‹‹ Back to SVS Home
- Installing and Initializing
- Understanding the Interface and Workflow
- Importing Your Data Into A Project
- Genetic Marker Maps and Affymetrix Library Files
- Genetic Marker Maps Overview
- Convert Text File into Marker Map DSM Format
- Download from Golden Helix, Inc.
- Download Affymetrix Annotation Files
- Managing the MarkerMaps Folder
- Applying a Genetic Marker Map to a Spreadsheet
- Exporting an Applied Genetic Marker Map to a DSM file
- Downloading Affymetrix Library (CDF) Files
- Spreadsheets
- Scripting and Other Integrated Statistical Tools
- Data Quality Assessment
- Quality Assurance Overview
- Quality Assurance Procedures
- Genotype Statistics by Marker
- Genotype Filtering by Marker
- Genotype Statistics by Sample
- PBAT Family-Based QC Statistics
- Principal Component Analysis Overview
- Genotypic Principal Component Analysis
- Numeric Principal Component Analysis
- Correcting for Stratification by Genomic Control
- Identity by Descent Estimation
- Inbreeding Coefficients
- LD Pruning
- Wave Detection and Correction
- Analysis
- PBAT Family-Based Analysis
- Copy Number Analysis
- Sequence Analysis
- Visualizing Data
- Genome Browser
- Export Options for Data and Plots
- Formulas and Theories: The Science Behind SNP and Variation Suite
- Appendices
- EULA
- Installing the Third-Party Condor ®
- Extracting Affymetrix Copy Number Data for use in SVS
- Exporting Data from GenomeStudio
- Platform Notes
- Hardware Acceleration
- A Glossary of Terms Used in Genetic Analysis
- References
Hardware Acceleration
21 Hardware Acceleration
As of version 7.4, SVS supports hardware accelerated analysis through OpenCL. This allows SVS to use highly parallel devices such as Graphics Processing Units (GPUs) to speed up certain tasks. Whereas traditional CPUs use several very fast cores, GPUs use hundreds of moderately fast cores to achieve their high performance. Many analysis tasks(such as CNAM Optimal Segmentation) can be efficiently divided across many cores. We have found that GPUs are able to perform copy number segmentation anywhere from 5 to 20 times faster than traditional CPUs.