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[SPARK-10354] [MLLIB] fix some apparent memory issues in k-means|| initializaiton #8526

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[SPARK-10354] [MLLIB] fix some apparent memory issues in k-means|| initializaiton #8526

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21 changes: 14 additions & 7 deletions mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -369,7 +369,7 @@ class KMeans private (
: Array[Array[VectorWithNorm]] = {
// Initialize empty centers and point costs.
val centers = Array.tabulate(runs)(r => ArrayBuffer.empty[VectorWithNorm])
var costs = data.map(_ => Vectors.dense(Array.fill(runs)(Double.PositiveInfinity))).cache()
var costs = data.map(_ => Array.fill(runs)(Double.PositiveInfinity))

// Initialize each run's first center to a random point.
val seed = new XORShiftRandom(this.seed).nextInt()
Expand All @@ -394,21 +394,28 @@ class KMeans private (
val bcNewCenters = data.context.broadcast(newCenters)
val preCosts = costs
costs = data.zip(preCosts).map { case (point, cost) =>
Vectors.dense(
Array.tabulate(runs) { r =>
math.min(KMeans.pointCost(bcNewCenters.value(r), point), cost(r))
})
}.cache()
}
}.persist(StorageLevel.MEMORY_AND_DISK)
val sumCosts = costs
.aggregate(Vectors.zeros(runs))(
.aggregate(new Array[Double](runs))(
seqOp = (s, v) => {
// s += v
axpy(1.0, v, s)
var r = 0
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@mengxr mengxr Aug 30, 2015

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not calling BLAS here because runs == 1 in most cases

while (r < runs) {
s(r) += v(r)
r += 1
}
s
},
combOp = (s0, s1) => {
// s0 += s1
axpy(1.0, s1, s0)
var r = 0
while (r < runs) {
s0(r) += s1(r)
r += 1
}
s0
}
)
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