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When development of the SciLINC project began it had four primary
goals. Edited for brevity, they were:


Increase public access to nationally significant
scientific literature.

Enhance the usefulness of digitized materials by
creating a Web repository of scanned literature, keywords, and
online resources with tools for searching and
analysis.

Create an educational tool for learning about plant
life. While the screensaver application is indexing keywords,
the participant's computer will display information about
plant life within the United States and around the world. The
information displayed will describe each plant name or term
currently being indexed on the participant's computer, and
will include descriptive data, images, maps, and the annotated
outlinks for that term.

Provide a model for adopting public-resource computing
applications within the library community.


Botanicus is doing a
wonderful job of meeting goals 1 and 2 including processing data
generated by SciLINC. The project has certainly also meet goal 4.

We have learned much about grid-based, distributed,
public-resource computing applications and the
BOINC architecture. There
are thoughts and plans for analyses down the road that will be much
more computationally intensive than the original SciLINC analysis and
we look forward in time to bringing these projects to you.

While the amount of data that SciLINC has to analyze will increase
greatly in the days ahead it does not appear that increasing the
volume of information is going to improve the user experience of
running the SciLINC client.

It has been suggested that we repackage our data into single files
instead of uploading and downloading 50 files per workunit as we
currently do. This suggestion has been heeded and implemented. We had
planned on doing it before SciLINC was rolled out but scheduling
prevented it and the community discovered the project before we were
ready to announce it. We expect that testing will show the repackaging
lessens the load placed upon the core BOINC client software. But, it
does not change the amount of data being transferred.

The truth is that the workunits fly by so rapidly that
implementing goal 3 never became realistic.

When development of SciLINC began, the project lead's
understanding was that from a technological and economic standpoint it
makes sense to use public-resource computing in place of an internal
grid computing architecture whenever less than a gigabyte of data is
required per cpu-day of computation. Using the BOINC framework to
transfer the data to clients, SciLINC meets this volume-of-computation
guideline.

However, our brief experience with the dedicated BOINC community
over the last couple weeks has shown that, to the community these
numbers may differ somewhat. In its original form SciLINC would have
needed to transfer roughly 250MiB of compressed data in order to
occupy a modern CPU for a day. This would expand to nearly 660MiB of
input data. Then the client would need to upload about 44MiB of
results which would compress to 17MiB. These numbers have only grown
as SciLINC has been improved and made more efficient.

This is not acceptable to the average BOINC user.

Looking at the numbers from the perspective of someone on dial-up,
if they set SciLINC to only 1% of their BOINC time, this would be
roughly 15 minutes out of a day. For this 15 minutes they would have
needed to download around 2.5MiB of data. This may not be a huge issue
for broadband users, but if someone is on dial-up (as we have learned
many BOINC fans still are) the transfer time would exceed the
computation time.

So, where are we now?

Even if the transfer:credit ratios were acceptable to the
community, we do not have enough data to realistically occupy hundred
or thousands of BOINC enthusiasts for a lengthy period of time. As we
have already seen on various community boards a relatively small
amount of credit is earned for a comparatively large load on their
system resources. Any computational and transport related improvements
that have been tested have only resulted in more data needing to be
transferred.

As stated above, we are investigating the possibility of
performing much more computationally intensive analyses in the months
ahead. It is expected that these will be a much better fit for a BOINC
project than the current task of text-indexing and taxonomic analysis
which has a relatively low mathematical complexity.

Because of this it has been decided that for now all SciLINC
computation will be performed internally. When we have something with
a better credit-reward ratio (and nicer screensaver) it will be made
available to the community.

Thank you again for your interest and support. We look forward to
working with you in the future.

The SciLINC Team
This has been
cross-posted to the forums
for discussion and feedback.

Read more...

Source: SciLINC
BOINC project SciLINC: Main page News
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KWSN DC News reporter reporting from inside the server.