我的Titan-XP上的设备查询显示我有30个多处理器,每个多处理器最多有2048个线程。认为可以同时在硬件上物理执行的最大线程数是30 * 2048是否正确?即:像以下内核配置会利用这个吗?
kernel<<<60, 1024>>>(...);
我真的希望物理上有最大数量的块执行,同时避免让块等待安排。这是设备查询的完整输出:
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "TITAN Xp"
CUDA Driver Version / Runtime Version 9.0 / 9.0
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 12190 MBytes (12781682688 bytes)
(30) Multiprocessors, (128) CUDA Cores/MP: 3840 CUDA Cores
GPU Max Clock rate: 1582 MHz (1.58 GHz)
Memory Clock rate: 5705 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 3145728 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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: 65536
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 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: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 4 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 9.0, NumDevs = 1, Device0 = TITAN Xp
Result = PASS
答案 0 :(得分:3)
是的,你的结论是正确的。对于CUDA 9或CUDA 9.1支持的所有GPU,可以“在飞行中”的最大线程数为2048 *。 (由CUDA 8支持的Fermi GPU在1536 * SM的情况下略低一些)
这是一个上限,你的内核的细节(资源利用率)可能意味着少于这个数字实际上可以“驻留”或“在飞行中”。这是GPU占用的一般主题。 CUDA包含一个占用率计算器电子表格以及一个程序化occupancy API来帮助确定您的特定内核。
使用有限数量的线程(例如,在您的情况下为60 * 1024)处理任意数据集大小的通常内核策略是使用某种形式的称为grid striding loop的构造。