2015年8月26日 · A GPU contains two or more Streaming Multiprocessors (SM) depending upon the compute capablity value. Each SM consists of Streaming Processors (SP) which are actually responisible for the execution of instructions.

2016年2月20日 · For the GTX 970 there are 13 Streaming Multiprocessors (SM) with 128 Cuda Cores each. Cuda Cores are also called Stream Processors (SP). You can define grids which maps blocks to the GPU. You can define blocks which map threads to Stream Processors (the 128 Cuda Cores per SM). One warp is always formed by 32 threads and all threads of a warp are executed simulaneously. To use the full possible ...

I know I can get the compute capabilty by just visiting this official cuda page, or this wiki page. But I dont know how I am supposed to find the sm of my card. Is this short for shader model? or ...

2016年2月26日 · Compile for the architecture (both virtual and real), that represents the GPUs you wish to target. A fairly simple form is: -gencode arch=compute_XX,code=sm_XX where XX is the two digit compute capability for the GPU you wish to target. If you wish to target multiple GPUs, simply repeat the entire sequence for each XX target.

SM means Streaming Multiprocessor / Shared Processor. As in this link How to determine number of GPU cores being utilized for a process? In Nvidia GTX 1080 there are 20 SMs, then utilization coul...

2012年5月3日 · In order to allow for architectural evolution, NVIDIA GPUs are released in different generations. New generations introduce major improvements in functionality and/or chip architecture, while GPU models within the same generation show minor configuration differences that „moderately‟ affect functionality, performance, or both.

2013年12月25日 · We all know that GPGPU has several stream multiprocesssors(SM) and each has a lot of stream processors(SP) when talking about its hardware architecture. But it introduces another conceptions block ...

2017年12月1日 · Is there a command to get the sm version of the gpu in given machine. Here is my use case: I build and run same cuda kernel on multiple machines. So I was wondering if there is a command which can detect sm version of gpu on the given system and pass that as arguement to nvcc: $ nvcc -arch=`gpuarch -device 0` mykernel.cu

2021年7月23日 · TL;DR The version you choose needs to correlate with your hardware, otherwise the code won't run, even if it compiles. So for example, if you want it to run on an RTX 3090, you need to make sure sm_80, sm_86 or sm_87 is in the list. sm_87 can do things that sm_80 might not be able to do, and it might do things faster that the others can do. Why does PyTorch need different way of installation ...

2011年2月23日 · To be more specific: GPU busy is the percentage of time over the last second that any of the SMs was busy, and the memory utilization is actually the percentage of time the memory controller was busy during the last second. You can keep the utilization counts near 100% by simply running a kernel on a single SM and transferring 1 byte over PCI-E back and forth. Utilization is not a "how well ...