DeepLearning4j k-means 非常慢

Question

I'm trying to use DL4J's K-Means implementation. I set it up as follows:

int CLUSTERS = 5;
int MAX_ITERATIONS = 300;
String DISTANCE_METRIC = "cosinesimilarity";
KMeansClustering KMEANS = KMeansClustering.setup(CLUSTERS, MAX_ITERATIONS, DISTANCE_METRIC);

My data points are vectors of size 300 (doubles), and my test set is comprised of ~ 100 data points each time (give or take). I'm running it on my CPU (4 cores) in a single threaded fashion.

Evaluation takes a very long time (a few seconds per example).

I took a peek inside the algorithm's implementation and it looks like its concurrency level is very high - a lot of threads are being created (one per data point, to be exact) and executed in parallel. Perhaps this is an overkill? Is there any way I can control it through configuration? Other ways to speed it up? If not, is there any other fast java-based solution for executing k-means?

Answer 1

"DL4J supports GPUs and is compatible with distributed computing software such as Apache Spark and Hadoop." from https://deeplearning4j.org

Extra Spark or Hadoop instance might help for scaling performance.