Here is the official announcement:

MADALGO Summer School on GEOMETRIC DATA STRUCTURES

August 16-19, 2010, Aarhus, Denmark

www.madalgo.au.dk/datastructures2010

OVERVIEW AND GOAL

Geometric data structures are methods of efficiently storing and querying data in two or more dimensions. Working with geometric data often requires the use of specialized and fundamentally geometric techniques. The goal of the summer school is to provide an in-depth introduction to some of the key techniques in geometric data structures, while highlighting a number of open problems.

LECTURES

The school will be taught by experts in the area of geometric data structures:

• Timothy Chan (University of Waterloo)
• Sariel Har-Paled (University of Illinois at Urbana-Champaign)
• John Iacono (Polytechnic Institute of New York University)
• Mihai Patrascu (AT&T)

PARTICIPATION

The summer school will take place on August 16-19, 2010 at Center for Massive Data Algorithmics (MADALGO) in the Department of Computer Science, University of Aarhus, Denmark.

The school is targeted at graduate students, as well as researchers interested in an in-depth introduction to geometric data structures.

The capacity of the summer school is limited. Prospective participants should register using the online registration form available at www.madalgo.au.dk/datastructures2010 as soon as possible. Registering graduate students must also have their supervisor send a letter confirming their graduate student status directly to madalgo@madalgo.au.dk; the subject line of the email should be ‘student_last_name/SS_2010/confirming’. Registration is on a first-come-first-serve basis and will close on July 1, 2010.

Registration is free; handouts, coffee breaks, lunches and a dinner will be provided by MADALGO and the Aarhus University.

ORGANIZING COMMITTEE

• Lars Arge (MADALGO)
• Gerth S. Brodal (MADALGO)
• John Iacono (Polytechnic Institute of New York University)
• Else Magård (MADALGO)
• Sara Ølholm (Aarhus University)

ABOUT MADALGO

Center for MAssive Data ALGOrithmics is a major basic research center funded by the Danish National Research Foundation. The center is located at the Department of Computer Science, University of Aarhus, Denmark, but also includes researchers at CSAIL, Massachusetts Institute of Technology in the US, and at the Max Planck Institute for Informatics and at Frankfurt University in Germany. The center covers all areas of the design, analysis and implementation of algorithms and data structures for processing massive data (interpreted broadly to cover computations where data is large compared to the computational resources), but with a main focus on I/O-efficient, cache-oblivious and data stream algorithms.

An interdisciplinary Post Doc position is available at Aarhus
University, Denmark, starting August 2010 or later. The responsibilities
of the Post Doc will include work on geospatial problems on the boundary
between computer science and biology, more specifically between
algorithms and ecology/biodiversity. Modest teaching responsibilities
may also be required. The Post Doc will be affiliated with the Danish
National Research Foundation Center MADALGO (Center for Massive Data
Algorithmics – www.madalgo.au.dk) under the supervision of computer
science Professor Lars Arge (person.au.dk/en/large@cs), but will also
work extensively with researchers in the Ecoinfomatics & Biodiversity
group at the Department of Biological Sciences under the supervision of
Professor Jens-Christian Svenning (person.au.dk/en/svenning@biology).

The Post Doc should focus on problems in relation to computationally
efficient use of modern detailed (and thus massive) topographic data in
various global-change-relevant modeling applications. These problems
include the computation of fine-resolution topography-derived variables
on a global scale, as well as local- and global-scale flooding scenario
modeling and impact assessment.

Applications are welcomed from computer science researchers with clearly
demonstrated skills in the design, analysis and implementation of
algorithms (preferably also with I/O-efficient algorithms), as well as
from geoinformatics or biology researchers with clearly demonstrated
skills in ecoinformatics, GIS, and programming. Researchers with
interdisciplinary research experience will be preferred. Applicants
should apply by uploading a letter of interest and a CV, as well as
indicate at least two names of references for recommendations, using the
application form available at www.madalgo.au.dk. To be assured of full
consideration, applications must arrive by May 17, 2010. Applications
will be considered until the position is filled.

For further information contact Professor Lars Arge at
large@madalgo.au.dk or Professor Jens-Christian Svenning at
svenning@biology.au.dk.

The nice new Motorola DROID phone does not have a tethering ability by standard (all-though a Verizon representative told that Verizon will be coming out with something at some point) but it is still possible to tether it using the Proxoid application. Unlike other approaches, you do not have to root your phone.

A detailed installation guide is available here. To use this guide with the Motorola DROID phone you should put the following line in /etc/udev/rules.d/11-android.rules:

SUBSYSTEMS=="usb", SYSFS{idVendor}=="22b8", SYSFS{idProduct}=="41db", MODE="0666", OWNER="USERNAME"

If you’re on Ubuntu Karmic Koala 9.10 you should restart udev using the following command:

sudo service udev restart

When trying to connect to the android phone you mave have to run

sudo ./adb kill-server

sudo ./adb start-server

Before you can see the device (this happens if the server was already started when you changed the udev rules). This works on Ubuntu Karmic Koala.

Be aware that tethering may not be allowed by your service provider, you should read your contract to find out.

I use the 64bit version of CUDA, go here to get it:
http://www.nvidia.com/object/cuda_get.html
(I selected Linux 64 bit and  Ubuntu 9.04 .)

This lets you download the CUDA Drivers, the CUDA Toolkit and the CUDA SDK, and you will end up with three files:

cudadriver_2.3_linux_64_190.18.run

cudatoolkit_2.3_linux_64_ubuntu9.04.run

cudasdk_2.3_linux.run

Installing the NVIDIA driver:

Cuda needs at least version 190 of the linux driver

1.

Uninstall existing NVIDIA drivers and nvidia-glx. (Use gui or aptitude).

2.

Install driver:

Quit x (log out of your desktop, go to a virtual terminal (CTRL+ALT+F1), login, run

$ sudo service gdm stop

3.

Run

$ sudo cudadriver_2.3_linux_64_190.18.run

to install the driver, follow the prompts.
Say yes to install 32 bit compatibility libraries.
If you want you can let the installer overwrite the Xorg configuration file.

4.

Reboot and log back in.

5.

Run

 $ nvidia-settings

to verify that your driver version is at least 190.18. Look for the driver version in the window:

Installing the CUDA Toolkit:

After having installed the driver we now need to install the CUDA toolkit itself.

1.

Run:

$ sudo sh cudatoolkit_2.3_linux_64_ubuntu9.04.run

2.

Press enter to install at the

/usr/local/cuda

default location.

3.

Register the new library files, run:

$ sudo gedit /etc/ld.so.conf.d/cuda.conf

and add

/usr/local/cuda/lib64

to the otherwise empty file and save it. Then run:

$ sudo ldconfig

You can also add

export PATH=/usr/local/cuda/bin:$PATH

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

to the end of your ~/.bashrc file.

(You should then login and out again of start a new bash with

bash --

to update your environment.

Installing the CUDA SDK and Compiling the Example Programs

We will now install the CUDA SDK to your own home directory so you can play with the supplied demos:

1.

Run:

$ sh cudasdk_2.3_linux.run

I chose to install it at the default location.

2.

As CUDA does not yet work with GCC 4.4 you will have to  install gcc-4.3:

$ sudo aptitude install gcc-4.3 g++-4.3

3.

Go to SDK source dir:

cd ~/NVIDIA_GPU_Computing_SDK/C$

4.

Create a directory and create symlinks to gcc-4.3/g++-4.3

$ mkdir mygcc

$ cd mygcc

$ ln -s $(which g++-4.3) g++

$ ln -s $(which gcc-4.3) gcc

$ cd ..

5.

Edit common makefile:

$ gedit common/common.mk

Find the lines that specify CC, CXX and LINK and change them to:

CXX        := g++-4.3

CC         := gcc-4.3

LINK       := g++-4.3 -fPIC

Add

#use gcc-4.3

NVCCFLAGS+=--compiler-bindir=${HOME}/NVIDIA_GPU_Computing_SDK/C/mygcc

Before the line that says “ifeq ($(nvcc_warn_verbose),1)”. This tells the nvcc computer that it should look in mygcc for gcc, which will cause it to pickup our gcc-4.3 compiler.

6.

You should now be able to compile everything by running

$ make

This should now compile all the examples in the SDK without errors.

Verify Installation

We can now verify that everything is working:

1.

Run:

$ bin/linux/release/deviceQuery

On my machine I get the following output (depending on your harware, you ourput may be different):

CUDA Device Query (Runtime API) version (CUDART static linking)

There is 1 device supporting CUDA

Device 0: "GeForce 8800 Ultra"
CUDA Driver Version:                           2.30
CUDA Runtime Version:                          2.30
CUDA Capability Major revision number:         1
CUDA Capability Minor revision number:         0
Total amount of global memory:                 804585472 bytes
Number of multiprocessors:                     16
Number of cores:                               128
Total amount of constant memory:               65536 bytes
Total amount of shared memory per block:       16384 bytes
Total number of registers available per block: 8192
Warp size:                                     32
Maximum number of threads per block:           512
Maximum sizes of each dimension of a block:    512 x 512 x 64
Maximum sizes of each dimension of a grid:     65535 x 65535 x 1
Maximum memory pitch:                          262144 bytes
Texture alignment:                             256 bytes
Clock rate:                                    1.51 GHz
Concurrent copy and execution:                 No
Run time limit on kernels:                     Yes
Integrated:                                    No
Support host page-locked memory mapping:       No
Compute mode:                                  Default (multiple host threads can use this device simultaneously)

Test PASSED

Press ENTER to exit...

You should now be good to go. Here’s an screenshot of the volumeRender demo:

$ bin/linux/release/volumeRender

Remember to use gcc-4.3/g++-4.3 and the –compiler-bindir option to nvcc when compiling your own CUDA source.

It is simple to design such a datastructure in the standard RAM using a simple integer variable, but this is not possible in faulty memory RAM where arbritrary memory cells can get corrupted at any point in time. In the paper we present tradeoff lower bounds and upper bounds and tradeoffs between the additive error factor and the time used per operation.

From the youtube description:

This video shows what parts of Denmark will flood if the ocean rises. The flood maps were computed using the TerraSTREAM software package and the finished flood maps were visualized by importing the resulting rasters into the GRASS open source GIS.

The algorithm used by TerraSTREAM takes dikes and natural features into account and works directly on the original input terrain. The terrain has been down-sampled afterwards for visualization purposes, the few seemingly disconnected flat areas are due to the fact that the computed output has been down-sampled for this video.

The size input grid dem used in this computation was around 100 gigabytes. The video was used in recent article(in Danish) which was about TerraSTREAM’s ability to compute flooding maps like the one shown in the video, on even very big terrains. This type of flood map can be used in an initial screening phase for computing flood risks.
In my opinion TerraSTREAM is a good example of I/O-efficient (external memory) algorithms put to use in efficiently solving real world problems on massive datasets.

There is a LIVE scoreboard and also live webcams.