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https://issues.apache.org/jira/browse/MAHOUT-1885?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15824869#comment-15824869
]
ASF GitHub Bot commented on MAHOUT-1885:
----------------------------------------
Github user andrewpalumbo commented on a diff in the pull request:
https://github.com/apache/mahout/pull/261#discussion_r96336350
--- Diff:
viennacl-omp/src/main/scala/org/apache/mahout/viennacl/openmp/OMPMMul.scala ---
@@ -0,0 +1,448 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mahout.viennacl.openmp
+
+import org.apache.mahout.logging._
+import org.apache.mahout.math
+import org.apache.mahout.math._
+import org.apache.mahout.math.flavor.{BackEnum, TraversingStructureEnum}
+import org.apache.mahout.math.function.Functions
+import org.apache.mahout.math.scalabindings.RLikeOps._
+import org.apache.mahout.math.scalabindings._
+import org.apache.mahout.viennacl.openmp.javacpp.Functions._
+import org.apache.mahout.viennacl.openmp.javacpp.LinalgFunctions._
+import org.apache.mahout.viennacl.openmp.javacpp.{CompressedMatrix,
Context, DenseRowMatrix}
+
+import scala.collection.JavaConversions._
+
+object OMPMMul extends MMBinaryFunc {
+
+ private final implicit val log = getLog(OMPMMul.getClass)
+
+ override def apply(a: Matrix, b: Matrix, r: Option[Matrix]): Matrix = {
+
+ require(a.ncol == b.nrow, "Incompatible matrix sizes in matrix
multiplication.")
+
+ val (af, bf) = (a.getFlavor, b.getFlavor)
+ val backs = (af.getBacking, bf.getBacking)
+ val sd = (af.getStructure, math.scalabindings.densityAnalysis(a),
bf.getStructure, densityAnalysis(b))
+
+
+ try {
+
+ val alg: MMulAlg = backs match {
+
+ // Both operands are jvm memory backs.
+ case (BackEnum.JVMMEM, BackEnum.JVMMEM) ⇒
+
+ sd match {
+
+ // Multiplication cases by a diagonal matrix.
+ case (TraversingStructureEnum.VECTORBACKED, _,
TraversingStructureEnum.COLWISE, _)
+ if a.isInstanceOf[DiagonalMatrix] ⇒ jvmDiagCW
+ case (TraversingStructureEnum.VECTORBACKED, _,
TraversingStructureEnum.SPARSECOLWISE, _)
+ if a.isInstanceOf[DiagonalMatrix] ⇒ jvmDiagCW
+ case (TraversingStructureEnum.VECTORBACKED, _,
TraversingStructureEnum.ROWWISE, _)
+ if a.isInstanceOf[DiagonalMatrix] ⇒ jvmDiagRW
+ case (TraversingStructureEnum.VECTORBACKED, _,
TraversingStructureEnum.SPARSEROWWISE, _)
+ if a.isInstanceOf[DiagonalMatrix] ⇒ jvmDiagRW
+
+ case (TraversingStructureEnum.COLWISE, _,
TraversingStructureEnum.VECTORBACKED, _)
+ if b.isInstanceOf[DiagonalMatrix] ⇒ jvmCWDiag
+ case (TraversingStructureEnum.SPARSECOLWISE, _,
TraversingStructureEnum.VECTORBACKED, _)
+ if b.isInstanceOf[DiagonalMatrix] ⇒ jvmCWDiag
+ case (TraversingStructureEnum.ROWWISE, _,
TraversingStructureEnum.VECTORBACKED, _)
+ if b.isInstanceOf[DiagonalMatrix] ⇒ jvmRWDiag
+ case (TraversingStructureEnum.SPARSEROWWISE, _,
TraversingStructureEnum.VECTORBACKED, _)
+ if b.isInstanceOf[DiagonalMatrix] ⇒ jvmRWDiag
+
+ // Dense-dense cases
+ case (TraversingStructureEnum.ROWWISE, true,
TraversingStructureEnum.COLWISE, true) if a eq b.t ⇒ ompDRWAAt
+ case (TraversingStructureEnum.ROWWISE, true,
TraversingStructureEnum.COLWISE, true) if a.t eq b ⇒ ompDRWAAt
+ case (TraversingStructureEnum.ROWWISE, true,
TraversingStructureEnum.COLWISE, true) ⇒ ompRWCW
+ case (TraversingStructureEnum.ROWWISE, true,
TraversingStructureEnum.ROWWISE, true) ⇒ jvmRWRW
+ case (TraversingStructureEnum.COLWISE, true,
TraversingStructureEnum.COLWISE, true) ⇒ jvmCWCW
+ case (TraversingStructureEnum.COLWISE, true,
TraversingStructureEnum.ROWWISE, true) if a eq b.t ⇒ jvmDCWAAt
+ case (TraversingStructureEnum.COLWISE, true,
TraversingStructureEnum.ROWWISE, true) if a.t eq b ⇒ jvmDCWAAt
+ case (TraversingStructureEnum.COLWISE, true,
TraversingStructureEnum.ROWWISE, true) ⇒ jvmCWRW
+
+ // Sparse row matrix x sparse row matrix (array of vectors)
+ case (TraversingStructureEnum.ROWWISE, false,
TraversingStructureEnum.ROWWISE, false) ⇒ ompSparseRWRW
+ case (TraversingStructureEnum.ROWWISE, false,
TraversingStructureEnum.COLWISE, false) ⇒ jvmSparseRWCW
+ case (TraversingStructureEnum.COLWISE, false,
TraversingStructureEnum.ROWWISE, false) ⇒ jvmSparseCWRW
+ case (TraversingStructureEnum.COLWISE, false,
TraversingStructureEnum.COLWISE, false) ⇒ jvmSparseCWCW
+
+ // Sparse matrix x sparse matrix (hashtable of vectors)
+ case (TraversingStructureEnum.SPARSEROWWISE, false,
TraversingStructureEnum.SPARSEROWWISE, false) ⇒
+ ompSparseRowRWRW
+ case (TraversingStructureEnum.SPARSEROWWISE, false,
TraversingStructureEnum.SPARSECOLWISE, false) ⇒
+ jvmSparseRowRWCW
+ case (TraversingStructureEnum.SPARSECOLWISE, false,
TraversingStructureEnum.SPARSEROWWISE, false) ⇒
+ jvmSparseRowCWRW
+ case (TraversingStructureEnum.SPARSECOLWISE, false,
TraversingStructureEnum.SPARSECOLWISE, false) ⇒
+ jvmSparseRowCWCW
+
+ // Sparse matrix x non-like
+ case (TraversingStructureEnum.SPARSEROWWISE, false,
TraversingStructureEnum.ROWWISE, _) ⇒ ompSparseRowRWRW
+ case (TraversingStructureEnum.SPARSEROWWISE, false,
TraversingStructureEnum.COLWISE, _) ⇒ jvmSparseRowRWCW
+ case (TraversingStructureEnum.SPARSECOLWISE, false,
TraversingStructureEnum.ROWWISE, _) ⇒ jvmSparseRowCWRW
+ case (TraversingStructureEnum.SPARSECOLWISE, false,
TraversingStructureEnum.COLWISE, _) ⇒ jvmSparseCWCW
+ case (TraversingStructureEnum.ROWWISE, _,
TraversingStructureEnum.SPARSEROWWISE, false) ⇒ ompSparseRWRW
+ case (TraversingStructureEnum.ROWWISE, _,
TraversingStructureEnum.SPARSECOLWISE, false) ⇒ jvmSparseRWCW
+ case (TraversingStructureEnum.COLWISE, _,
TraversingStructureEnum.SPARSEROWWISE, false) ⇒ jvmSparseCWRW
+ case (TraversingStructureEnum.COLWISE, _,
TraversingStructureEnum.SPARSECOLWISE, false) ⇒ jvmSparseRowCWCW
+
+ // Everything else including at least one sparse LHS or RHS
argument
+ case (TraversingStructureEnum.ROWWISE, false,
TraversingStructureEnum.ROWWISE, _) ⇒ ompSparseRWRW
+ case (TraversingStructureEnum.ROWWISE, false,
TraversingStructureEnum.COLWISE, _) ⇒ jvmSparseRWCW
+ case (TraversingStructureEnum.COLWISE, false,
TraversingStructureEnum.ROWWISE, _) ⇒ jvmSparseCWRW
+ case (TraversingStructureEnum.COLWISE, false,
TraversingStructureEnum.COLWISE, _) ⇒ jvmSparseCWCW2flips
+
+ // Sparse methods are only effective if the first argument is
sparse, so we need to do a swap.
+ case (_, _, _, false) ⇒ (a, b, r) ⇒ apply(b.t, a.t, r.map {
+ _.t
+ }).t
+
+ // Default jvm-jvm case.
+ // for some reason a SrarseRowMatrix DRM %*% SrarseRowMatrix
DRM was dumping off to here
+ case _ ⇒ ompRWCW
+ }
+ }
+
+ alg(a, b, r)
+ } catch {
+ // TODO FASTHACK: just revert to JVM if there is an exception..
+ // eg. java.lang.nullPointerException if more openCL contexts
+ // have been created than number of GPU cards.
+ // better option wuold be to fall back to OpenCl First.
+ case ex: Exception =>
+ println(ex.getMessage + "falling back to JVM MMUL")
+ return MMul(a, b, r)
+ }
+ }
+
+ type MMulAlg = MMBinaryFunc
+
+ @inline
+ private def ompRWCW(a: Matrix, b: Matrix, r: Option[Matrix] = None):
Matrix = {
+ println("ompRWCW")
+ //
+ // require(r.forall(mxR ⇒ mxR.nrow == a.nrow && mxR.ncol == b.ncol))
+ // val (m, n) = (a.nrow, b.ncol)
+ //
+ // val mxR = r.getOrElse(if (densityAnalysis(a)) a.like(m, n) else
b.like(m, n))
+ //
+ // for (row ← 0 until mxR.nrow; col ← 0 until mxR.ncol) {
+ // // this vector-vector should be sort of optimized, right?
+ // mxR(row, col) = a(row, ::) dot b(::, col)
+ // }
+ // mxR
+
+ val hasElementsA = a.zSum() > 0.0
+ val hasElementsB = b.zSum() > 0.0
+
+ // A has a sparse matrix structure of unknown size. We do not want to
+ // simply convert it to a Dense Matrix which may result in an OOM
error.
+
+ // If it is empty use JVM MMul, since we can not convert it to a VCL
CSR Matrix.
+ if (!hasElementsA) {
+ println("Matrix a has zero elements can not convert to CSR")
+ return MMul(a, b, r)
+ }
+
+ // CSR matrices are efficient up to 50% non-zero
+ if(b.getFlavor.isDense) {
--- End diff --
Thanks for your review, @sscdotopen - I've made all changes, though some
have not shown up because on this PR for some reason.
> Inital Implementation of VCL Bindings
> -------------------------------------
>
> Key: MAHOUT-1885
> URL: https://issues.apache.org/jira/browse/MAHOUT-1885
> Project: Mahout
> Issue Type: Improvement
> Components: Math
> Affects Versions: 0.12.2
> Reporter: Andrew Palumbo
> Assignee: Andrew Palumbo
> Fix For: 0.13.0
>
>
> Push a working experimental branch of VCL bindings into master. There is
> still a lot of work to be done. All tests are passing, At the moment there
> am opening this JIRA mostly to get a number for PR and to test profiles
> against on travis.
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