mirror of https://github.com/jlizier/jidt
Added docs about memory management in GPU.
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Pedro Martinez Mediano
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Memory management in KSG CUDA functions
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=======================================
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This file contains information about how memory is allocated and managed in the
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CUDA KSG calculators. It is meant to act as documentation and as a tool for
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future developers.
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All memory is allocated (in a single cudaMalloc call) and distributed in the
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function allocateDeviceMemory. The location of all pointers in memory is as
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follows (diagram not to scale):
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_____
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float *pointset -> |
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float *distances -> |
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int *indexes -> |
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int *npoints_x -> |
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int *npoints_y -> |
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float *digammas -> |
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|_____
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The pointset contains both the source and the target data. Source data is
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represented by a tensor X[i,j,k], where k runs across data samples, j across
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source dimensions and i across different surrogate shufflings (or different
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realisations of the process). Assume we have R realisations of a process with M
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variables, of N samples each. Then the arrangement of X in memory is:
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_____ _
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float *pointset, *source -> | X[0,0,0] |
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| X[0,0,1] |
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| . |
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| . | Shuffle 0, dimension 0
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| . |
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| X[0,0,N] _
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| X[1,0,0] |
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| X[1,0,1] |
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| . |
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| . | Shuffle 1, dimension 0
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| . |
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| X[1,0,N] _|
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| .
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| . ... Dimension 0 of all other shuffles
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| .
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| X[R,0,N] _
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| X[0,1,0] |
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| X[0,1,1] |
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| . |
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| . | Shuffle 0, dimension 1
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| . |
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| X[0,1,N] _|
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| X[1,1,0] |
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| X[1,1,1] |
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| . | Shuffle 1, dimension 1
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| . |
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| . |
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| X[1,1,N] _|
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| .
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| . ... All other dimensions of all other shuffles
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| .
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| X[R,M,N]
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float *dest -> |
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|_____
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dest follows the same structure as source.
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Distances contains the distances between each point and its K nearest
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neighbours. D[p,q,r] represents the distance from point r to its q'th
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neighbour, for each shuffling p. Assume we have R realisations of a process of
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N samples each, and we are finding the K nearest neighbours of each point. Then
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the arrangement of D in memory is as follows:
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_____ _
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float *distances -> | D[0,0,0] |
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| D[0,0,1] |
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| . |
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| . | Shuffle 0, distances to 1st neighbour
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| . |
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| D[0,0,N] _|
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| D[1,0,0] |
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| D[1,0,1] |
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| . |
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| . | Shuffle 1, distances to 1st neighbour
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| . |
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| D[1,0,N] _|
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| .
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| . ... Distances to 1st neighbour of all other surrogates
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| .
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| D[R,0,N] _
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| D[0,1,0] |
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| D[0,1,1] |
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| . |
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| . | Shuffle 0, distances to 2nd neighbour
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| . |
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| D[0,1,N] _|
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| .
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| . ... Distances to all other neighbours of all other surrogates
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| .
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| D[R,K-1,N]_
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float *radii -> | D[0,K,0] |
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| D[0,K,1] |
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| . |
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| . | Shuffle 0, distances to K-th neighbour
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| . |
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| D[1,K,N] _|
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| .
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| . ... Distances to K-th neighbour of all other surrogates
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| .
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|_____ D[R,K,N]
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The distances from each point to its K-th nearest neighbour are particularly
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important since they are used in other parts of the algorithm, and they are
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called the range search radii.
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Indexes contain the index of each nearest neighbour of each point in the main
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pointset, and follow the same structure as distances.
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Npoints contains the count of points -- in either the source (nx) or the dest
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(ny) -- that lie within the range search radius of each point. NX[i,j] represents
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the number of points in source which are closer to $j$ than $j$'s radii.
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_____
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int *npoints, *nx -> | NX[0,0]
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| NX[0,1]
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| .
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| .
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| .
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| NX[0,N]
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| NX[1,0]
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| NX[1,1]
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| .
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| .
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| .
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| NX[R,N]
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int *ny -> | NY[0,0]
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| .
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| .
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| .
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|_____ NY[R,N]
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The array ny contains the equivalent point count in dest, and follows the same
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structure as nx.
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Digammas also follow the same structure as nx and ny, and are calculated as
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digammas[i] = digamma(nx[i]+1) + digamma(ny[i]+1)
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Reference: Kraskov, A., Stoegbauer, H., Grassberger, P., "Estimating mutual
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information", Physical Review E 69, (2004) 066138.
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