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Hm...I f'ed up the matrix multiplication slide in the presentation.

Thankfully the calculation in the example program was correct.
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1 parent a96f192 commit e964da13032882ef6fd59bae33e8d549ca2c7a8f @mre committed Dec 2, 2011
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@@ -4,7 +4,7 @@
![Cube](Grafiken/matrixmult1.png)
-!SLIDE
+<!SLIDE transition=fade>
# Beispiel: Matrixprodukt
@@ -36,7 +36,7 @@
## Parallelisierung mit CUDA
@@@ C
- void mult_matrix(float *M, float *N, float *P)
+ void mult_matrix(float *M, float *N, float *P)
{
@@ -45,7 +45,7 @@
* - Allocate device memory
* - Copy matrices to device
* - Perform calculation
- * - Copy result and free matrices
+ * - Copy result and free matrices
*/
@@ -57,7 +57,7 @@
## Parallelisierung mit CUDA
@@@ C
- void mult_matrix(float *M, float *N, float *P)
+ void mult_matrix(float *M, float *N, float *P)
{
/* Allocate device memory */
float *Md, *Nd, *Pd;
@@ -78,7 +78,7 @@
## Parallelisierung mit CUDA
@@@ C
- void mult_matrix(float *M, float *N, float *P)
+ void mult_matrix(float *M, float *N, float *P)
{
/* Allocate device memory... */
@@ -99,7 +99,7 @@
## Parallelisierung mit CUDA
@@@ C
- void mult_matrix(float *M, float *N, float *P)
+ void mult_matrix(float *M, float *N, float *P)
{
/* Allocate device memory... */
/* Copy matrices to device... */
@@ -146,13 +146,13 @@
## Parallelisierung mit CUDA
@@@ C
- void mult_matrix(float *M, float *N, float *P)
+ void mult_matrix(float *M, float *N, float *P)
{
/* Allocate device memory... */
/* Copy matrices to device... */
/* Perform calculation... */
- /* Copy result from device to host */
+ /* Copy result from device to host */
cudaMemcpy(P, Pd, size, cudaMemcpyDeviceToHost);
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