1. SCIRun and SPA integration status Steven G. Parker Ayla Khan Oscar Barney

2. Scientific Computing and Imaging Institute, University of Utah SCIRun with PtolemyII

3. Scientific Computing and Imaging Institute, University of Utah SCIRun with PtolemyII SCIRunJNIActor declares native methods implemented in SCIRun Ptolemy package using JNI interface Actor accepts ObjectToken encapsulating SCIRunData class: contains type information and raw data Actor calls native functions that access data object in Java

4. Scientific Computing and Imaging Institute, University of Utah SCIRun with PtolemyII SCIRun contains modules that: Convert multi-dimensional arrays from Java object to nrrd (nearly raw raster data) format (Ptolemy package) Convert nrrd data to SCIRun types (Teem package) Example of data stored in a nrrd Arrow points to the module that reads data from Java

5. Scientific Computing and Imaging Institute, University of Utah Nearly Raw Raster Data (NRRD) Format Library and file format that supports image processing involving N-dimensional raster data and supports a large range of data types Distributed with Gordon Kindlmann’s Teem library Teem is used for manipulation of structured scientific data Can be thought of as wrappers for raw data and are easier to create than SCIRun data objects for simple datatypes SCIRun’s Teem interface has modules that convert nrrds to SCIRun data types

6. Scientific Computing and Imaging Institute, University of Utah Current Status Simple initial test case randomly generated mesh converted to SCIRun mesh via Teem nrrd to field converter requires pre-existing SCIRun network file containing instance of Ptolemy converter module (name and path of saved network set by SCIRunJNIActor)

7. Scientific Computing and Imaging Institute, University of Utah Current Caveats SCIRun thread library vs. native threads used by JNI: interface is currently somewhat unstable Data from other actors must be encapsulated in SCIRun data object Teem package converters may limit access to full range of SCIRun datatypes

8. Scientific Computing and Imaging Institute, University of Utah Next Steps Insert SCIRun into SPA workflow Target specific workflows Expand support for SCIRun data types Improve SCIRun synchronization with Java Add ability to send data from SCIRun to Java actors Increase efficiency of reading large data sets from Java Allow processing of multiple data sets in one iteration or over multiple iterations

9. Scientific Computing and Imaging Institute, University of Utah What are Components? No universally accepted definition in computer science research …yet A unit of software development/deployment/reuse i.e. has interesting functionality Ideally, functionality someone else might be able to (re)use Can be developed independently of other components Interacts with the outside world only through well-defined interfaces Implementation is opaque to the outside world Can be composed with other components “Plug and play” model to build applications Composition based on interfaces

10. Scientific Computing and Imaging Institute, University of Utah What is a Component Architecture? A set of standards that allows: Multiple groups to write units of software (components)… And have confidence that their components will work with other components written in the same architecture These standards define… The rights and responsibilities of a component How components express their interfaces The degree to which components are protected from each other The environment in which are composed to form an application and executed (framework) The rights and responsibilities of the framework

11. Scientific Computing and Imaging Institute, University of Utah What is the CCA? CCA is a specification of a component environment designed for high performance scientific computing Specification is decided by the CCA Forum ­CCA Forum membership open to all “CCA-compliant” just means conforming to the specification ­Doesn’t require using any of our code! A tool to enhance the productivity of scientific programmers Make the hard things easier, make some intractable things tractable Support & promote reuse & interoperability Not a magic bullet

12. Scientific Computing and Imaging Institute, University of Utah CCA Philosophy and Objectives Local and remote components Support local, HPC parallel, and distributed computing High Performance Design should support high-performance mechanisms wherever possible (i.e. minimize copies, extra communications, extra synchronization) Support SPMD and MPMD parallelism Allow user to choose parallel programming models Heterogeneity Multiple architectures, languages, run-time systems used simultaneously in an application Integration Components should be easy to make and easy to use Openness and simplicity CCA spec should be open & usable with open software

13. Scientific Computing and Imaging Institute, University of Utah CCA Concepts: Language Interoperability Existing language interoperability approaches are “point- to-point” solutions Babel provides a unified approach in which all languages are considered peers Babel used primarily at interfaces C C++ f77 f90 Python Java Babel C C++ f77 f90 Python Java Few other component models support all languages and data types important for scientific computing

14. Scientific Computing and Imaging Institute, University of Utah CCA Implementations Caffeine (SCMD parallel, Babel or C++) SCIRun2 (Distributed, MCMD parallel, Babel or C++, incomplete) XCAT (Grid-based, C++) LegionCCA (Grid-based, uses Legion) Decaf (Single process, Babel)

15. Scientific Computing and Imaging Institute, University of Utah CCA myths (debunked) CCA is too complicated No, code integration is complicated Producing quality reusable code is complicated CCA makes both easier Some implementations are more complicated than they should be CCA is buggy No, but some (all) of the implementations are Some are better than others CCA has high overheads No, this has been measured to be about the cost of a C++ virtual function call for most cases CCA is a data model No, but you could use CCA to build one CCA is a workflow engine No, but you could use CCA to build one CCA is perfect No, but it is an open forum

16. Scientific Computing and Imaging Institute, University of Utah Should I use CCA? Of course! If you care about interoperable software for high- performance computing It won’t solve all of your problems If you are trying to build a complex system involving multiple languages If you don’t, you might end up creating something just like it Please participate in CCA forum and help us improve the specification

17. Scientific Computing and Imaging Institute, University of Utah