Hi all, I would like to know if there's a way to tweak the performance of the aCGH package, particularly the find.hmm.states function Input dataset Agilent CNV 31 samples 200,000 clones Hardware 2 Intel Xeon Dual Core 3GHz (total of 4CPUs) 4 GB RAM Windows 2003 Server Edition Software R version 2.7.0 (2008-04-22) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] tools splines stats graphics grDevices utils datasets [8] methods base other attached packages: [1] aCGH_1.14.0 sma_0.5.15 multtest_1.20.0 Biobase_2.0.0 [5] survival_2.34-1 cluster_1.11.10 Function Call hmm(ex.acgh)<-find.hmm.states(ex.acgh) I am familiar with OpenMP. Is it possible to include these openMP pragmas into the function to speed up the computation? This is a concern as i will be moving onto an Illumina SNP dataset with 59 samples and 400,000 clones. Or would running it on a Linux machine be faster? dear moderators, Please direct me to the right forum if you think that this should be on the BioC-Dev mailing list instead. regards thank you -- Julian Lee Bioinformatics Specialist Cellular and Molecular Research National Cancer Center Singapore

Dear Julian, We have parallelized (over arrays or arrays * chromosomes) the calls to find.hmm (as well as other aCGH methods) using MPI. The R code is available from the ADaCGH package from CRAN. (The paper describing the approach, showing benchmarks, etc, is available from http://www.ploson e.org/article/fetchArticle.action?articleURI=info%3Adoi%2F10.1371%2Fjo urnal.pone.0000737). HTH, R. -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch on behalf of Julian Lee Sent: Tue 06-May-08 11:03 To: bioconductor Subject: [BioC] aCGH package - performance Issues Hi all, I would like to know if there's a way to tweak the performance of the aCGH package, particularly the find.hmm.states function Input dataset Agilent CNV 31 samples 200,000 clones Hardware 2 Intel Xeon Dual Core 3GHz (total of 4CPUs) 4 GB RAM Windows 2003 Server Edition Software R version 2.7.0 (2008-04-22) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] tools splines stats graphics grDevices utils datasets [8] methods base other attached packages: [1] aCGH_1.14.0 sma_0.5.15 multtest_1.20.0 Biobase_2.0.0 [5] survival_2.34-1 cluster_1.11.10 Function Call hmm(ex.acgh)<-find.hmm.states(ex.acgh) I am familiar with OpenMP. Is it possible to include these openMP pragmas into the function to speed up the computation? This is a concern as i will be moving onto an Illumina SNP dataset with 59 samples and 400,000 clones. Or would running it on a Linux machine be faster? dear moderators, Please direct me to the right forum if you think that this should be on the BioC-Dev mailing list instead. regards thank you -- Julian Lee Bioinformatics Specialist Cellular and Molecular Research National Cancer Center Singapore _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor **NOTA DE CONFIDENCIALIDAD** Este correo electr?nico, y ...{{dropped:3}}

Hi R, wonderful article. 8 algorithms in MPI. I would so love to test your code but unfortunately i do not have the luxury of a linux cluster here(that however can be fixed ;)). I do however have a Sun v490, 4 dual core UltraSparcIV++ with 32GB RAM. I presume it'll work on this SMP too, however any concerns if i were to take this onto a Solaris machine? regards ----- Original Message ----- From: "Diaz.Ramon" <rdiaz@cnio.es> To: "Julian Lee" <julian at="" omniarray.com="">, "bioconductor" <bioconductor at="" stat.math.ethz.ch=""> Sent: Tuesday, May 6, 2008 2:19:36 AM GMT -08:00 US/Canada Pacific Subject: RE: [BioC] aCGH package - performance Issues Dear Julian, We have parallelized (over arrays or arrays * chromosomes) the calls to find.hmm (as well as other aCGH methods) using MPI. The R code is available from the ADaCGH package from CRAN. (The paper describing the approach, showing benchmarks, etc, is available from http://www.ploson e.org/article/fetchArticle.action?articleURI=info%3Adoi%2F10.1371%2Fjo urnal.pone.0000737). HTH, R. -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch on behalf of Julian Lee Sent: Tue 06-May-08 11:03 To: bioconductor Subject: [BioC] aCGH package - performance Issues Hi all, I would like to know if there's a way to tweak the performance of the aCGH package, particularly the find.hmm.states function Input dataset Agilent CNV 31 samples 200,000 clones Hardware 2 Intel Xeon Dual Core 3GHz (total of 4CPUs) 4 GB RAM Windows 2003 Server Edition Software R version 2.7.0 (2008-04-22) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] tools splines stats graphics grDevices utils datasets [8] methods base other attached packages: [1] aCGH_1.14.0 sma_0.5.15 multtest_1.20.0 Biobase_2.0.0 [5] survival_2.34-1 cluster_1.11.10 Function Call hmm(ex.acgh)<-find.hmm.states(ex.acgh) I am familiar with OpenMP. Is it possible to include these openMP pragmas into the function to speed up the computation? This is a concern as i will be moving onto an Illumina SNP dataset with 59 samples and 400,000 clones. Or would running it on a Linux machine be faster? dear moderators, Please direct me to the right forum if you think that this should be on the BioC-Dev mailing list instead. regards thank you -- Julian Lee Bioinformatics Specialist Cellular and Molecular Research National Cancer Center Singapore _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor **NOTA DE CONFIDENCIALIDAD** Este correo electr?nic...{{dropped:22}}