K Means¶

This is HLS C based K-Means clustering Implementation for Xilinx FPGA Devices. K-means clustering is a method of vector quantization, that is popular for cluster analysis in data mining. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Command line argument flags:

-x: : Used to specify kernel xclbin
-i: : File containing data to be clustered
-c: : Golden file for comparison
-n: : Used to specify number of clusters
-o: : Output cluster center coordinates(default=off)

KEY CONCEPTS: K-Means, Multiple compute units

KEYWORDS: DATAFLOW, ARRAY PARTITION

The application sets two compute units by default so that it will fit all Xilinx Devices. For bigger Xilinx Devices, user can increase the number of Compute units in krnl_kmeans.cfg, kmeans_config.h and config.mk files for achieving better performance. It is tested upto 8 compute units for U200 device and got better results when compared to single compute units, which shows that application is more compute bound (not memory bound).

Following is the real log reported while running the design on U200 platform with 8 compute units:

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  Performance Summary
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  Device Initialization      :    3224.1098 ms
  Buffer Allocation          :      33.4063 ms
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  Compute Memberships        :   11490.2810 ms
  Update Delta               :    1387.0610 ms
  Update Centers             :    1745.7366 ms
  Update Clusters            :      41.0705 ms
  Total K-Means Compute Time :   15208.3733 ms
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EXCLUDED PLATFORMS:

All Embedded Zynq Platforms, i.e zc702, zcu102 etc

All Versal Platforms, i.e vck190 etc

u200_gen3x16

Alveo U30

All NoDMA Platforms, i.e u50 nodma etc

Samsung U.2 SmartSSD

Samsung SmartSSD Computation Storage Drive

DESIGN FILES¶

Application code is located in the src directory. Accelerator binary files will be compiled to the xclbin directory. The xclbin directory is required by the Makefile and its contents will be filled during compilation. A listing of all the files in this example is shown below

data/100
data/100.gold_c10
data/100.gold_c5
src/fpga_kmeans.cpp
src/fpga_kmeans.h
src/host.cpp
src/kmeans.h
src/kmeans_clustering_cmodel.c
src/kmeans_config.h
src/krnl_kmeans.cpp
src/krnl_kmeans.h
src/timer.h

Access these files in the github repo by clicking here.

COMMAND LINE ARGUMENTS¶

Once the environment has been configured, the application can be executed by

./kmeans -x <krnl_kmeans XCLBIN> -i ./data/100 -c ./data/100.gold_c10 -n 10