Inspired by an article compared performance between Away3D vs. Papervision3D, I’ve just wanted to compare simple performance Sandy 3D (3.1 AS3) vs. Papervision 3D (2.0. Revision 849. Code name: Greate White) in rendering 1,000 objects.
As a result, the papervision3D is faster than Sandy3D by roughly 2~3 times or even more. My benchmark implementations of both Sandy and Papervision3D and source codes are available.
Optimization advice of Sandy can be found here.
I’ve found a very good introduction about how to parallelize applications from http://www.cs.princeton.edu/courses/archive/spr08/cos598A/parallelization_course.pdf
Especially, a few tools are very helpful to analyze the code at the beginning stage:
1. gprof – profiler. Help to decide which part should look at. Consider to use a pthread wrapper available at http://sam.zoy.org/writings/programming/gprof.html
2. helgrind – To detect race conditions
In order to try OpenMP3.0, I had been trying to install gcc-4.3.2 on an x86_64 Linux box without any luck. I got the following error in building gcc-4.3.2
/usr/bin/ld: crti.o: No such file: No such file or directory
It turns out that I need 32bit libc for cross-compilation which I can’t install since I’m not a superuser. The workaround is to disable this feature by using “--disable-multilib” option as follow:
./configure –-disable-multilib
make
After installation, if you meet the following error in compiling OpenMP file:
gcc: libgomp.spec: No such file or directory,
find libgomp.spec under /path/to/gcc/lib64 and make a symbolic link under /path/to/gcc/lib/gcc/x86_64-unknown-linux-gnu/4.3.2 (See [2])
Reference:
[1] gcc-help mailing list
[2] OpenMP in 30 Minutes
Currently in the Internet many collaborative tagging sites exist, but there is the need for a service to integrate the data from the multiple sites to form a large and unified set of collaborative data from which users can have more accurate and richer information than from a single site. In our paper, we have proposed a collective collaborative tagging (CCT) service architecture in which both service providers and individual users can merge folksonomy data (in the form of keyword tags) stored in different sources to build a larger, unified repository. We have also examined a range of algorithms that can be applied to different problems in folksonomy analysis and information discovery. These algorithms address several common problems for online systems: searching, getting recommendations, finding communities of similar users, and finding interesting new information by trends. Our contributions are to a) systematically examine the available public algorithms’ application to tag-based folksonomies, and b) to propose a service architecture that can provide these algorithms as online capabilities.
I’ve just faced with a cross-domain problem in making a mash-up site by using ajax call. In particular, I’m developing an igoogle gadget which should use cross-domain calls to retrieve data from a server outside of google domain.
I’ve found a good article to workaround this problem: http://developer.yahoo.com/javascript/howto-proxy.html
Among the solutions suggested in the article, I’m very pleased with JSONP approaches. Intuition of JSONP approach is that cross-domain problems will not occur for <script> tag. By exploiting this, call a cross-domain function, which designed to return json data enclosed in a function call, by using "src” attribute of <script> tag. Details can be found at http://bob.pythonmac.org/archives/2005/12/05/remote-json-jsonp/
The following is a short summary of:
J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1):5--53, 2004.
Commonly observed user tasks from recommender systems are as follow:
-. Annotation in context : Uses a recommender in an existing context. E.g., Overlay prediction information on top of existing links
-. Find good items : Video recommender, Item recommender, etc
-. Find All Good Items : purpose is to lower false negative rate
-. Recommend sequence : can be interesting
-. Find credible recommender : making the system more credible
Ratings can be explicit (users rate directly) or implicit (inferred from users behavior or preferences)
Commonly used evaluation metrics are:
1. Predictive accuracy
-. MAE (Mean Absolute Error) = (\sum (p_i – r_i))/N
-. MSE (Mean Square Error) = (\sum (p_i – r_i)^2)/N
-. RMSE (Root Mean Square Error) = \sqrt (MSE)
2. Classification accuracy
-. Precision : the ratio of relevant items selected to number of items selected
-. Recall : the ratio of relevant items selected to total number of relevant items available (Note: Precision and recall are inversely related.)
-. MAP(Mean Average Precision, aka F1) = 2PR/(P+R)
Collaborative filtering, also known as social tagging, is much more popular these days in the Internet but it’s not so clear yet how much and what kind of information we can extract from the system. Regarding this problem, I think Netflix prize would be a great challenge to make those questions clear out.
Simple. From the training set, provided by Netflix, which contains over 100M movie 1-to-5 scale ratings, we need to predict unknown movie ratings for the given qualifying set. More specifically, each data in the training set is quadruple of <user, movie, date of grade, grade> and the qualifying set is given <user, movie, date of grade, unknown grade>. We need to fill out the unknown grades by prediction and submit them for evaluation.
Besides two data sets, the training and qualifying set, Netflix provides the probe set which is a problem set with answers. With this, we can roughly estimate the accuracy without consulting with the scoring oracle.
Hard. However, we can learn from the front-runners, posted at the Leaderboard. The first annual progress winner is BellKor and they wrote about their algorithm.
#. of data in the training set = 100,480,507
#. of users in the training set = 480,189
#. of movies in the training set = 17,770
#. of data to predict in the qualifying set = 2,817,131
#. of data in the probe set = 1,408,395
As a student, trying to apply machine learning algorithms to various applications, it is interesting to study:
J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1):5--53, 2004.
R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 95--104, 2007
R. Bell and Y. Koren. Improved Neighborhood-based Collaborative Filtering. KDD-Cup and Workshop.
R. M. Bell and Y. Koren, Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights, Proc. IEEE International Conference on Data Mining (ICDM'07), 2007
Possible useful papers can be found from the Internet. I will keep this list up-to-date, as I read through.
I attended PMPP (Programming Massively Parallel Processors Agenda) workshop at NCSA last week (July 10th, 2008). It was great opportunity to see what we can do with parallel processors and modern powerful GPUs. Recently Multi-core or many-core has been drawing great attentions in various research communities and people are still struggling to find a way to maximize its capabilities in the coming 80-core era. In other side, Peoples have been also trying to utilize GPUs as a parallel computing unit. Modern GPUs are equipped with tens of, or hundreds of cores, which can be used for computing intensive jobs. The workshop was about all of these: Multicores and GPUs.
Here are some highlights:
I have been doing tag analysis to find underlying relationships between tags, users, and resources in folksonomies. I think there are possibly two different approaches: i) frequency analysis based on resource vectors spanning over the term space and ii) graph-based analysis based on tag graphs.
In the frequency analysis, we can use Principal Component Analysis(PCA) or Independent Component Analysis(ICA). Similar approaches have been done in the field of IR. An example can be found in here, where PCA and ICA were used for tag-advertising matching.
With PCA and ICA, I produced the following figures:
(a) PCA with Tag-graph
(b) ICA with Tag-graph (number of component = 3)
It is very interesting that we can see some tag relationships (or tag clusters) in both two pictures but reasonable interpretation is not so easy to get. I think it's a good starting point to cluster terms for extract meaningful information. I will keep working on those graph.
As for the next step, I'm going to do analysis with graph-based methods.
