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Why I can't use max of Max dimension size of a thread block (x,y,z): (1024, 1024, 64)? If I use (1024, 1024) it doesn't work, and when I use (32, 32) or (1, 1024) etc. it works. Is it about shared memory?
Here is my result from deviceQuery:
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 3 CUDA Capable device(s)
Device 0: "Tesla M2070"
CUDA Driver Version / Runtime Version 5.5 / 5.5
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 5375 MBytes (5636554752 bytes)
(14) Multiprocessors, ( 32) CUDA Cores/MP: 448 CUDA Cores
GPU Clock rate: 1147 MHz (1.15 GHz)
Memory Clock rate: 1566 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 786432 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65535), 3D=(2048, 2048, 2048)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
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 multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (65535, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 6 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 1: "Tesla M2070"
CUDA Driver Version / Runtime Version 5.5 / 5.5
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 5375 MBytes (5636554752 bytes)
(14) Multiprocessors, ( 32) CUDA Cores/MP: 448 CUDA Cores
GPU Clock rate: 1147 MHz (1.15 GHz)
Memory Clock rate: 1566 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 786432 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65535), 3D=(2048, 2048, 2048)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
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 multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (65535, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 20 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 2: "Tesla M2070"
CUDA Driver Version / Runtime Version 5.5 / 5.5
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 5375 MBytes (5636554752 bytes)
(14) Multiprocessors, ( 32) CUDA Cores/MP: 448 CUDA Cores
GPU Clock rate: 1147 MHz (1.15 GHz)
Memory Clock rate: 1566 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 786432 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65535), 3D=(2048, 2048, 2048)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
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 multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (65535, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 17 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
> Peer access from Tesla M2070 (GPU0) -> Tesla M2070 (GPU1) : No
> Peer access from Tesla M2070 (GPU0) -> Tesla M2070 (GPU2) : No
> Peer access from Tesla M2070 (GPU1) -> Tesla M2070 (GPU1) : No
> Peer access from Tesla M2070 (GPU1) -> Tesla M2070 (GPU2) : Yes
> Peer access from Tesla M2070 (GPU1) -> Tesla M2070 (GPU0) : No
> Peer access from Tesla M2070 (GPU1) -> Tesla M2070 (GPU1) : No
> Peer access from Tesla M2070 (GPU2) -> Tesla M2070 (GPU0) : No
> Peer access from Tesla M2070 (GPU2) -> Tesla M2070 (GPU1) : Yes
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 5.5, CUDA Runtime Version = 5.5, NumDevs = 3, Device0 = Tesla M2070, Device1 = Tesla M2070, Device2 = Tesla M2070
Result = PASS
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Remember: CUDA Streaming Multiprocessor executes threads in warps (32 threads) There is a maximum of 1024 threads per block (for our GPU) There is a maximum of 1536 threads per multiprocessor (for our GPU)
Maximum number of threads in a block The maximum number of threads in the block is limited to 1024. This is the product of whatever your threadblock dimensions are (x*y*z). For example (32,32,1) creates a block of 1024 threads.
Features and Technical Specifications points out that Maximum number of threads per block and Maximum x- or y-dimension of a block are both 1024. Thus, the maximum value of block_size can be 1024. In a block, a warp is made up of 32 consecutive threads.
x and higher, blocks may contain up to 1024 threads. The threads in the same thread block run on the same stream processor. Threads in the same block can communicate with each other via shared memory, barrier synchronization or other synchronization primitives such as atomic operations.
Why I can't use max of Max dimension size of a thread block (x,y,z): (1024, 1024, 64)?
Because each one of those is an individual limit for that dimension. There is an additional overall limit also indicated in your deviceQuery printout:
Maximum number of threads per block: 1024
A threadblock is up to a 3-dimensional structure, so the total number of threads in a block is equal to the product of the individual dimensions that you choose. This product must also be less than or equal to 1024 (and greater than 0). This is just another hardware limit of the device.
Is it about shared memory?
The above is unrelated to any usage of shared memory. (Your code doesn't appear to be using shared memory anyway.)
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