Commit a9813bd2 authored by He Guanlin's avatar He Guanlin
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Update README.md

parent 69909f46
......@@ -6,17 +6,17 @@ Optimized parallel implementations of the k-means clustering algorithm:
In particular, for both implementations we use a two-step summation method with package processing to handle the effect of rounding errors that may occur during the phase of updating cluster centroids.
Makefile Configuration
## Makefile Configuration
- By commenting the "-DDP" option or not, our code supports computations either in single or double precision, respectively.
- The choice for the "--gpu-architecture" option should be updated according to your own GPU device.
- If necessary, update the CUDA path according to your own situation.
"main.h" Configuration
## "main.h" Configuration
The configuration for benchmark dataset, block size, etc., are adjustable in the "main.h" file.
Our CUDA C code does not generate any synthetic data, so users should specify the path and filename of their benchmark dataset in the "INPUT_DATA" constant, and also give the NbPoints, NbDims, NbClusters. If users want to impose the initial centroids, they should provide a text file containing the coordinates of initial centroids and specifiy the corresponding path and filename in the "INPUT_INITIAL_CENTROIDS" constant.
The synthetic dataset used in our papers below is too large (about 1.8GB) to be loaded here. So we provide the Synthetic_Data_Generator.py instead. Since the generator uses the random function, the dataset generated each time will have different values but will always keep the same distribution.
Execution
## Execution
Before execution, recompile the code by entering the "make" command if any change has been made to the code.
Then you can run the executable file "kmeans" with several arguments:
-t <GPU|CPU>: run computations on target GPU or on target CPU (default: GPU)
......@@ -28,7 +28,7 @@ Example:
k-means on CPU: ./kmeans -t CPU -cpu-nt 20
k-means on GPU: ./kmeans
Corresponding papers
## Corresponding papers
The approaches and experiments are documented in the following two papers. The second paper is an extended version of the first paper.
He, G., Vialle, S., & Baboulin, M. (2021). Parallelization of the k-means algorithm in a spectral clustering chain on CPU-GPU platforms. In B. B. et al. (Ed.), Euro-par 2020: Parallel processing workshops (Vol. 12480, LNCS, pp. 135–147). Warsaw, Poland: Springer. Available from: https://link.springer.com/chapter/10.1007/978-3-030-71593-9_11
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