & Buyya 05]

– Based on workflow properties like model representation and scheduling policy

– Illustration of divergence in the field

• Considered a Grand Challenge [Gil, et al. 07]



Scaling the Experiment


Laboratory Institution Co-laboratory

@Home@Home

OtherOtherInstitutionsInstitutions

Task 1

Task 2

Task 3

Task 4

Task 5



Independent Replication


Collaborator 3rd Party

Task 1

Task 2

Task 3

Task 4

Task 5



Heterogeneous Environments


Laboratory Institution Co-laboratory

Task 1

Task 2

Task 3

Task 4

Task 5

Task 1

Task 2

Task 3

Task 4

Task 5

Task 1

Task 2

Task 3

Task 4

Task 5

Workflow Engine 1

Grid Infrastructure 1

Workflow Engine 1


Workflow Engine 2


Collaborator 3rd Party



Research Challenge• Scientific validation requires:

– Scaling– Replication

• Existing technologies exhibit three challenges:– Require scientists to become engineers or vice versa– Existing workflow specifications entwine scientific and

engineering concerns– Existing workflow specifications are not portable




Agenda• Introduction




• Project Goals





• Timeline




Project Goals


What do you get out of it? • Experience as a software architect in an unfamiliar domain• Experience refactoring / applying architectural recovery• Exposure to popular Grid technologies• Experience implementing distributed software systems with architectural middleware• A grade

What do I get out of it? • Data on kernel extraction for a canonical set of scientific applications• Performance impacts of architected code• Feedback on methodologies







• Project Goals





• Timeline




Scientific Software Packages (1/3)


Search The search benchmark solves a game of connect-4 on a 6 x 7 board using a alpha-beta pruning technique. The problem size is determined by the initial position from which the game in analyzed.

Euler The Euler benchmark solves the time-dependent Euler equations for flow in a channel with a "bump" on one of the walls. A structured, irregular, N x 4N mesh is employed, and the solution method is a finite volume scheme using a fourth order Runge-Kutta method with both second and fourth order damping.





MolDyn MolDyn is an N-body code modeling particles interacting under a Lennard-Jones potential in a cubic spatial volume with periodic boundary conditions.

Monte Carlo A financial simulation, using Monte Carlo techniques to price products derived from the price of an underlying asset. The code generates N sample time series with the same mean and fluctuation as a series of historical data.





Ray tracer This benchmark measures the performance of a 3D ray tracer. The scene rendered contains 64 spheres, and is rendered at a resolution of NxN pixels.



What is Kernel Decomposition?• Decomposition is a process in which scientific kernelsscientific kernels are

identified and control/data flow is determined.• Scientific kernels are like functionsfunctions - they have internal scope

and a single entry and exit point. In graph theoretic terms, the call dominancy tree for the basic blocks in the module only have one source and one sink.

• Scientific kernels reify a scientific conceptscientific concept.




Decomposition Process1. Analyze the code for blocks that you can summarize as a step

in the high-level process. Candidates include:– functions/methods that the original programmer(s) abstracted

– Sequences of code that processes linearly

2. Draw the call graph between these blocks

3. Design data structures to pass requisite data between these kernels

4. Iterate, balancing between the complexity of the data to be passed and the unity of code captured in your kernels




Executable Creation

For Each Kernel:• Design data/file formats to capture your input and

output data structures• Write marshalling & unmarshalling code• Implement kernel as a run() method• Watch for scoping issues (you might not have fully

decomposed your system)• Command-line interface:

java <KernelName> <input file path> <output file path>




Workflow Implementation


YAWL:Yet Another Workflow Language

http://www.yawl-system.com/

Java-based ImplementationWindows, Linux, OS-X



Workflow Implementation


Taverna:

http://taverna.sourceforge.net/

Java-based ImplementationWindows, Linux, OS-X



Orchestrate the Executables• Once you have finalized your executables:

1. Implement the control flow you have captured via a workflow specification

2. Incorporate data dependencies

3. Validate the correctness of the resulting orchestration

4. Compare timing and memory usage for the original implementation and the workflow-based system – valgrind, JMX are possible tools








• Project Goals





• Timeline




Making Decisions in Design Space

• Existing workflow languages violate separation of concerns– Scientists should work in languages applicable to the design

space, not the solution space– Engineers should not have to become scientists to be able

to scale workflow-based systems

• If workflow languages become the realm of the scientist, how does the software engineer effect change?


Manipulation of the system at the architectural level



A Model-Driven Approach


ComputationIndependent Model

ImplementationIndependent Model

Implementation

Workflow Model

Domain-SpecificSoftware Architecture

Deployment



Workflow Control Elements

• Sequence: Do A then B

• Conditional: Do A or B

• Loop: Do A n times

• Branch: Do A and B together

• Bound: Do C after completing A and B




Orchestration Through Connectors

• Lau, et al., have proposed exogenous connectors [Lau, et al. 06].

– encapsulate both control and data flow in a software system

– can be hierarchically composed to simulate control flow

• Control can be managed through several constructs:

– Sequence

– Conditional

– Branch & Bound


A B

A B

C

A B

A B



Implementation

• Prism-MW, an architecturally-aware middleware

– Components, Connectors, Topologies and Architecture are reified as first class elements

• Exogenous connectors, invoking connectors, and component wrappers around tasks are build with Prism




SWSA: A Domain Architecture




Putting it Together• After you have implemented your workflow

orchestration:1. Modify your executables to be prism components: replace

marshalling & unmarshalling code with prism event handling code

2. Develop prism-based connectors with custom event handlers that implement orchestration logic

3. Compare this implementation to the original code and the workflow system for computation time and memory footprint




Timeline• Monday, March 30: Email [email protected] and

[email protected] with the names, email addresses and student ID numbers of each team member. One email per team please.

• Thursday, April 9: Status report 1 due. One per team. Details and format will be sent to the teams by April 2.

• Thursday, April 23: Status report 2 due. One per team. Details and format will be sent to the teams by April 16.

• Friday, May 1: Project materials due (project report, workflow-based system, and SWSA system).




References/Further ReadingReferences of workflow systems:

[Yu & Buyya 05] Yu, J. and Buyya, R. A Taxonomy of Workflow Management Systems for Grid Computing. Journal of Grid Computing 3(3-4): pp. 171-200. 2005.

[Gil, et. al. 07] Gil, Y., et. al. Examining the Challenges of Scientific Workflows. IEEE Computer 40(12): pp. 24-32. 2007.

References of exogenous connectors:

[Lau, et. al. 06] Lau, K., et. al. A Software Component Model and its Preliminary Formalisation. In F.S. de Boer et al., editors, Proceedings of Fourth International Symposium on Formal Methods for Components and Objects, Lecture Notes in Computer Science 4111(1-21). 2006.

References on SWSA:

[Woollard 08] Woollard, D. Supporting the Engineering Aspects of e-Science Through Workflow Services. Proceedings of the First Brazilian e-Science Workshop, Campinas, Brazil, 2008.

[Woollard, et. al. 09] Woollard, D. et. al. Injecting Software Architectural Constraints into Legacy Scientific Applications. To appear in Proceedings of the ICSE 2009 Workshop on Software Engineering for Computational Science and Engineering. Vancouver, Canada, 2009.


National Aeronautics and Space Administration Jet Propulsion Laboratory March 31, 2009 CSCI 578...

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