Multi-GPU stress test http://wili.cc/blog/gpu-burn.html
To build GPU Burn:
make
To remove artifacts built by GPU Burn:
make clean
GPU Burn
Usage: gpu_burn [OPTIONS] [TIME]
-m X Use X MB of memory
-m N% Use N% of the available GPU memory
-d Use doubles
-tc Try to use Tensor cores (if available)
-l List all GPUs in the system
-i N Execute only on GPU N
-h Show this help message
Example:
gpu_burn -d 3600Example output
$ ./gpu_burn
Run length not specified in the command line. Using compare file: compare.hsaco
Burning for 10 seconds.
========================================= ROCm System Management Interface =========================================
=================================================== Concise Info ===================================================
Device Node IDs Temp Power Partitions SCLK MCLK Fan Perf PwrCap VRAM% GPU%
(DID, GUID) (Edge) (Avg) (Mem, Compute, ID)
====================================================================================================================
0 1 0x740f, 7916 30.0°C 41.0W N/A, N/A, 0 800Mhz 1600Mhz 0% auto 300.0W 0% 0%
1 2 0x740f, 28773 34.0°C 41.0W N/A, N/A, 0 800Mhz 1600Mhz 0% auto 300.0W 0% 0%
====================================================================================================================
=============================================== End of ROCm SMI Log ================================================
Initialized device 0 with 65520 MB of memory (65452 MB available, using 58906 MB of it), using FLOATS
Results are 268435456 bytes each, thus performing 228 iterations
Initialized device 1 with 65520 MB of memory (65452 MB available, using 58906 MB of it), using FLOATS
Results are 268435456 bytes each, thus performing 228 iterations
Killing processes with SIGTERM (soft kill)
Freed memory for dev 0
Uninitted cublas
Freed memory for dev 1
Uninitted cublas
done
Tested 2 GPUs:
GPU 0: OK
GPU 1: OK