Transcript Slide 1

Linear Algebra on GPUs
Jens Krüger
Technische Universität München
Why LA on GPUs?
1.
Why should we care
about Linear Algebra
at all?
 Use LA to solve PDEs
 solving PDEs can increase realism for
VR, Education, Simulations, Games, …
Why Linear Algebra on GPUs?
2.
… and why do it on the GPU?
a)
The GPU is a fast streaming processor
•
b)
LA operations are easily “streamable”
The result of the computation is already on the GPU and
ready for display
Getting started …
Visual simulation
Education and Training
Basis linear
algebra operators
High
bandwidth
Computer
graphics
applications
Visual computing
GPU as workhorse
for numerical computations
Programmable GPUs
General linear
algebra package
Parallel
computing
Representation
Vector representation
– 2D textures best we can do
• Per-fragment vs. per-vertex operations
• High texture memory bandwidth
• Read-write access, dependent fetches
1
N
1
N
Representation (cont.)
Dense Matrix representation
– treat a dense matrix as a set of column vectors
– again, store these vectors as 2D textures
i
N Vectors
Matrix
N 2D-Textures
N
... ...
N
N
1
1
i
N
i
...
...
N
Representation (cont.)
Banded Sparse Matrix representation
– treat a banded matrix as a set of diagonal vectors
i
Matrix
2 Vectors
2 2D-Textures
1
N
N
N
1
2
2
Representation (cont.)
Banded Sparse Matrix representation
– combine opposing vectors to save space
i
Matrix
2 Vectors
2 2D-Textures
1
N
N
N-i
N
1
2
2
Operations
• Vector-Vector Operations
– Reduced to 2D texture operations
– Coded in vertex/fragment shaders
return tex0 OP tex1
Operations (cont.)
• Vector-Vector Operations
– Reduce operation for scalar products
original Texture
st
1 pass
nd
2 pass
...
...
...
Reduce m x n region
in fragment shader
...
Operations (cont.)
In depth example: Vector / Banded-Matrix Multiplication
A
b
x
=
Example (cont.)
Vector / Banded-Matrix Multiplication
A
A
b
x
=
b
Example (cont.)
Compute the result in 2 Passes
Pass 1:
=
b
A
x
multiply
Example (cont.)
Compute the result in 2 Passes
Pass 2:
A
=
b
shift
b‘
x
multiply
Representation (cont.)
Random sparse matrix representation
– Textures do not work
• Splitting yields highly fragmented textures
• Difficult to find optimal partitions
– Idea: encode only non-zero entries in vertex arrays
Column as TexCoord 1-4
Matrix  Vector = Result
Matrix
Values as TexCoord 0
Vertex Array 1
Tex0
Pos
Tex1-4
Vertex Array 2
Tex0
Pos
Tex1-4
Vector
Result
=
N
Row Index as Position
N
Building a Framework
Presented so far:
• representations on the GPU for
– single float values
– vectors
– matrices
• dense
• banded
• random sparse
• operations on these representations
– add, multiply, reduce, …
– upload, download, clear, clone, …
Building a Framework
Example: CG
Encapsulate into Classes for more complex algorithms
– Example use: Conjugate Gradient Method, complete source:
void clCGSolver::solveInit() {
m_clMatrix->matrixVectorOp(CL_SUB,m_clvX,m_clvB,m_clvR);
// R = A*x-b
m_clvR->addVector(m_clvR,m_clvR,-1.0f,0.0f);
// R = -R
m_clvR->addVector(m_clvR,m_clvP,1.0f,0.0f);
// P = R
m_clvR->reduceAdd(m_clvR, clfRho);
// rho = sum(R*R);
}
void clCGSolver::solveIteration() {
m_clMatrix->matrixVectorOp(CL_NULL,m_clvP,NULL,m_clvQ);
// Q = Ap;
m_clvP->reduceAdd(m_clvQ,clfTemp);
// temp = sum(P*Q);
clfRho->divZ(clfTemp,clfAlpha);
// alpha = rho/temp;
m_clvX->addVector(m_clvP,m_clvX,1,clfAlpha);
// X = X + alpha*P
m_clvR->subtractVector(m_clvQ,m_clvR,1,clfAlpha);
// R = R - alpha*Q
m_clvR->reduceAdd(m_clvR,clfNewRho);
// newrho = sum(R*R);
clfNewRho->divZ(clfRho,clfBeta);
// beta = newrho/rho
m_clvR->addVector(m_clvP,m_clvP,1,clfBeta);
// P = R+beta*P;
clFloat *temp; temp=clfNewRho;
clfNewRho=clfRho; clfRho=temp;
// swap rho and newrho pointer
}
void clCGSolver::solve(int maxiter) {
solveInit();
for (int i = 0;i< maxiter;i++) solveIteration();
}
int clCGSolver::solve(float rhoTresh, int maxiter) {
solveInit();
clfRho->clone(clfNewRho);
for (int i = 0;i< maxiter && clfNewRho.getData() > rhoTresh;i++) solveIteration();
return i;
}
Building a Framework
Example: Jacobi
• The Jacobi method for a problem
A x = b
where
A = L + D +U
can be expressed with matrices as follows:
x
(k +1)
-1
(k )
= D  (b - ( L + U )  x )
If the Matrix is stored in the diagonal form the matrix
D-1 can be computed on the fly by inverting the
diagonal elements during the vector-vector product. So
one Jacobi step becomes one matrix-vector product,
one vector-vector product and one vector subtract.
Example 1
2D Waves (explicit)
• usually you would compute:
t +1
i, j
x
(
= x
t
i -1, j
+x
t
i , j -1
+x
t
i +1, j
+x
t
i , j +1
- 4 x
t
i, j
)
• you need to write a custom shader for this filter
• think about this as a matrix-vector operation!
-


-


-





-


-


-







-


-


-
x 1t
x1t +1
x 2t
x2t +1
x 3t
x3t +1
x 4t
x4t +1
x 5t
x 6t
x 7t
x 7t
x 9t
=
x5t +1
x6t +1
x7t +1
x8t +1
x9t +1
Example 2
2D Waves (implicit)
2D wave equation
– Finite difference discretization
– Implicit Crank-Nicholson scheme
Key Idea: Rewrite as Matrix-Vector Product
Example 2:
2D wave equation (cont.)
(
)
cit =   xit-1, j + xit, j -1 + xit+1, j + xit, j +1 - 4  xit, j + 2  xit, j - xit,-j1
t 2  c 2
where  =
2  h 2
Navier Stokes on GPUs
The Equations
The Navier-Stokes Equations for 2D:
Advection
du 1 2
=
 u - V   u + f x - p
dt Re
dv 1 2
=
 v - V  v + f y - p
dt Re
Diffusion
div (V ) = 0
Pressure
Zero Divergence
External Forces
NSE Discretization
( )
v
p
1   2 v  2 v   (uv )  v 2
=
+
+
gy

2
2 
t Re  x
y 
x
y
y
• Diffusion
  2v 
vi +1, j - 2vi , j + vi -1, j
 2  =
(dy )2
 x  i, j
  2v 
vi , j +1 - 2vi , j + vi , j -1
 2 =
(dy )2
 y  i , j
• Advection
 (uv )
1  (ui , j + ui , j +1 )(vi , j + vi +1, j ) (ui -1, j + ui -1, j +1 )(vi -1, j + vi , j )
1  ui , j + ui , j +1 (vi , j - vi +1, j ) (ui -1, j + ui -1, j +1 )(vi -1, j - vi , j )
=
+



 x 


d
d
x
2
2
2
2
x
2
2
2
2


i, j


• Pressure
 p 
pi , j +1 - pi , j
=
 
dy
 y  i, j
Navier-Stokes
Equations (cont.)
Rewrite the Navier Stokes Equations
(+ )
()
ui ,t j 1 = Fi , tj -
dt (t +1) (t +1)
(pi+1, j - pi, j )
dx
(+ )
()
vi ,t j 1 = Gi ,tj -
where
Fi , j = u i , j
Gi , j
dt (t +1) (t +1)
(p - p )
dy i , j +1 i , j
( )


  2 u     u 2 
  (uv )
1    2 u 

;
+ t
+
+
g








x
 Re   x 2 

y 2  i , j   x  i , j  y  i , j

,
i
j




( )


  2 v     (uv )
 v2 
1    2 v 

;
= v i , j + t
+
+
g






y
 Re   x 2 

y 2  i , j   x  i , j  y  i , j

,
i
j




now F and G can be computed
Navier-Stokes
Equations (cont.)
Problem: Pressure is still unknown!
(+ )
()
ui ,t j 1 = Fi , tj -
dt (t +1) (t +1)
(pi+1, j - pi, j )
dx
(+ )
()
vi ,t j 1 = Gi ,tj -
dt (t +1) (t +1)
(p - p )
dy i , j +1 i , j
From div (V ) = 0 derive:
u t +1 v t +1 G t
 2 p t +1 F t
 2 p t +1
+
=
- t
+
- t
0=
2
x
y
x
x
y
y 2
..to get this Poisson Equation:
( +1)
( +1)
( +1)
pi +n1, j - 2 pi ,nj + pi -n1, j
(dx )
2
+
( +1)
( +1)
( +1)
pi ,nj +1 - 2 pi ,nj + pi ,nj -1
(dy )2
(n )
(n )
(n )
(n )
1  Fi , j - Fi -1, j Gi , j - Gi , j -1 
=
+

dt 
dx
dy

Navier-Stokes
Equations (cont.)
The basic algorithm:
1. Compute F and G
1. add external forces
2. advect
3. diffuse
easy 
semi-lagrange [Stam 1999]
explicit
2. solve the Poisson equation use the CG solver
3. update velocities
subtract pressure gradient
Multigrid on GPUs
Multigrid “in english”
1.
do a few Jacobi/Gauss-Seidel iterations on the fine grid
–
–
Jacobi/G-S eliminate high frequencies in the error
conjugate gradient does not have this property !!!
2.
compute the residuum of the last approximaton
3.
propagate this residuum to the next coarser grid
–
4.
solve the coarser grid for the absolute error
–
5.
can be done by the means of a matrix multiplication
for the solution you can use another multigrid step
backpropagate the error to the finer grid
–
can be done by another matrix multiplication (transposed matrix from 3.)
6.
use the error to correct the first approximation
7.
do another few Jacobi/Gauss-Seidel iterations to remove noise
introduced by the propagation steps
Multigrid “in greek”
A x =b
A h x h + e h = b h
h
h
(
h
)
•
Consider this problem: x is the solution vector of a set
of linear equations
•
this equation holds for current approximation x’ with
the error e
h •
- A  x
A  e = b142
43
h
h
h
h
rearranging leads to the residual equation with
residuum r
rh
I
2h
h
 A  e{ = I
h
2h
h
h
2h
h
I 2 h e
(1I 42
 A  I ) e
4 43
4
2h
h
h
h
2h
2h
= r 2h
A2 h
2h
2h

=
A e
r
2h
r
h
•
now multiply both sides with a non quadratic
interpolation matrix and replace the error with an error
times the transposed interpolation matrix
•
Let A2h be the product of the Interpolation Matrix, A
and the transposed Interpolation matrix.
•
Finally we end up with a new set of linear equations
with only half the size in every dimension of the old
one. We solve this set for the error at this grid level.
Propagating the error the above steps we can derive
the error for the large system and use it to correct out
approximation
Multigrid (cont.)
• „fine grid“, „coarse grid“ only makes sense if the
problem to solve corresponds to a grid 
– this is the case in the finite difference methods described
before 
– need to find an “interpolation matrix” for the propagation step
to generate the coarser grid (for instance simple linear
interpolation)
– need an “extrapolation matrix” to move from the coarse to the
fine grid
• the coarse grid matrices can be pre-computed
Multigrid on GPUs
Observation:
• you only need matrix-vector operations and a
“Jacobi smoother” to do multigrid
• to put it on the GPU simply use the matrix-vector
operations from the framework
Improvement:
• to do the interpolation an extrapolation steps we can
use the fast bilinear interpolation hardware of the
GPU instead of a vector-matrix multiplication
Multigrid on GPUs (cont.)
We can embed the new “rescale” easily into the vector
representation by simply rendering one textured quad.
Vector Texture
Interpolation
Render Target
Render Target
Extrapolation
Vector Texture
Selected References
•
Chorin, A.J., Marsden, J.E. A Mathematical Introduction to Fluid Mechanics. 3rd ed. Springer. New
York, 1993
•
Briggs, Henson, McCormick A Multigrid Tutorial, 2nd ed. siam, ISBN 0-89871-462-1
•
Acton Numerical Methods that Work, The Mathematical Association of America ISBN 0-88385-450-3
•
Krüger, J. Westermann, R. Linear algebra operators for GPU implementation of numerical algorithms,
In Proceedings of SIGGRAPH 2003, ACM Press / ACM SIGGRAPH
•
Bolz , J., Farmer, I., Grinspun, E., Schröder, P. Sparse Matrix Solvers on the GPU: Conjugate
Gradients and Multigrid, In Proceedings of SIGGRAPH 2003, ACM Press / ACM SIGGRAPH
•
Hillesland, K. E. Nonlinear Optimization Framework for Image-Based Modeling on Programmable
Graphics Hardware, In Proceedings of SIGGRAPH 2003, ACM Press / ACM SIGGRAPH
•
Fedkiw, R., Stam, J. and Jensen, H.W. Visual Simulation of Smoke. In Proceedings of SIGGRAPH
2001, ACM Press / ACM SIGGRAPH. 2001.
•
Stam, J. Stable Fluids. In Proceedings of SIGGRAPH 1999, ACM Press / ACM SIGGRAPH, 121-128.
1999.
•
Harris, M., Coombe, G., Scheuermann, T., and Lastra, A. Physically-Based Visual Simulation on
Graphics Hardware.. Proc. 2002 SIGGRAPH / Eurographics Workshop on Graphics Hardware 2002.