wrong results given by the GPU the classical matr mul using shared memory outpus wrong results

Accelerated Computing CUDA CUDA Programming and Performance
1

i’ve tried to implement the classical matrix multiplication method that uses shared memory on a GTS250 ; but gpu seems to output some slightly different results (sometines a big difference) than that computed in a cpu code …
i’ve tried the same code in an emulation mode (using -deviceemu) and results were 100% correct …
then that shows that there’s something wrong with the device ??
what could be the reason ??

2

i’ve tried to implement the classical matrix multiplication method that uses shared memory on a GTS250 ; but gpu seems to output some slightly different results (sometines a big difference) than that computed in a cpu code …
i’ve tried the same code in an emulation mode (using -deviceemu) and results were 100% correct …
then that shows that there’s something wrong with the device ??
what could be the reason ??

3

nothing explains the errors that are obtained ?
could it be that in simulation, i didn’t specified the GPU version (GTS250) so he didn’t considered the real amount of shared memory or something like that ?

4

nothing explains the errors that are obtained ?
could it be that in simulation, i didn’t specified the GPU version (GTS250) so he didn’t considered the real amount of shared memory or something like that ?

5

What data type are you using?

6

What data type are you using?

7

data type : float everywhere (cpu or gpu)
i want to know is there someway to specify the amount of shared memory per block in the simulator, as he’ll not able to know that i’m acutally using (gts250)

thnks

8

data type : float everywhere (cpu or gpu)
i want to know is there someway to specify the amount of shared memory per block in the simulator, as he’ll not able to know that i’m acutally using (gts250)

thnks