Scalable Multicore Code for RNA Secondary Structure Prediction
The GTfold package includes fast, scalable multicore code for predicting RNA secondary structure that is one to two orders of magnitude faster than the de facto standard programs and achieves comparable accuracy of prediction.
We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. GTfold, which is implemented in C/C++ and uses OpenMP primitives for parallelization of the algorithm, opens up a new path for the algorithmic improvements and the application of improved thermodynamic models to increase prediction accuracy.
We have binaries available for 64 bit Linux machines and Mac OSX. (Windows binaries are currently only available for Version 2.0) Please see Guide page for usage details.
|Mac OSX (64-bit)||gtfold-3.0_x86-64_osx.tar.gz|
|Cygwin (32-bit) GTfold 2.0 only||gtfold-2.0_x86_cygwin.tar.gz|
If you wish to download and compile GTfold from the latest source code, please visit the Develop page.
- David A Bader
- Christine E Heitsch
- Stephen Harvey
- George Johnston
- Christopher Mize
- Manoj Soni
- M. Shel Swenson
M.S. Swenson, J. Anderson, A. Ash, P. Gaurav, Z.Sukos, D.A. Bader, S.C. Harvey, and C.E. Heitsch. 2012. "GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops." BMC Research Notes. 5(1):341.
A. Mathuriya, D.A. Bader, C.E. Heitsch, and S.C. Harvey. "GTfold: A Scalable Multicore Code for RNA Secondary Structure Prediction." 24th Annual ACM Symposium on Applied Computing (SAC), Computational Sciences Track, Honolulu, HI, March 8-12, 2009.