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Batched Serial Eigendecomposition #873
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@kyungjoo-kim Curious on your thoughts on this one - the general non-MKL eigendecomposition code path would be very useful on device and host |
@srajama1 Who is best to help out with this one? |
@vqd8a can you take this question ? |
@srajama1 Yes, sure. |
@vqd8a any updates? |
@sslattery So sorry, I have not had time for it over the past few days. Let me look at it this week. |
Uncommenting the code, I get incorrect results for the following matrix: A = { { 0.5, 0.0, 2.5 , 0.0, 0.0 },
{ 0.0, 0.5, 0.0 , 0.0, 0.0 },
{ 0.0, 0.0, 0.5 , 0.0, 0.4 },
{ 0.0, 0.0, 0.0 , 0.5, 0.0 },
{ 0.0, 0.0, 1.05, 0.0, 0.5 } }; This matrix has all real eigenvalues, but the kernel is returning one pair of complex eigenvalues. |
@sslattery @terwin Sorry for my late reply. |
@vqd8a The general eigendecomposition is the most critical for us right now. An SVD is also important for us but second in priority. Thanks! |
@vqd8a Also note that it is critical for us to have a fully templated code path and therefore the internal implementation rather than those that called MKL or LAPACK were necessary for us. |
@sslattery Thanks for letting me know. I will work on the general eigendecomposition. |
Looking at the eigendecomposition code here:
kokkos-kernels/src/batched/KokkosBatched_Eigendecomposition_Serial_Internal.hpp
Line 24 in f64b1c5
What is the state of the device code path without MKL (e.g. the general code path starting here:
kokkos-kernels/src/batched/KokkosBatched_Eigendecomposition_Serial_Internal.hpp
Line 123 in f64b1c5
Is MKL the only issue or does this code path also need testing before it is available on device?
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