From 880a4d3ca9792ddc66de2b9fb5296741ad544641 Mon Sep 17 00:00:00 2001 From: Christopher Rackauckas Date: Mon, 23 Oct 2023 12:14:48 +0200 Subject: [PATCH] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8c7c443..f0026b9 100644 --- a/README.md +++ b/README.md @@ -564,7 +564,7 @@ GPU-specialized ODE solver `cuda.GPUTsit5()` to solve 10,000 ODEs on the GPU in parallel: ```py -sol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(CUDABackend()),trajectories=10000,saveat=0.01) +sol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(cuda.CUDABackend()),trajectories=10000,saveat=0.01) ``` For the full list of choices for specialized GPU solvers, see @@ -573,7 +573,7 @@ For the full list of choices for specialized GPU solvers, see Note that `EnsembleGPUArray` can be used as well, like: ```py -sol = de.solve(ensembleprob,de.Tsit5(),cuda.EnsembleGPUArray(CUDABackend()),trajectories=10000,saveat=0.01) +sol = de.solve(ensembleprob,de.Tsit5(),cuda.EnsembleGPUArray(cuda.CUDABackend()),trajectories=10000,saveat=0.01) ``` though we highly recommend the `EnsembleGPUKernel` methods for more speed. Given @@ -640,7 +640,7 @@ GPU-acceleration to the mix: ```py def time_func(): - sol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(CUDABackend()),trajectories=1000,saveat=0.01) + sol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(cuda.CUDABackend()),trajectories=1000,saveat=0.01) timeit.Timer(time_func).timeit(number=1) @@ -663,7 +663,7 @@ timeit.Timer(time_func).timeit(number=1) ```py def time_func(): - sol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(CUDABackend()),trajectories=10000,saveat=0.01) + sol = de.solve(ensembleprob,cuda.GPUTsit5(),cuda.EnsembleGPUKernel(cuda.CUDABackend()),trajectories=10000,saveat=0.01) timeit.Timer(time_func).timeit(number=1)