Jetson vs Kayla (compute capability), which one to choose ?

Hello there,

Pls consider the following configurations:

  1. Kayla devkit with GT640 (with GDDR5)
  2. jetson devkit with Tegra k1

From the site I learn option 1 is compute capability 3.5 . So far, apart from this article (which says it is 3.2 ) i have no idea what the compute capability of tegra k1 is.

So here is my question: Between 1 & 2 which has higher compute capability ? Could somebody run cudadevicequery for both options and post here ?

Thank you,

I guess I found the answer to the first question here.

But still would be great if someone could dump the cudadevicequery output here. Also could someone point me to an online resource comparing compute capability 3.2 vs 3.5 ?

./deviceQuery Starting…

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: “GK20A”
CUDA Driver Version / Runtime Version 6.0 / 6.0
CUDA Capability Major/Minor version number: 3.2
Total amount of global memory: 1746 MBytes (1831051264 bytes)
( 1) Multiprocessors, (192) CUDA Cores/MP: 192 CUDA Cores
GPU Clock rate: 960 MHz (0.96 GHz)
Memory Clock rate: 924 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 131072 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
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: 2048
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): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: No
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.0, CUDA Runtime Version = 6.0, NumDevs = 1, Device0 = GK20A
Result = PASS