vectorised a lot of operations, liberal use of numba kernels #73
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mladenivkovic
requested changes
Aug 25, 2020
| dist = dist[mask] | ||
| neighs = neighs[mask] | ||
| # do neighbour loops | ||
| block_size = 65536 |
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Why this particular block size?
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| if neighs.shape[0] == 0: | ||
| raise RuntimeError("Found no neighbour for a particle.") |
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This was up here for a reason. dist = dist[correct][1:] below will crash the code otherwise.
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| h[p] = dist[-1] / kernel_gamma | ||
| W = kernel_func(dist, [dist[-1]] * len(dist)) | ||
| rho[p] += np.sum(self.m[neighs] * W) |
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My first version was vectorised like this ... :/
| neighs = neighs[correct][1:] | ||
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| if neighs.shape[0] == 0: | ||
| raise RuntimeError("Found no neighbour for a particle.") |
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Again this might crash in 1276 when calling dist = dist[correct][1:]
| from swiftsimio.accelerated import jit | ||
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| @jit(nopython=True, fastmath=True) | ||
| def correct_for_periodic(dx, ndim, boxsize): |
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Some documentation would be great here that this is for distances, not particle positions
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Hi @mladenivkovic - this includes significant speed improvements (over 10x) on your IC code. It still needs work but we're getting there!