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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-3418] Sparse Matrix support (CCS) and a...
Date Thu, 18 Sep 2014 01:21:21 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/2294#discussion_r17704452
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala ---
    @@ -197,4 +201,368 @@ private[mllib] object BLAS extends Serializable {
             throw new IllegalArgumentException(s"scal doesn't support vector type ${x.getClass}.")
         }
       }
    +
    +  // For level-3 routines, we use the native BLAS.
    +  private def nativeBLAS: NetlibBLAS = {
    +    if (_nativeBLAS == null) {
    +      _nativeBLAS = NativeBLAS
    +    }
    +    _nativeBLAS
    +  }
    +
    +  /**
    +   * C := alpha * A * B + beta * C
    +   * @param transA specify whether to use matrix A, or the transpose of matrix A. Should
be "N" or
    +   *               "n" to use A, and "T" or "t" to use the transpose of A.
    +   * @param transB specify whether to use matrix B, or the transpose of matrix B. Should
be "N" or
    +   *               "n" to use B, and "T" or "t" to use the transpose of B.
    +   * @param alpha a scalar to scale the multiplication A * B.
    +   * @param A the matrix A that will be left multiplied to B. Size of m x k.
    +   * @param B the matrix B that will be left multiplied by A. Size of k x n.
    +   * @param beta a scalar that can be used to scale matrix C.
    +   * @param C the resulting matrix C. Size of m x n.
    +   */
    +  def gemm(
    +      transA: Boolean,
    +      transB: Boolean,
    +      alpha: Double,
    +      A: Matrix,
    +      B: DenseMatrix,
    +      beta: Double,
    +      C: DenseMatrix): Unit = {
    +    if (alpha == 0.0) {
    +      logWarning("gemm: alpha is equal to 0. Returning C.")
    +    } else {
    +      A match {
    +        case sparse: SparseMatrix =>
    +          gemm(transA, transB, alpha, sparse, B, beta, C)
    +        case dense: DenseMatrix =>
    +          gemm(transA, transB, alpha, dense, B, beta, C)
    +        case _ =>
    +          throw new IllegalArgumentException(s"gemm doesn't support matrix type ${A.getClass}.")
    +      }
    +    }
    +  }
    +
    +  /**
    +   * C := alpha * A * B + beta * C
    +   *
    +   * @param alpha a scalar to scale the multiplication A * B.
    +   * @param A the matrix A that will be left multiplied to B. Size of m x k.
    +   * @param B the matrix B that will be left multiplied by A. Size of k x n.
    +   * @param beta a scalar that can be used to scale matrix C.
    +   * @param C the resulting matrix C. Size of m x n.
    +   */
    +  def gemm(
    +      alpha: Double,
    +      A: Matrix,
    +      B: DenseMatrix,
    +      beta: Double,
    +      C: DenseMatrix): Unit = {
    +    gemm(false, false, alpha, A, B, beta, C)
    +  }
    +
    +  /**
    +   * C := alpha * A * B + beta * C
    +   * For `DenseMatrix` A.
    +   */
    +  private def gemm(
    +      transA: Boolean,
    +      transB: Boolean,
    +      alpha: Double,
    +      A: DenseMatrix,
    +      B: DenseMatrix,
    +      beta: Double,
    +      C: DenseMatrix): Unit = {
    +    val mA: Int = if (!transA) A.numRows else A.numCols
    +    val nB: Int = if (!transB) B.numCols else B.numRows
    +    val kA: Int = if (!transA) A.numCols else A.numRows
    +    val kB: Int = if (!transB) B.numRows else B.numCols
    +    val tAstr = if (!transA) "N" else "T"
    +    val tBstr = if (!transB) "N" else "T"
    +
    +    require(kA == kB, s"The columns of A don't match the rows of B. A: $kA, B: $kB")
    +    require(mA == C.numRows, s"The rows of C don't match the rows of A. C: ${C.numRows},
A: $mA")
    +    require(nB == C.numCols,
    +      s"The columns of C don't match the columns of B. C: ${C.numCols}, A: $nB")
    +
    +    nativeBLAS.dgemm(tAstr, tBstr, mA, nB, kA, alpha, A.values, A.numRows, B.values,
B.numRows,
    +      beta, C.values, C.numRows)
    +  }
    +
    +  /**
    +   * C := alpha * A * B + beta * C
    +   * For `SparseMatrix` A.
    +   */
    +  private def gemm(
    +      transA: Boolean,
    +      transB: Boolean,
    +      alpha: Double,
    +      A: SparseMatrix,
    +      B: DenseMatrix,
    +      beta: Double,
    +      C: DenseMatrix): Unit = {
    +    val mA: Int = if (!transA) A.numRows else A.numCols
    +    val nB: Int = if (!transB) B.numCols else B.numRows
    +    val kA: Int = if (!transA) A.numCols else A.numRows
    +    val kB: Int = if (!transB) B.numRows else B.numCols
    +
    +    require(kA == kB, s"The columns of A don't match the rows of B. A: $kA, B: $kB")
    +    require(mA == C.numRows, s"The rows of C don't match the rows of A. C: ${C.numRows},
A: $mA")
    +    require(nB == C.numCols,
    +      s"The columns of C don't match the columns of B. C: ${C.numCols}, A: $nB")
    +
    +    val Avals = A.values
    +    val Arows = if (!transA) A.rowIndices else A.colPtrs
    +    val Acols = if (!transA) A.colPtrs else A.rowIndices
    +
    +    // Slicing is easy in this case. This is the optimal multiplication setting for sparse
matrices
    +    if (transA){
    +      var colCounterForB = 0
    +      if (!transB){ // Expensive to put the check inside the loop
    +        while (colCounterForB < nB) {
    +          var rowCounterForA = 0
    +          val Cstart = colCounterForB * mA
    +          val Bstart = colCounterForB * kA
    +          while (rowCounterForA < mA) {
    +            var i = Arows(rowCounterForA)
    +            val indEnd = Arows(rowCounterForA + 1)
    +            var sum = 0.0
    +            while (i < indEnd) {
    +              sum += Avals(i) * B.values(Bstart + Acols(i))
    +              i += 1
    +            }
    +            val Cindex = Cstart + rowCounterForA
    +            C.values(Cindex) = beta * C.values(Cindex) + sum * alpha
    +            rowCounterForA += 1
    +          }
    +          colCounterForB += 1
    +        }
    +      } else {
    +        while (colCounterForB < nB) {
    +          var rowCounter = 0
    +          val Cstart = colCounterForB * mA
    +          while (rowCounter < mA) {
    +            var i = Arows(rowCounter)
    +            val indEnd = Arows(rowCounter + 1)
    +            var sum = 0.0
    +            while (i < indEnd) {
    +              sum += Avals(i) * B(colCounterForB, Acols(i))
    +              i += 1
    +            }
    +            val Cindex = Cstart + rowCounter
    +            C.values(Cindex) = beta * C.values(Cindex) + sum * alpha
    +            rowCounter += 1
    +          }
    +          colCounterForB += 1
    +        }
    +      }
    +    } else {
    +      // Scale matrix first if `beta` is not equal to 0.0
    +      if (beta != 0.0){
    +        nativeBLAS.dscal(C.values.length, beta, C.values, 1)
    +      }
    +      // Perform matrix multiplication and add to C. The rows of A are multiplied by
the columns of
    +      // B, and added to C.
    +      var colCounterForB = 0 // the column to be updated in C
    +      if (!transB) { // Expensive to put the check inside the loop
    +        while (colCounterForB < nB) {
    +          var colCounterForA = 0 // The column of A to multiply with the row of B
    +          while (colCounterForA < kA){
    +            var i = Acols(colCounterForA)
    +            val indEnd = Acols(colCounterForA + 1)
    +            val Bval = B(colCounterForA, colCounterForB)
    +            val Cstart = colCounterForB * mA
    --- End diff --
    
    move this to the outer loop


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