Block Algorithms and their Derivation

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Block Algorithms and their Derivation


It is comparatively straightforward to recode many of the algorithms in LINPACK and EISPACK so that they call Level 2 BLAS . Indeed, in the simplest cases the same floating-point operations are performed, possibly even in the same order: it is just a matter of reorganizing the software. To illustrate this point we derive the Cholesky factorization algorithm that is used in the LINPACK  routine SPOFA , which factorizes a symmetric positive definite matrix as . Writing these equations as:

and equating coefficients of the j-th column, we obtain:

Hence, if

has already been computed, we can compute


from the equations:

Here is the body of the code of the LINPACK routine SPOFA , which implements the above method:

         DO 30 J = 1, N
            INFO = J
            S = 0.0E0
            JM1 = J - 1
            IF (JM1 .LT. 1) GO TO 20
            DO 10 K = 1, JM1
               T = A(K,J) - SDOT(K-1,A(1,K),1,A(1,J),1)
               T = T/A(K,K)
               A(K,J) = T
               S = S + T*T
   10       CONTINUE
   20       CONTINUE
            S = A(J,J) - S
C     ......EXIT
            IF (S .LE. 0.0E0) GO TO 40
            A(J,J) = SQRT(S)
   30    CONTINUE

And here is the same computation recoded in ``LAPACK-style'' to use the Level 2 BLAS  routine STRSV (which solves a triangular system of equations). The call to STRSV has replaced the loop over K which made several calls to the Level 1 BLAS routine SDOT. (For reasons given below, this is not the actual code used in LAPACK - hence the term ``LAPACK-style''.)

      DO 10 J = 1, N
         CALL STRSV( 'Upper', 'Transpose', 'Non-unit', J-1, A, LDA,
     $               A(1,J), 1 )
         S = A(J,J) - SDOT( J-1, A(1,J), 1, A(1,J), 1 )
         IF( S.LE.ZERO ) GO TO 20
         A(J,J) = SQRT( S )

This change by itself is sufficient to make big gains in performance on a number of machines.

For example, on an IBM RISC Sys/6000-550 (using double precision) there is virtually no difference in performance between the LINPACK-style and the LAPACK-style code. Both styles run at a megaflop rate far below its peak performance for matrix-matrix multiplication. To exploit the faster speed of Level 3 BLAS , the algorithms must undergo a deeper level of restructuring, and be re-cast as a block algorithm - that is, an algorithm that operates on blocks or submatrices of the original matrix.

To derive a block form of Cholesky factorization , we write the defining equation in partitioned form thus:

Equating submatrices in the second block of columns, we obtain:

Hence, if

has already been computed, we can compute

as the solution to the equation

by a call to the Level 3 BLAS routine STRSM; and then we can compute


This involves first updating the symmetric submatrix

by a call to the Level 3 BLAS routine SSYRK, and then computing its Cholesky factorization. Since Fortran does not allow recursion, a separate routine must be called (using Level 2 BLAS rather than Level 3), named SPOTF2 in the code below. In this way successive blocks of columns of U are computed. Here is LAPACK-style code for the block algorithm. In this code-fragment NB denotes the width  of the blocks.

      DO 10 J = 1, N, NB
         JB = MIN( NB, N-J+1 )
         CALL STRSM( 'Left', 'Upper', 'Transpose', 'Non-unit', J-1, JB,
     $               ONE, A, LDA, A( 1, J ), LDA )
         CALL SSYRK( 'Upper', 'Transpose', JB, J-1, -ONE, A( 1, J ), LDA,
     $               ONE, A( J, J ), LDA )
         CALL SPOTF2( 'Upper', JB, A( J, J ), LDA, INFO )
         IF( INFO.NE.0 ) GO TO 20

But that is not the end of the story, and the code given above is not the code that is actually used in the LAPACK routine SPOTRF . We mentioned in subsection 3.1.1 that for many linear algebra computations there are several vectorizable variants, often referred to as i-, j- and k-variants, according to a convention introduced in [33] and used in [45]. The same is true of the corresponding block algorithms.

It turns out that the j-variant that was chosen for LINPACK, and used in the above examples, is not the fastest on many machines, because it is based on solving triangular systems of equations, which can be significantly slower than matrix-matrix multiplication. The variant actually used in LAPACK is the i-variant, which does rely on matrix-matrix multiplication.

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Next: Examples of Block Up: Performance of LAPACK Previous: The BLAS as

Tue Nov 29 14:03:33 EST 1994