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pandoc:introduction-to-vsc:10_performance:10_performance [2018/01/31 13:17] – Pandoc Auto-commit pandoc | pandoc:introduction-to-vsc:10_performance:10_performance [2018/03/21 11:33] – Pandoc Auto-commit pandoc | ||
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* HPC (High Performance Computing): execution time spent on just **a few lines of code** | * HPC (High Performance Computing): execution time spent on just **a few lines of code** | ||
- | * Linear Algebra * (Dense) matrix multiplication: | + | * Linear Algebra |
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===== Know algorithmic complexity (2) ===== | ===== Know algorithmic complexity (2) ===== | ||
- | * Other codes * Identify **main parameters** * Identify **scaling** (usually polynomial) | + | * Other codes |
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* Large problem size => subalgorithm with highest exponent of complexity becomes dominant | * Large problem size => subalgorithm with highest exponent of complexity becomes dominant | ||
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===== Very important libraries ===== | ===== Very important libraries ===== | ||
- | * **Level 3 BLAS** (=“Matrix Multiplication”) * O(n^3) operations with O(n^2) memory accesses * portable performance (often faster by a factor of 10) * including triangular and symmetric / hermitian matrices * any three-loop operation with $C=\alpha AB + \beta C$ can be written using Level 3 BLAS | + | * **Level 3 BLAS** (=“Matrix Multiplication”) |
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- | * MKL: free **Math Kernel Library** from Intel * Linear Algebra * FFT * Neural networks * Statistics * Sparse algorithms * Parallel * .. | + | * MKL: free **Math Kernel Library** from Intel |
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* **HDF5**: Hierarchical Data Format: efficient and portable library | * **HDF5**: Hierarchical Data Format: efficient and portable library | ||