=============The Survey=========================

Please answer the attached survey with as much or as little verbosity as you please and return it to me by September 10.  The survey has 3 mandatory sections and 4 voluntary (bonus) sections.  The sections are as follows;
Mandatory;
	1) Data Structures
	2) Execution Model
	3) Parallelism and Load-Balancing
Voluntary;
	4) Graphics and Rendering
	5) Presentation
	6) Basic Deployment and Development Environment Issues
	7) Collaboration
We will spend this workshop focusing on the first 3 sections, but I think we will derive some useful/motivating information from any answers to questions in the voluntary sections.

I'll post my answers to this survey on diva mailing list very soon.  You can post your answers publicly if you want to, but I am happy to regurgitate your answers as "anonymous contributors" if it will enable you to be more candid in your evaluation of available technologies.

1) Data Structures/Representations/Management==================
The center of every successful modular visualization architecture has been a flexible core set of data structures for representing data that is important to the targeted application domain.  Before we can begin working on algorithms, we must come to some agreement on common methods (either data structures or accessors/method  calls) for exchanging data between components of our vis framework.

There are two potentially disparate motivations for defining the data representation requirements.  In the coarse-grained case, we need to define standards for exchanging data between components in this framework (interoperability).  In the fined-grained case, we want to define some canonical data structures that can be used within a component -- one developed specifically for this framework.  These two use-cases may drive different set of requirements and implementation issues.
	* Do you feel both of these use cases are equally important or should we focus exclusively on one or the other?
	* Do you feel the requirements for each of these use-cases are aligned or will they involve two separate development tracks?  For instance, using "accessors" (method calls that provide abstract access to essentially opaque data structures) will likely work fine for the coarse-grained data exchanges between components, but will lead to inefficiencies if used to implement algorithms within a particular component.
	* As you answer the "implementation and requirements" questions below, please try to identify where coarse-grained and fine-grained use cases will affect the implementation requirements.

What are requirements for the data representations that must be supported by a common infrastructure.  We will start by answering Pat's questions of about representation requirements and follow up with personal experiences involving particular domain scientist's requirements.
	Must: support for structured data
	Must/Want: support for multi-block data?
	Must/Want: support for various unstructured data representations? (which ones?)
	Must/Want: support for adaptive grid standards?  Please be specific about which adaptive grid methods you are referring to.  Restricted block-structured AMR (aligned grids), general block-structured AMR (rotated grids), hierarchical unstructured AMR, or non-hierarchical adaptive structured/unstructured meshes.
	Must/Want: "vertex-centered" data, "cell-centered" data? other-centered?
	Must: support time-varying data, sequenced, streamed data?
	Must/Want: higher-order elements?
	Must/Want: Expression of material interface boundaries and other special-treatment of boundary conditions.
	* For commonly understood datatypes like structured and unstructured, please focus on any features that are commonly overlooked in typical implementations.  For example, often data-centering is overlooked in structured data representations in vis systems and FEM researchers commonly criticize vis people for co-mingling geometry with topology for unstructured grid representations.  Few datastructures provide proper treatment of boundary conditions or material interfaces.  Please describe your personal experience on these matters.
	* Please describe data representation requirements for novel data representations such as bioinformatics and terrestrial sensor datasets.  In particular, how should we handle more abstract data that is typically given the moniker "information visualization".

What do you consider the most elegant/comprehensive implementation for data representations that you believe could form the basis for a comprehensive visualization framework?
	* For instance, AVS uses entirely different datastructures for structure, unstructured and geometry data.  VTK uses class inheritance to express the similarities between related structures.  Ensight treats unstructured data and geometry nearly interchangably.  OpenDX uses more vector-bundle-like constructs to provide a more unified view of disparate data structures.  FM uses data-accessors (essentially keeping the data structures opaque).
	* Are there any of the requirements above that are not covered by the structure you propose?
	* This should focus on the elegance/usefulness of the core design-pattern employed by the implementation rather than a point-by-point description of the implemenation!
	* Is there information or characteristics of particular file format standards that must percolate up into the specific implementation of the in-memory data structures?

For the purpose of this survey, "data analysis" is defined broadly as all non-visual data processing done *after* the simulation code has finished and *before* "visual analysis".
	* Is there a clear dividing line between "data analysis" and "visual analysis" requirements?
	* Can we (should we) incorporate data analysis functionality into this framework, or is it just focused on visual analysis.
	* What kinds of data analysis typically needs to be done in your field?  Please give examples and how these functions are currently implemented.
	* How do we incorporate powerful data analysis functionality into the framework?

2) Execution Model=======================
It will be necessary for us to agree on a common execution semantics for our components.  Otherwise, while we might have compatible data structures but incompatible execution requirements.  Execution semantics is akin to the function of protocol in the context of network serialization of data structures.  The motivating questions are as follows;
	* How is the execution model affected by the kinds of algorithms/system-behaviors we want to implement.
	* How then will a given execution model affect data structure implementations
	* How will the execution model be translated into execution semantics on the component level.  For example will we need to implement special control-ports on our components to implement particular execution models or will the semantics be implicit in the way we structure the method calls between components.

What kinds of execution models should be supported by the distributed visualization architecture
	* View dependent algorithms? (These were typically quite difficult to implement for dataflow visualization environments like AVS5).
	* Out-of-core algorithms
	* Progressive update and hierarchical/multiresolution algorithms?
	* Procedural execution from a single thread of control (ie. using an commandline language like IDL to interactively control an dynamic or large parallel back-end)
	* Dataflow execution models?  What is the firing method that should be employed for a dataflow pipeline?  Do you need a central executive like AVS/OpenDX or, completely distributed firing mechanism like that of VTK, or some sort of abstraction that allows the modules to be used with either executive paradigm?
	* Support for novel data layouts like space-filling curves?
	* Are there special considerations for collaborative applications?
	* What else?

How will the execution model affect our implementation of data structures?
	* how do you decompose a data structure such that it is amenable to streaming in small chunks?
	* how do you represent temporal dependencies in that model?
	* how do you minimize recomputation in order to regenerate data for view-dependent algorithms.

What are the execution semantics necessary to implement these execution models?
	* how does a component know when to compute new data? (what is the firing rule)
	* does coordination of the component execution require a central executive or can it be implemented using only rules that are local to a particular component.
	* how elegantly can execution models be supported by the proposed execution semantics?  Are there some things, like loops or back-propagation of information that are difficult to implement using a particular execution semantics?

How will security considerations affect the execution model?

3) Parallelism and load-balancing=================
Thus far, managing parallelism in visualization systems has been a tedious and difficult at best.  Part of this is a lack of powerful abstractions for managing data-parallelism, load-balancing and component control.

Please describe the kinds of parallel execution models that must be supported by a visualization component architecture.
	* data-parallel/dataflow pipelines?
	* master/slave work-queues?
	* streaming update for management of pipeline parallelism?
	* chunking mechanisms where the number of chunks may be different from the number of CPU's employed to process those chunks?
	* how should one manage parallelism for interactive scripting languages that have a single thread of control?  (eg. I'm using a commandline language like IDL that interactively drives an arbitrarily large set of parallel resources.  How can I make the parallel back-end available to a single-threaded interactive thread of control?)

Please describe your vision of what kinds of software support / programming design patterns are needed to better support parallelism and load balancing.
	* What programming model should be employed to express parallelism.  (UPC, MPI, SMP/OpenMP, custom sockets?)
	* Can you give some examples of frameworks or design patterns that you consider very promising for support of parallelism and load balancing.  (ie. PNNL Global Arrays or Sandia's Zoltan)
		http://www.cs.sandia.gov/Zoltan/
		http://www.emsl.pnl.gov/docs/global/ga.html
	* Should we use novel software abstractions for expressing parallelism or should the implementation of parallelism simply be an opaque property of the component? (ie. should there be an abstract messaging layer or not)
	* How does the NxM work fit in to all of this?  Is it sufficiently differentiated from Zoltan's capabilities?


===============End of Mandatory Section (the rest is voluntary)=============

4) Graphics and Rendering=================
What do you use for converting geometry and data into images (the rendering-engine).  Please comment on any/all of the following.
	* Should we build modules around declarative/streaming methods for rendering geometry like OpenGL, Chromium and DirectX or should we move to higher-level representations for graphics offered by scene graphs?  What are the pitfalls of building our component architecture around scene graphs?
	* What about Postscript, PDF and other scale-free output methods for publication quality graphics?  Are pixmaps sufficient?

In a distributed environment, we need to create a rendering subsystem that can flexibly switch between drawing to a client application by sending images, sending geometry, or sending geometry fragments (image-based rendering)?  How do we do that?
	* Please describe some rendering models that you would like to see supported (ie. view-dependent update, progressive update) and how they would adjust dynamically do changing objective functions (optimize for fastest framerate, or fastest update on geometry change, or varying workloads and resource constraints).
	* Are there any good examples of such a system?

What is the role of non-polygonal methods for rendering (ie. shaders)?
	* Are you using any of the latest gaming features of commodity cards in your visualization systems today?
	* Do you see this changing in the future? (how?)

5) Presentation=========================
It will be necessary to separate the visualization back-end from the presentation interface.  For instance, you may want to have the same back-end driven by entirely different control-panels/GUIs and displayed in different display devices (a CAVE vs. a desktop machine).   Such separation is also useful when you want to provide different implementations of the user-interface depending on the targeted user community.  For instance, visualization experts might desire a dataflow-like interface for composing visualization workflows whereas a scientists might desire a domain-specific dash-board like interface that implements a specific workflow.  Both users should be able to share the same back-end components and implementation even though the user interface differs considerably.

How do different presentation devices affect the component model?
	* Do different display devices require completely different user interface paradigms?  If so, then we must define a clear separation between the GUI description and the components performing the back-end computations.  If not, then is there a common language to describe user interfaces that can be used across platforms?
	* Do different display modalities require completely different component/algorithm implementations for the back-end compute engine?  (what do we do about that??)

What Presentation modalities do you feel are important and what do you consider the most important.
	* Desktop graphics (native applications on Windows, on Macs)
	* Graphics access via Virtual Machines like Java?
	* CAVEs, Immersadesks, and other VR devices
	* Ultra-high-res/Tiled display devices?
	* Web-based applications?

What abstractions do you think should be employed to separate the presentation interface from the back-end compute engine?
	* Should we be using CCA to define the communication between GUI and compute engine or should we be using software infrastructure that was designed specifically for that space? (ie. WSDL, OGSA, or CORBA?)
	* How do such control interfaces work with parallel applications?  Should the parallel application have a single process that manages the control interface and broadcasts to all nodes or should the control interface treat all application processes within a given component as peers?


6) Basic Deployment/Development Environment Issues============
One of the goals of the distributed visualization architecture is seamless operation on the Grid -- distributed/heterogeneous collections of machines.  However, it is quite difficult to realize such a vision without some consideration of deployment/portability issues.  This question also touches on issues related to the development environment and what kinds of development methods should be supported.

What languages do you use for core vis algorithms and frameworks.
	* for the numerically intensive parts of vis algorithms
	* for the glue that connects your vis algorithms together into an application?
	* How aggressively do you use language-specific features like C++ templates?
	* is Fortran important to you?  Is it important that a framework support it seamlessly?
	* Do you see other languages becoming important for visualization (ie. Python, UPC, or even BASIC?)

What platforms are used for data analysis/visualization?
	* What do you and your target users depend on to display results? (ie. Windows, Linux, SGI, Sun etc..)
	* What kinds of presentation devices are employed (desktops, portables, handhelds, CAVEs, Access Grids, WebPages/Collaboratories) and what is their relative importance to active users.
	* What is the relative importants of these various presentation methods from a research standpoint?
	* Do you see other up-and-coming visualization platforms in the future?

Tell us how you deal with the issue of versioning and library dependencies for software deployment.
	* For source code distributions, do you bundle builds of all related libraries with each software release (ie. bundle HDF5 and FLTK source with each release).
	* What methods are employed to support platform independent builds (cmake, imake, autoconf).  What are the benefits and problems with this approach.
	* For binaries, have you have issues with different versions of libraries (ie. GLIBC problems on Linux and different JVM implemetnations/version for Java).  Can you tell us about any sophisticated packaging methods that address some of these problems (RPM need not apply)
	* How do you handle multiplatform builds?

How do you (or would you) provide abstractions that hide the locality of various components of your visualization/data analysis application?
	* Does anyone have ample experience with CORBA, OGSA, DCOM, .NET, RPC?  Please comment on advantages/problems of these technologies.
	* Do web/grid services come into play here?
	

7) Collaboration ==========================
If you are interested in "collaborative appllications" please define the term "collaborative".  Perhaps provide examples of collaborative application paradigms.

Is collaboration a feature that exists at an application level or are there key requirements for collaborative applications that necessitate component-level support?
	* Should collaborative infrastructure be incorporated as a core feature of very component?
	* Can any conceivable collaborative requirement be satisfied using a separate set of modules that specifically manage distribution of events and data in collaborative applications?
	* How is the collaborative application presented?  Does the application only need to be collaborative sometimes?
	* Where does performance come in to play?  Does the visualization system or underlying libraries need to be performance-aware?  (i.e. I'm doing a given task and I need a framerate of X for it to be useful using my current compute resources), network aware (i.e. the system is starving for data and must respond by adding an alternate stream or redeploying the pipeline).  Are these considerations implemented at the component level, framework level, or are they entirely out-of-scope for our consideration?