From: "Ilmi
Yoon" <yoon@cs.sfsu.edu>
Date: Tue Sep 9, 2003 6:55:29 PM US/Pacific
To: "John
Shalf" <jshalf@lbl.gov>
Subject: Re: DiVA Survey
(Please return by Sept 10!)
Hi John,
Here are my answers.
Ilmi
----- Original Message -----
From: "John Shalf" <jshalf@lbl.gov>
To: <diva@lbl.gov>
Sent: Wednesday, August 27, 2003 3:33 PM
Subject: DiVA Survey (Please return by Sept
10!)
=============The
Survey=========================
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?
&& I think we need to decide the
coarse-grain something like SOAP that wraps
the internal data with XML format. But I
think we don't need to decide the
fined-grain since each component can have
choose their own way/foramt and
then post format to public, so the party who
want to use the component needs
to follow the interface. But if we like to
decide initial sets of format
that must/may be supported by diva
components, then we can list most popular
format and choose some/all of them.
* 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.
&& There will be some overhead and inefficiency usingaccessors
for data
exchange, but I like the apporach of
accessors and believe the CCA achieves
the reusability in expense of performance as
OOP does anyway. Just we try to
make the expense as little as possible.
* 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).
&& Combination of (externally) FM
data-accessors and (internally) VTK class
inheritance.
* 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?
&& Some components do purely data
analysis, some do only visual, but there
will be calls to the data analysis component
from the visual during the
analysis.
* Can we (should we) incorporate data
analysis functionality into this
framework, or is it just focused on visual
analysis.
&& Not all data analysis, but there
are lots of data analysis being used for
visual analysis and, the more tools are
provided initially, it gets easier
to make user-group become big. So, we can
list candidates.
* 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.
&& I guess we can make each
component propagate/fire the execution of next
component/components in the
network/pipeline. Each component can use their
own memory or shared memory to access the
data in process. In such case,
algorithm of each component does not get
much affected by other coponents
around.
* 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).
&& I like to say "must",
but it is for improving usability and efficiency,
so people may live without it.
It will definitely improve the efficiency.
If we want to support view
dependent algorithm, then we should consider
it from the beginning of the
dataflow design, so it can be easily
integrated into. View dependent or
image-based algorithm doesn't necessarily
make much changes to existing data
flow design. View dependant or image-based
algorithms are useful to
eliminate majority of data blocks from the
rendering pipeline. Therefore, it
is good to provide capability to choose
subset of data to be rendered from
the dataflow.
* Out-of-core algorithms
* Progressive update and
hierarchical/multiresolution algorithms?
&& MUST! for improving usability and efficiency. And can be used to
support
view-dependent algorithm.
* 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)
&& Good to have
* 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?
&& Some locking mechanizm for subset
of data or dispatching of changes from
one client to multiple clients
* 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?
&& It is usually useful to have access
to frame buffer so, I prefer OpenGL
style over VRML style.
In addition, I don't know how useful the
scene graphs for visualization. I
guess scene graphs for visualizations are
relatively simple, so it is
possible to convert the scene graphs to
declarative way. So, mainly support
declarative methods and then additional
support of scen graphs and
conversions to declarative methods.
What are the pitfalls of building our
component architecture around
scene graphs?
&& might lose access to frame buffer
and pixel level manipulation --
extremely difficult for view dependent or
image-based approach
* 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?