Table of Contents

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Introduction

This page deals with vectorization and optimization of Radioss OpenRadioss Fortran code. This is a fundamental aspect of the code that needs to be well understood and learned by new Radioss programmers. Breaking performance of current code is not allowed. Furthermore, new OpenRadioss contributors. New functionality should be developed taking into account the same level of care regarding performance.

Vectorization deals with the execution of computational loops. It allows a computer to compute several loop indexes during the same cycle leveraging vector registers. This concept was first introduced on vector supercomputers (CRAY, NEC, FUJITSU…)

Nonetheless, though there are no more vector supercomputers, vectorization is reintroduced on modern CPUs like Xeon processor with AVX and AVX512

For instance, AVX512 first introduced into Intel Xeon Phi Knights Landing and Xeon Skylake allows the handling of 8x 64-bit double precision real at the same time or 16x single precision

Vectorization

DO I=1,NEL A(I,:DIM)=B(I,:DIM) + C(I,:DIM) END DO

Or for this simple case:

Expand

Vector Length

Most computations in Radioss, like element or contact forces, are performed by packets of MVSIZ. This parameter is adjustable to match so-called vector length. This parameter is also important for cache locality. It is optimized by parallel development team according to hardware characteristics

New treatments need to respect this programming model which is to split the loop over number of elements or nodes by packets of MVSIZ. This will ensure optimal vector length and cache size as well as minimal local storage (local arrays of size MVSIZ instead of number of elements or nodes)

Loop Control

IF/THEN/ELSE

It is recommended to minimize the use of IF/THEN/ELSE instruction inside computational loop

Every time a test does not depend on loop index value, it is asked to perform it outside of such loop

GOTO

GOTO is absolutely forbidden inside computational loops as it inhibits vectorization and optimization

EXIT/CYCLE

EXIT and CYCLE need to be minimized and avoided in computational loops

Loop Size

Inside a loop it is recommended to keep the number of instructions reasonable. 20 instructions or less is good. Very long loops should be split to keep cache efficiencyMost compilers will be able to fuse short loops, while they will probably fail to vectorize long complex loops

Calling a procedure inside a loop inhibits vectorization

Data Dependency

The loop below is not vectorized due to possible dependence (same value of INDEX(I) for different I):

Code Block

language	fortran

DO I = 1, N
         K = INDEX(I)
         A(K) = B(K)
         B(K) = 2*A(K)
END DO

In case of no true dependence, vectorization needs to be forced by adding a compiler directive

To keep portability across different platforms and compilers, an architecture specific include file exists named vectorize.inc that manages vectorization directives. The programmer just needs to add this include file just before the DO loop:

Code Block

language	fortran

#include "vectorize.inc"
       DO I = 1, N
            K = INDEX(I)
            A(K) = B(K)
            B(K) = 2*A(K)
       END DO

Notice there is another include file named simd.inc which makes unconditional vectorization, even if a true dependence is detected by the compiler. It is recommended to only use vectorise.inc which is more conservative regarding correctness

For Intel compiler:

vectorize.inc corresponds to !DIR$ IVDEP
simd.inc corresponds to !DIR$ SIMD

Procedure CALL

Calling a procedure inside a loop inhibits vectorization therefore it is not authorized

Inside Radioss, there are vectorized versions of procedures, basically the loop is put inside the procedure rather than outside:

VALPVEC to replace JACOBIEW
VINTER to replace FINTER

Nested Loops

In practice, only the inner most loop will be vectorized. So the inner most loop needs to be the largest one.

For fixed size loops it is possible to unroll them by hand or to use Fortran90 enhancement. Then the compiler is able to vectorize the outer loop

Note

Unroll by hand is not enough on AVX512 or SSE architectures

Only Fortran 90 syntax helps in this case

Example Nested Loop with `NEL >> DIM`

Code Block

language	fortran

DO I = 1, NEL
    DO J=1, DIM
      A(I,J) = B(I,J) + C(I,J)
    END DO
END DO

To be transformed to:

Code Block

language	fortran

DO J=1, DIM
    DO I = 1, NEL
     A(I,J) = B(I,J) + C(I,J)
    END DO
END DO

Or using Fortran90 notation:

Code Block

language	fortran

Code Block

language	fortran

A(:NEL,:DIM)=B(:NEL,:DIM) + C(:NEL,:DIM)

Arithmetic Functions

Expand

Power

Never use real variable for integer power because of the cost of real power arithmetic. Take care to not use real variable defined in constant.inc when integer is enough

`A**2`	Allowed
`A**DEUXTWO`	Forbidden as DEUX `TWO` is a `my_real` variable defined in constant.inc
`A**DTIERSTHIRD`	here there is no other choice as a real power arithmetic is required

Div

For invariant, it is advised to multiply by invert instead of doing a division by a constant inside a loop

Arrays

Expand

Fortran90 Array Operations

Use of Fortran90 array operations is encouraged as long as code readability is kept , by always specifying array bounds to avoid confusion between variable and array arithmetic.

Example:

Code Block

language	fortran

INTEGER, DIMENSION(NUMNOD) :: A, B, C
A = B + C

! confusion between variable and array operation

Code Block

language	fortran

                               ! confusion between variable and array operation  
A(:NUMNOD) = B(:NUMNOD) + C(:NUMNOD)

! default lower bound:1

    ! default lower bound:1

Multidimensional Arrays

Data Locality

Large arrays over a number of nodes or elements are defined to maximize data locality and have therefore the smallest dimension first, like in the example below:

Code Block

language	fortran

Rule of thumb for data locality of 2D arrays:

If the large dimension is >= MVSIZ or 128 : it should be last (X(3,NUMNOD)

, V(3,NUMNOD

)

, A(3,NUMNOD)

Leading Dimension for Vectorization

For vectorization on Xeon, it is better to have leading dimension first. So, depending on array size and access pattern a compromise needs to be found:

For large arrays like X, V, A, it is better to keep locality

For data structure of few times MVSIZ, like new element buffer arrays or temporary arrays of size x times MVSIZ, having largest dimension first is better:

HOUR

If the large dimension is <= MVSIZ or 128: it should be first (C(MVSIZ,5)

better than HOUR(5,MVSIZ

)

According to test with recent Intel compiler, Fortran90 array notation can also improve code generated:

A(I,1:3) = B(I,1:3) + C(I,1:3) no need to unroll loop

Structure Of Arrays

Code Block

language	fortran

TYPE GRID_STRUCT_ my_real :: x(100),y(100),z

Use structure of arrays (left example (POINT%X(1:NBPOINTS) ) rather than arrays of structure (right example)

Correct

Incorrect

(

100) END TYPE GRID_STRUCT_

Code Block

language	fortran

TYPE POINT_STRUCT_
  my_real ::  x, y, z
END TYPE POINT_STRUCT_

Code Block

language	fortran

TYPE(GRID_STRUCT_) MESH;
! a grid of 3 arrays x, y, z of size 100

Code Block

language	fortran

TYPE(

POINT

_STRUCT_) P(100); ! an array of size 100 of 3 scalars x, y, z

Warning On Large Array Initialization

Especially inside Radioss Starter it is common to find code using large array flag over NUMNOD initialized to zero many times which is time consuming

Here is a method to avoid such an issue: left is original poorly performing code, right is optimized version

Original

(

Poor Performance)

Optimized Code

Code Block

language	fortran

DO I=1, IX_TIMES
!IX_TIMES set to 0 
         TAG(1:NUMNOD)=0
         DO J=1,FEW_ITEMS
           N = INDEX(I,J)
           IF(TAG(N) == 0)THEN
! but few values change
             TAG(N)=1
! additional treatments…  
           END IF   
         END DO
       END DO

Code Block

language	fortran

! single init to 0
TAG(1:NUMNOD)=0
DO I=1, IX_TIMES
  DO J=1,FEW_ITEMS
    N = INDEX(I,J)
    IF(TAG(N) == 0)THEN
      TAG(N)=1
! additional treatments…
    END IF
  END DO
! set to 0 only if needed
  DO J=1 , FEW_ITEMS
    N = INDEX(I,J)
    TAG(N) = 0
  END DO
END DO

Additional Fortran90 Restrictions Regarding Efficiency

Operator Overloading

Usage of object oriented feature like operator overloading, while it is efficient in code writing and clarity is not recommended due to lack of performance efficiency1:NBPOINTS)%X)

Object Oriented Programming

It is not recommended to use object-oriented features unless you can verify that it does not harm performance

\uD83D\uDCCB Related articles

OpenRadioss HMPP Development InsightsOpenRadioss Coding Standards

OpenRadioss Coding Recommendations

OpenRadioss Performance Aspects: Parallelism

OpenRadioss Coding: Additional Considerations

OpenRadioss Reader (Radioss Block Format)

OpenRadioss HMPP Development Insights

Versions Compared

Old Version 16

New Version Current

Key

Introduction

Vectorization

Vector Length

Loop Control

Data Dependency

Procedure CALL

Nested Loops

Example Nested Loop with `NEL >> DIM`

Arithmetic Functions

Power

Arrays

Fortran90 Array Operations

Multidimensional Arrays

Data Locality

Leading Dimension for Vectorization

Structure Of Arrays

Warning On Large Array Initialization

Additional Fortran90 Restrictions Regarding Efficiency

Operator Overloading

Object Oriented Programming

\uD83D\uDCCB Related articles

Page Comparison

Versions Compared

Old Version 16

New Version Current

Key

Introduction

Vectorization

Vector Length

Loop Control

Data Dependency

Procedure CALL

Nested Loops

Example Nested Loop with NEL >> DIM

Arithmetic Functions

Power

Arrays

Fortran90 Array Operations

Multidimensional Arrays

Data Locality

Leading Dimension for Vectorization

Structure Of Arrays

Warning On Large Array Initialization

Additional Fortran90 Restrictions Regarding Efficiency

Operator Overloading

Object Oriented Programming

\uD83D\uDCCB Related articles

Example Nested Loop with `NEL >> DIM`