"Flexible Analysis Software for Emerging Architectures." Kenneth Moreland, Brad King, Robert Maynard, and Kwan-Liu Ma. In 2012 SC Companion (Proceedings of Petascale Data Analytics: Challenges and Opportunities), November 2012, pg. 821–826. DOI 10.1109/SC.Companion.2012.115.
We are on the threshold of a transformative change in the basic architecture of high-performance computing. The use of accelerator processors, characterized by large core counts, shared but asymmetrical memory, and heavy thread loading, is quickly becoming the norm in high performance computing. These accelerators represent significant challenges in updating our existing base of software. An intrinsic problem with this transition is a fundamental programming shift from message passing processes to much more fine thread scheduling with memory sharing. Another problem is the lack of stability in accelerator implementation; processor and compiler technology is currently changing rapidly. In this paper we describe our approach to address these two immediate problems with respect to scientific analysis and visualization algorithms. Our approach to accelerator programming forms the basis of the Dax toolkit, a framework to build data analysis and visualization algorithms applicable to exascale computing.
Flexible Analysis Software for Emerging Architectures
I built two simple programs to provide the empirical results from this paper. The first is called thresholdexample that loads in a sample data set and runs the Dax threshold algorithm using one of the many device adapters available in Dax. The second is called pistoncompare that performs, as close as possible, a comparison between identical threshold operations in Dax and PISTON. Below are all the source you should need to replicate the results.
In addition, you will of course need the appropriate build tools and libraries: CMake, CUDA, an OpenMP-compliant C++ compiler, and the TBB library.
The paper also features some timings from the VTK threshold algorithm. These timings were performed using the ParaView 3.14 binary provided by Kitware. I executed this Python script from ParaView to run the threshold algorithm for the 20 trials.
Since the writing of the paper, changes to the Dax API mean the original examples no longer compile with the latest version of Dax. I will try to periodically update the code to work against the most recent versions of Dax and PISTON. If you find they no longer work, you can send me an email and hopefully I'll be able to get to in a reasonable amount of time. (Even better, you can help me fix the problem.)
Unfortunately, I am unable to post the data set I used as, to the best of my knowledge, it has not been released openly. However, the data is a simple brick of floats, so it should be trivial to load a similar data set if you cannot get hold of this one.
Here you can find the detailed timing numbers for the experimental results.
To uses these data to get plots like those in the paper, you will need to use something like a pivot table. (For what it's worth, I used Tableau, which is really a visual pivot table and plotter.)