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. K. Kulshreshtha, A. Koniaeva 7 / 13 <strong>Vectorizing</strong> <strong>ADOL</strong>-C using CUDA Euro AD 10.06.2013.Vector <strong>forward</strong> more: in parallelF : R n −→ R m , x ∈ R n , Ẋ ∈ R n×pẎ = F ′ (x)Ẋ ∈ R m×pḞ(x, Ẋ) := [F(x), F ′ (x)Ẋ]Vector <strong>forward</strong> <strong>mode</strong>In parallel• Can easily be parallelised if derivatives required at a large number <strong>of</strong>evaluation points x• Can also be parallelised to propogate several direction simultaneously
. K. Kulshreshtha, A. Koniaeva 7 / 13 <strong>Vectorizing</strong> <strong>ADOL</strong>-C using CUDA Euro AD 10.06.2013.Vector <strong>forward</strong> more: in parallelF : R n −→ R m , x ∈ R n , Ẋ ∈ R n×pẎ = F ′ (x)Ẋ ∈ R m×pḞ(x, Ẋ) := [F(x), F ′ (x)Ẋ]Vector <strong>forward</strong> <strong>mode</strong>• Parallel evaluation at different points:• Scaer <strong>the</strong> evaluation points in <strong>the</strong> grid or among blocksIn parallel
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Vectorizing the forward mode of ADOL-C on a GPU ... - Autodiff.org

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