I’m having trouble trying to expand my code out to perform with multiple cases. I have been developing with the most common case in mind, now its time for testing and i need to ensure that it all works for the different cases. Currently my kernel is executed within a loop (there are reasons why we aren’t doing one kernel call to do the whole thing.) to calculate a value across the row of a matrix. The most common case is 512 columns by 512 rows. I need to consider matricies of the size 512 x 512, 1024 x 512, 512 x 1024, and other combinations, but the largest will be a 1024 x 1024 matrix. I have been using a rather simple kernel call:
launchKernel<<<1,512>>>(................)
This kernel works fine for the common 512x512 and 512 x 1024 (column, row respectively) case, but not for the 1024 x 512 case. This case requires 1024 threads to execute. In my naivity i have been trying differnt versions of the simple kernel call to launch 1024 threads.
launchKernel<<<2,512>>>(................) // 2 blocks with 512 threads each ???
launchKernel<<<1,1024>>>(................) // 1 block with 1024 threads ???
I beleive my problem has something to do with my lack of understanding of the threads and blocks
Here is the output of deviceQuery, as you can see i can have a max of 1024 threads
C:\ProgramData\NVIDIA Corporation\NVIDIA GPU Computing SDK 4.1\C\bin\win64\Release\deviceQuery.exe Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Found 2 CUDA Capable device(s)
Device 0: "Tesla C2050"
CUDA Driver Version / Runtime Version 4.2 / 4.1
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 2688 MBytes (2818572288 bytes)
(14) Multiprocessors x (32) CUDA Cores/MP: 448 CUDA Cores
GPU Clock Speed: 1.15 GHz
Memory Clock rate: 1500.00 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 786432 bytes
Max Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536,65535), 3D=(2048,2048,2048)
Max Layered Texture Size (dim) x layers 1D=(16384) x 2048, 2D=(16384,16384) x 2048
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per block: 1024
Maximum sizes of each dimension of a block: 1024 x 1024 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 65535
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and execution: Yes with 2 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Concurrent kernel execution: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support enabled: Yes
Device is using TCC driver mode: No
Device supports Unified Addressing (UVA): No
Device PCI Bus ID / PCI location ID: 40 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 1: "Quadro 600"
CUDA Driver Version / Runtime Version 4.2 / 4.1
CUDA Capability Major/Minor version number: 2.1
Total amount of global memory: 1024 MBytes (1073741824 bytes)
( 2) Multiprocessors x (48) CUDA Cores/MP: 96 CUDA Cores
GPU Clock Speed: 1.28 GHz
Memory Clock rate: 800.00 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 131072 bytes
Max Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536,65535), 3D=(2048,2048,2048)
Max Layered Texture Size (dim) x layers 1D=(16384) x 2048, 2D=(16384,16384) x 2048
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per block: 1024
Maximum sizes of each dimension of a block: 1024 x 1024 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 65535
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Concurrent kernel execution: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support enabled: No
Device is using TCC driver mode: No
Device supports Unified Addressing (UVA): No
Device PCI Bus ID / PCI location ID: 15 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 4.2, CUDA Runtime Version = 4.1, NumDevs = 2, Device = Tesla C2050, Device = Quadro 600
I am using only the Tesla C2050 device
Here is a stripped out version of my kernel, so you have an idea of what it is doing.
#define twoPi 6.283185307179586
#define speed_of_light 3.0E8
#define MaxSize 999
__global__ void launchKernel
(
const float *array1,
const double *array2,
const float scalar1,
const float scalar2,
const float scalar3,
const float scalar4,
const float scalar5,
const float scalar6,
const int scalar7,
const int scalar8,
float *outputArray1,
float *outputArray2)
{
float scalar9;
int idx;
double scalar10;
double scalar11;
float sumReal, sumImag;
float real, imag;
float coeff1, coeff2, coeff3, coeff4;
sumReal = 0.0;
sumImag = 0.0;
// kk loop 1 .. 512 (scalar7)
idx = (blockIdx.x * blockDim.x) + threadIdx.x;
/* Declare the shared memory parameters */
__shared__ float SharedArray1[MaxSize];
__shared__ double SharedArray2[MaxSize];
/* populate the arrays on shared memory */
SharedArray1[idx] = array1[idx]; // first 512 elements
SharedArray2[idx] = array2[idx];
if (idx+blockDim.x < MaxSize){
SharedArray1[idx+blockDim.x] = array1[idx+blockDim.x];
SharedArray2[idx+blockDim.x] = array2[idx+blockDim.x];
}
__syncthreads();
// input scalars used here.
scalar10 = ...;
scalar11 = ...;
for (int kk = 0; kk < scalar8; kk++)
{
/* some calculations */
// SharedArray1, SharedArray2 and scalar9 used here
sumReal = ...;
sumImag = ...;
}
/* calculation of the exponential of a complex number */
real = ...;
imag = ...;
coeff1 = (sumReal * real);
coeff2 = (sumReal * imag);
coeff3 = (sumImag * real);
coeff4 = (sumImag * imag);
outputArray1[idx] = (coeff1 - coeff4);
outputArray2[idx] = (coeff2 + coeff3);
}
Because my max threads per block is 1024, i thought i would be able to continue to use the simple kernel launch, am i wrong?
How do i successfully launch each kernel with 1024 threads?