28 lines
2 KiB
Text
28 lines
2 KiB
Text
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== Homework 4
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Michael Zhang \<zhan4854\@umn.edu\>
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1. A short description of how you went about parallelizing the k-means algorithm. You should include how you decomposed the problem and why, i.e., what were the tasks being parallelized.
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My parallelized program included the following procedures:
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- `findDistanceToCentroid` - This computes an $N times K$ matrix of distances from each data point to each centroid.
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- `assignClosestCentroid` - This reduces the distances to find the minimum distance for each centroid, and assigns the closest one to an $N times 1$ vector.
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- `recentralizeCentroidSum` - This computes a sum for the purposes of averaging, and also counts the number of elements in each cluster.
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- `recentralizeCentroidDiv` - This uses the count from the previous step and divides everything in parallel.
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I tried to make sure every thread is computing approximately one single for-loop's worth of data, most of the time over the $d$ axis
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2. Give details about how many elements and how the computations in your kernels are handled by a thread.
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Threads are dynamically allocated
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3. Ensure you include details about the thread hierarchy, i.e., whether the threads are organized in a 1D, 2D, or, 3D fashion in a thread-block, and whether the thread-blocks are arranged 1D, 2D, or, 3D grid. NOTE: If you choose to write CUDA kernels where the number of thread blocks is determined dynamically by the program during runtime, then send -1 as the input argument for the number of thread blocks to the invocation. In your program, use -1 as a flag to indicate that the number of thread blocks will need to be computed during runtime.
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4. You need to perform a parameter study in order to determine how the number of elements processed by a thread and the size of a thread-block, i.e., the \# threads in a block, affect the performance of your algorithm. Your writeup should contain some results showing the runtime that you obtained for different choices.
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5. You should include results on the 'large_cpd.txt' dataset with 256, 512, and 1024 clusters.
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