Question: Simple Bioinformatics Algorithms Using Parallel Computing (Cuda)?
6
gravatar for Newvin
9.9 years ago by
Newvin350
Newvin350 wrote:

Hi,

I am a bioinformatics graduate student looking to do a short project using CUDA. I would like compare the performance of simple bioinformatics algorithms executing on either the CPU or GPU. Thus, for each selected algorithm, I would code one version in C and another using CUDA. Execution times would then be compared and analyzed.

I am wondering if anyone can suggest some simple algorithms or program ideas that might lend themselves to parallel computation. I am thinking about very simple problems...an example might be sequence alignment...

I do have a good amount of programming experience and have used CUDA for about a month now.

Any help would be appreciated and Thank You!

parallel C algorithm • 4.6k views
ADD COMMENTlink modified 9.7 years ago by Jarretinha3.3k • written 9.9 years ago by Newvin350

Thanks to all for your great ideas. I decided to go with the ORF finder, but if I have extra time I may try to implement some of the algorithms others have suggested as well.

ADD REPLYlink written 9.9 years ago by Newvin350
4
gravatar for Prateek
9.9 years ago by
Prateek1.0k
Boston, MA
Prateek1.0k wrote:
  1. You could write an ORF (open reading frames) finder for 6 frames of reference given a multi-fasta file.
ADD COMMENTlink written 9.9 years ago by Prateek1.0k

Nice and simple!

ADD REPLYlink written 9.9 years ago by Eric Normandeau10k
3
gravatar for Jarretinha
9.9 years ago by
Jarretinha3.3k
São Paulo, Brazil
Jarretinha3.3k wrote:

You can use this as an example:

Parallelized pairwise sequence alignment using CUDA on multiple GPUs

Other very important examples of algorithms that might benefit from CUDA are:

  • Maximum likelihood calculations (linear models, phylogenetics);
  • Transforms and their kernels (Radon, Burrows-Wheeler);
  • CYK/Backward/Viterbi algorithms;
  • Combinatorial optimization fo GWAS;

I would stick to MSA algorithms. They're better explored and are easier to bechmark. The most wanted by now are CO for GWAS, used to assess high order interactions between SNPs. Most approaches use pure brute-force. Hence, a parallel algorithm would provide a huge improvement.

ADD COMMENTlink written 9.9 years ago by Jarretinha3.3k
2
gravatar for Pierre Lindenbaum
9.9 years ago by
France/Nantes/Institut du Thorax - INSERM UMR1087
Pierre Lindenbaum133k wrote:

I would try to map each pair of primers from UniSTS on the human Genome.

ADD COMMENTlink written 9.9 years ago by Pierre Lindenbaum133k
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