april 19, 2010hips 20101 transforming linear algebra libraries: from abstraction to parallelism...

April 19, 2010 HIPS 2010 1

Transforming Linear Algebra Libraries: From Abstraction to

Parallelism

Ernie Chan

April 19, 2010 HIPS 2010 2

Motivation

Statically

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Outline

Inversion of a Triangular MatrixRequisite Semantic InformationStatic Generation of a Directed Acyclic

GraphPerformanceConclusion

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Inversion of a Triangular Matrix

Formal Linear Algebra Methods Environment (FLAME) High-level abstractions for expressing linear

algebra algorithms

Triangular Inversion (Trinv)

R := U-1

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LAPACK-style Implementation

DO J = 1, N, NB

JB = MIN( NB, N-J+1 )

CALL DTRSM( ‘Left’, ‘Upper’, ‘No transpose’, ‘Non-unit’,

$ JB, N-J-JB+1, -ONE, A( J, J ), LDA,

$ A( J, J+JB ), LDA )

CALL DGEMM( ‘No transpose’, ‘No transpose’,

$ J-1, N-J-JB+1, JB, ONE, A( 1, J ), LDA,

$ A( J, J+JB ), LDA, ONE, A( 1, J+JB ), LDA )

CALL DTRSM( ‘Right’, ‘Upper’, ‘No transpose’, ‘Non-unit’,

$ J-1, JB, ONE, A( J, J ), LDA,

$ A( 1, J ), LDA )

CALL DTRTI2( ‘Upper’, ‘Non-unit’,

$ JB, A( J, J ), LDA, INFO )

ENDDO

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FLASH Matrix of matrices

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FLA_Part_2x2( A, &ATL, &ATR, &ABL, &ABR, 0, 0, FLA_TL );

while ( FLA_Obj_length( ATL ) < FLA_Obj_length( A ) ) { FLA_Repart_2x2_to_3x3( ATL, /**/ ATR, &A00, /**/ &A01, &A02, /* ******** */ /* **************** */ &A10, /**/ &A11, &A12, ABL, /**/ ABR, &A20, /**/ &A21, &A22, 1, 1, FLA_BR ); /*-------------------------------------------------------*/ FLASH_Trsm( FLA_LEFT, FLA_UPPER_TRIANGULAR, FLA_NO_TRANSPOSE, FLA_NONUNIT_DIAG, FLA_MINUS_ONE, A11, A12 ); FLASH_Gemm( FLA_NO_TRANSPOSE, FLA_NO_TRANSPOSE, FLA_ONE, A01, A12, FLA_ONE, A02 ); FLASH_Trsm( FLA_RIGHT, FLA_UPPER_TRIANGULAR, FLA_NO_TRANSPOSE, FLA_NONUNIT_DIAG, FLA_ONE, A11, A01 ); FLASH_Trinv( FLA_UPPER_TRIANGULAR, FLA_NONUNIT_DIAG, A11 ); /*-------------------------------------------------------*/ FLA_Cont_with_3x3_to_2x2( &ATL, /**/ &ATR, A00, A01, /**/ A02, A10, A11, /**/ A12, /* ********** */ /* ************* */ &ABL, /**/ &ABR, A20, A21, /**/ A22, FLA_TL );}

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Extensible Markup Language (XML)<?xml version="1.0" encoding="ISO-8859-1"?>

<Function name="FLA_Trinv" type="blk" variant="3">

<Option type="uplo">FLA_UPPER_TRIANGULAR</Option>

<Declaration>

<Operand type="matrix" direction="TL->BR" inout="both">A</Operand>

</Declaration>

<Loop>

<Guard>A</Guard>

<Update>

<Statement name="FLA_Trsm">

<Option type="side">FLA_LEFT</Option>


<Option type="trans">FLA_NO_TRANSPOSE</Option>

<Option type="diag">FLA_NONUNIT_DIAG</Option>

<Parameter>FLA_MINUS_ONE</Parameter>

<Parameter partition="11">A<Parameter>

<Parameter partition="12">A<Parameter>

<Statement name="FLA_Gemm">



<Parameter>FLA_ONE<Parameter>

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Extensible Markup Language (XML) Cont. <Parameter partition="01">A</Parameter>

<Parameter partition="12">A</Parameter>

<Parameter>FLA_ONE</Parameter>


</Statement>

<Statement name="FLA_Trsm">

<Option type="side">FLA_RIGHT</Option>




<Parameter>FLA_ONE</Parameter>



</Statement>

<Statement name="FLA_Trinv">




</Statement>

</Update>

</Loop>

</Function>

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Outline



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Requisite Semantic Information

Partitioning Scheme<?xml version="1.0" encoding="ISO-8859-1"?>



<Declaration>


</Declaration>

<Loop>

<Guard>A</Guard> 

<Update>

<Statement name="FLA_Trsm“>



</Statement>

<Statement name="FLA_Gemm“>



</Statement>




</Statement>

<Statement name="FLA_Trinv“>

<!–- ‘Upper’, ‘Non-unit’, A11 -->

</Statement>

</Update>

</Loop>

</Function>

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Problem Size*<?xml version="1.0" encoding="ISO-8859-1"?>



<Declaration>


</Declaration>

<Loop>


<Update>



</Statement>



</Statement>



</Statement>


<!–- ‘Upper’, ‘Non-unit’, A11 -->

</Statement>

</Update>

</Loop>

</Function>

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Updates<?xml version="1.0" encoding="ISO-8859-1"?>



<Declaration>


</Declaration>

<Loop>


<Update>



</Statement>



</Statement>



</Statement>


<!–- ‘Upper’, ‘Non-unit’, A11 -->

</Statement>

</Update>

</Loop>

</Function>

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Input and Output Parameters<?xml version="1.0" encoding="ISO-8859-1"?>

<Function name="FLA_Trsm">

<Declaration>

<Operand type=“scalar“ inout=“in">alpha</Operand>

<Operand type="matrix“ inout=“in">A</Operand>

<Operand type="matrix“ inout=“both“>B</Operand>

</Declaration>

</Function>

<Function name="FLA_Gemm">

<Declaration>

<Operand type=“scalar“ inout=“in">alpha</Operand>

<Operand type="matrix“ inout=“in">A</Operand>

<Operand type="matrix“ inout=“in">B</Operand>

<Operand type=“scalar“ inout=“in">beta</Operand>

<Operand type="matrix“ inout="both">C</Operand>

</Declaration>

</Function>

<Function name="FLA_Trinv">

<Declaration>

<Operand type="matrix“ inout="both">A</Operand>

</Declaration>

</Function>

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Outline



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Static Generation of a DAG

Code Generation Convert XML representation to FLASH code

generation intermediary Annotated with input and output information

Create directed acyclic graph (DAG) by statically unrolling the loop

Operations on submatrix blocks (tasks) are vertices Data dependencies between tasks are edges

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Data Dependencies Flow (read-after-write)

S1: A = B + C;

S2: D = A + E; Anti (write-after-read)

S3: F = A + G;

S4: A = H + I; Output (write-after-write)

S5: A = J + K;

S6: A = L + M;

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Problem Size Problem size cannot be determined a priori Fix the block size or loop unrolling factor

Balance between instruction footprint and data granularity of tasks

Example Trinv on 3x3 matrix of blocks

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Trinv Iteration 1

Trinv2 Trsm0 Trsm1

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Trinv Iteration 2

Trsm5 Gemm4

Trinv6 Trsm3

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Trinv Iteration 3

Trsm7

Trsm8

Trinv9

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Trsm1

Trinv2

Trsm0

Gemm4

Trsm5

Trinv9

Trsm3

Trsm7 Trsm8

Trinv6

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Outline



April 19, 2010 HIPS 2010 26

Performance

LabVIEW Graphical, data flow programming language (G)

Anti-dependencies cannot exist in G• Copies are made when wire is split

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Performance

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Performance

Target Architecture 16-core AMD processor

4 socket quad-core Opteron 1.9 GHz 4 GB of RAM per socket

LabVIEW 8.6 Windows XP

Basic Linear Algebra Subprograms (BLAS) MKL 7.2

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Performance

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Performance

Results Parallelism

Exploit parallelism inherent within DAG Hierarchical matrix storage

Spatial locality Overhead

Copy matrix from flat row-major storage to hierarchical matrix and back

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Performance

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Outline



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Conclusion

Instantiate linear algebra algorithm using a code generation intermediary

Statically produce a directed acyclic graph by fixing block size or loop unrolling factor

XML → FLASH → DAG

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Acknowledgments

Jim Nagle, Robert van de Geijn We thank the other members of FLAME team

for their support

Funding National Instruments NSF Grants

CCF—0540926 CCF—0702714

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Conclusion

More Information

http://www.cs.utexas.edu/~flame

Questions?

[email protected]

april 19, 2010hips 20101 transforming linear algebra libraries: from abstraction to parallelism...

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