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f1e8307308
2. Simplify handling of special cases by taking advantage of the fact that the builtin vrsqrt approximation handles negative, zero and +inf arguments correctly. This speeds up the SSE and AVX implementations by ~20%. 3. Make the Newton-Raphson formula used for rsqrt more numerically robust: Before: y = y * (1.5 - x/2 * y^2) After: y = y * (1.5 - y * (x/2) * y) Forming y^2 can overflow for very large or very small (denormalized) values of x, while x*y ~= 1. For AVX512, this makes it possible to compute accurate results for denormal inputs down to ~1e-42 in single precision. 4. Add a faster double precision implementation for Knights Landing using the vrsqrt28 instruction and a single Newton-Raphson iteration. Benchmark results: https://bitbucket.org/snippets/rmlarsen/5LBq9o |
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| .. | ||
| src | ||
| Cholesky | ||
| CholmodSupport | ||
| CMakeLists.txt | ||
| Core | ||
| Dense | ||
| Eigen | ||
| Eigenvalues | ||
| Geometry | ||
| Householder | ||
| IterativeLinearSolvers | ||
| Jacobi | ||
| KLUSupport | ||
| LU | ||
| MetisSupport | ||
| OrderingMethods | ||
| PardisoSupport | ||
| PaStiXSupport | ||
| QR | ||
| QtAlignedMalloc | ||
| Sparse | ||
| SparseCholesky | ||
| SparseCore | ||
| SparseLU | ||
| SparseQR | ||
| SPQRSupport | ||
| StdDeque | ||
| StdList | ||
| StdVector | ||
| SuperLUSupport | ||
| SVD | ||
| UmfPackSupport | ||