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Small drawing of a B+ tree

Publishing STX B+ Tree 0.9 - Speed Gains over 0.8.6

Posted on 2013-05-07 21:16 by Timo Bingmann at Permlink with 0 Comments. Tags: stx-btree

This blog post announces the new version 0.9 of my popular STX B+ Tree library of C++ templates, speedtests and demos. Since the last release in 2011, many patches and new ideas have accumulated. Here a summary of the most prominent changes:

STX B+ tree version 0.9 is available from the usual project webpage.

The switch from binary search to linear scan, and all further patches and optimization call for a direct comparison of version 0.8.6 and 0.9. Because of special optimizations to the btree_set specializations, the following plots differentiate between set and maps.

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Photo of my Raspberry Pi Model B

STX B+ Tree Speed Test Measurements on Raspberry Pi (Model B)

Posted on 2013-05-06 09:48 by Timo Bingmann at Permlink with 1 Comments. Tags: stx-btree frontpage

The Raspberry Pi is maybe one of the most hyped embedded system projects in the last year, and I also got myself one for experiments. People are doing amazing things with this Linux-in-a-box SoC. Doubtlessly, the popularity is due to the standardized platform and a large community forming around it, which makes fixing the many small problems with Linux on ARM systems feasible. For me, the Raspberry Pi is an alternative architecture on which to test my algorithms and libraries, which exhibits somewhat different characteristics than the highly optimized desktop CPUs.

So I decided to run my STX B+ Tree speed test on the Raspberry Pi Model B, because most people use the SoC for multimedia purposes and little other memory performance data is available. The B+ tree speed test gives insight into the platform's overall memory and processing performance, and thus yields a better assessment of how useful the system is for general purpose applications (unlike multimedia decoding). Most benchmarks focus solely on floating point or integer arithmetic, which alone are very poor indicators for overall system performance. The Raspberry Pi forums say it has performance similar to a "Pentium 2 with 300 MHz", but that is for arithmetic.

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STX B+ Tree Measuring Memory Usage with malloc_count

Posted on 2013-05-05 09:44 by Timo Bingmann at Permlink with 2 Comments. Tags: stx-btree frontpage

Within the next few days, a new version of my popular STX B+ Tree library will be released. In light of this imminent release, I created a memory profile with my malloc_count tool, comparing the requirements of four different C++ maps with integer keys and values.

The test is really simple: create a map container, insert 8 Mi random integer key/value pairs, and destruct it. The memory profile shows the amount of memory over time as allocated via malloc() or new. The test encompasses the usual gcc STL's map which is a red-black tree, the older hash_map from gcc's STL extensions, the newer gcc C++ tr1::unordered_map, and of course the stx::btree_map with default configuration. As a reference, I also added the usual STL vector and deque (not map containers), to verify the plotting facilities.

To isolate heap fragmentation, the profiler fork()s separate process contexts before each run. To avoid problems with multiple equal random keys, the multimap variant of all containers is used. Here is the memory profile (also included in the STX B+ Tree tarball):

Memory profile of map containers

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Animation showing binary search and linear scan

STX B+ Tree Revisiting Binary Search

Posted on 2013-05-04 12:44 by Timo Bingmann at Permlink with 0 Comments. Tags: stx-btree

While developing another piece of software, I happened to require a customizable binary search implementation, which lead me to revisit the binary search function of my quite popular STX B+ Tree implementation.

The binary search is a central component in the container, both for performance and correctness, as it is used when traversing the tree in search for a key or an insertion point. It is implemented in the find_lower() (and find_upper()) functions.

In a first step, I cleaned the implementation to use an exclusive right boundary. In this binary search variant, hi points to the first element beyond the end (with the same meaning as usual end() C++ iterator). This got rid of the special case handled after the while loop. The exclusive right boundary is also a more natural implementation variant (even though most computer science textbooks contain the inclusive version).

Having thus changed the binary search, I reran the speed tests. However, to my surprise, the performance of the library decreased slightly, but consistently. See the code diff 39580c19 and resulting speed test PDF, where solid lines are after the patch and dashed ones before.

After some research, I found a good, independent study of binary search variants by Stephan Brumme. His summary is that a linear scan is more efficient than binary search, if the keys are located in only one cache line. This (of course) explained the performance decrease I measured, as my "special case" after the search was in fact a very short linear scan of two element.

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Thumbnail of a pie chart filling to 100%

Released disk-filltest 0.7 - Simple Tool to Detect Bad Disks by Filling with Random Data

Posted on 2013-03-27 21:32 by Timo Bingmann at Permlink with 0 Comments. Tags: c++ utilities

This post announces the first version of disk-filltest, a very simple tool to test for bad blocks on a disk by filling it with random data. The function of disk-filltest is simple:

See the disk-filltest project page for more information about version 0.7.


Memory profile plot as generated by example in malloc_count tarball

Released malloc_count 0.7 - Tools for Runtime Memory Usage Analysis and Profiling

Posted on 2013-03-16 22:17 by Timo Bingmann at Permlink with 1 Comments. Tags: c++ coding tricks

This post announces the first version of malloc_count, a very useful tool that I have been fine-tuning in the past months. The code library provides facilities to

The code tool works by intercepting the standard malloc(), free(), etc functions. Thus no changes are necessary to the inspected source code.

See the malloc_count project page for more information about version 0.7.


Instacode coloring of assembler code

Coding Tricks 101: How to Save the Assembler Code Generated by GCC

Posted on 2013-01-24 18:07 by Timo Bingmann at Permlink with 2 Comments. Tags: c++ coding tricks frontpage

This is the first issue of a series of blog posts about some Linux coding tricks I have collected in the last few years.

Folklore says that compilers are among the most complex computer programs written today. They incorporate many optimization algorithms, inline functions and fold constant expressions; all without changing output, correctness or side effects of the code. If you think about it, the work gcc, llvm and other compilers do is really amazing and mostly works just great.

Sometimes, however, you want to know exactly what a compiler does with your C/C++ code. Most straight-forward questions can be answered using a debugger. However, if you want to verify whether the compiler really applies those optimizations to your program, that your intuition expects it to do, then a debugger is usually not useful, because optimized programs can look very different from the original. Some example questions are:

These questions can be answered definitely by investigating the compiler's output. On the Net, there are multiple "online compilers," which can visualize the assembler output of popular compilers for small pieces of code: see the "GCC Explorer" or "C/C++ to Assembly v2". However, for inspecting parts of a larger project, these tools are unusable, because the interesting pieces are embedded in much larger source files.

Luckily, gcc does not output binary machine code directly. Instead, it internally writes assembler code, which then is translated by as into binary machine code (actually, gcc creates more intermediate structures). This internal assembler code can be outputted to a file, with some annotation to make it easier to read.

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Example of the Inducing Process

eSAIS - Inducing Suffix and LCP Arrays in External Memory

Posted on 2012-11-19 15:49 by Timo Bingmann at Permlink with 2 Comments. Tags: research stringology stxxl c++

This web page accompanies our conference paper "Inducing Suffix and LCP Arrays in External Memory", which we presented at the Workshop on Algorithm Engineering and Experiments (ALENEX 2013). A PDF of the publication is available from this site as alenex13esais.pdf alenex13esais.pdf or from the online proceedings. The paper was joint work with my colleagues Johannes Fischer and Vitaly Osipov.

Download alenex13esais.pdf

The slides to my presentation of the paper on January 7th, 2013 in New Orleans, LA, USA is available: alenex13esais-slides.pdf alenex13esais-slides.pdf. They contain little text and an example of the eSAIS algorithm with a simplified PQ.

Download alenex13esais-slides.pdf

We have also submitted a full version of the eSAIS paper to a journal. Due to long publication cycles, we make a pre-print of the journal article is available here: esais-preprint.pdf esais-preprint.pdf. The full paper contains more details on the inducing algorithm for the LCP array and additional experimental details.

Download esais-preprint.pdf

Our implementations of eSAIS, the eSAIS-LCP variants, DC3 and DC3-LCP algorithms as described in the paper are available below under the GNU General Public License v3 (GPL).

eSAIS and DC3 with LCP version 0.5.4 (current) updated 2013-12-13
Source code archive:
(includes STXXL 1.4.0)
eSAIS-DC3-LCP-0.5.4.tar.bz2 eSAIS-DC3-LCP-0.5.4.tar.bz2 (1.37 MiB) Browse online
Git repositories Suffix and LCP construction algorithms
git clone https://github.com/bingmann/eSAIS
cd eSAIS; git submodule init; git submodule update
STXXL 1.4.0
git clone https://github.com/stxxl/stxxl

For more information about compiling and testing the implementation, please refer to the README included in the source.

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The QuadClip Algorithm

Finding Roots of Polynomials by Clipping - Report and Implementation from my Lab Course in Numerical Mathematics

Posted on 2012-03-20 22:29 by Timo Bingmann at Permlink with 0 Comments. Tags: maths university c++

This semester I had the pleasure to take part in a lab exercise course supervised by Prof. Thomas Linß at the FernUniversity of Hagen. The objective was to comprehend, implement and evaluate a particular recent advancement in the field of numerical mathematics. My topic was finding the roots of a polynomial by clipping in Bézier representation using two new methods, one devised by Michael Bartoň and Bert Jüttler [1], the other extended from the first by Ligang Liu, Lei Zhang, Binbin Lin and Guojin Wang [2].

My implementation of this topic was done for the lab course in C++ and contains many in themselves interesting sub-algorithms, which are combined into the clipping algorithms for finding roots. These sub-algorithms may prove useful for other purposes, which is the main reason for publishing this website. Among these are:

For the lab course I wrote two documents, both in German: one is an abstract Kurzfassung.pdf Kurzfassung.pdf (1 page), which is translated into English below, and the other a short report Ausarbeitung.pdf Ausarbeitung.pdf (6 pages). The report contains a short description of the algorithms together with execution and convergence speed measurements, which verify the original authors experiments. For presenting the lab work I created these Slides.pdf Slides.pdf, which however are not self-explanatory due to my minimum-text presentation style.

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Drawing of PG(2,3)

Vervollständigung meiner Seminararbeit in Diskreter Mathematik

Posted on 2011-08-31 20:12 by Timo Bingmann at Permlink with 0 Comments. Tags: maths university

Heute habe ich meine Seminararbeit in Diskreter Mathematik an der FernUni Hagen vervollständigt. Ich möchte mich bei Prof. Hochstättler bedanken für die erfahrene Leitung und planvolle Einführung in die interessanten Gebiete der Design Theorie und endlicher Geometrien. Die Arbeit ist auf deutsch und unter folgendem Link downloadbar.

Download PDF: Seminararbeit-Singer-Differenzenmengen.pdf Seminararbeit-Singer-Differenzenmengen.pdf (2.79 MiB).

Download Seminararbeit-Singer-Differenzenmengen.pdf

Zusammenfassung

Die Entwicklung von Differenzenmengen in der Design Theorie begann 1938 mit einer algebraischen Untersuchung endlicher projektiver Geometrien von James Singer. Nach Einführung der wichtigsten Grundbegriffe der Design Theorie und projektiver Geometrien werden in dieser Ausarbeitung die beiden Resultate von Singer entwickelt. Die Existenz einer Kollineation mit Periode \frac{n^{d+1}-1}{n-1} führt zur Definition von Differenzenmengen und beide Ergebnisse liefern elegante Konstruktionsverfahren für PG(d,p^k).


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