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https://github.com/eth-cscs/node-burn


https://github.com/eth-cscs/node-burn

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README 

        
# NODE BURN



Because sometimes burning the GPU is not enough.



A simple tool for running compute or memory intensive workloads on both CPU and GPU, in order to understand

* the performance of the individual components

* the impact of the workloads on one another

* the maximum power consumption of a node



## Features



Node burn can run GEMM or STREAM workloads on the GPU only, CPU only, or both simultaneously.



```bash

# run GEMM with matrix dimension 5000*5000 on the GPU,

# and STREAM triad with length 500000 on the CPU, for 30 seconds.

./burn -ggemm,5000 -cstream,500000 -d30



# run GEMM on the GPU, nothing on the CPU, for 3 minutes.

./burn -cgemm,5000 -d180



# run GEMM on the CPU, nothing on the GPU, for 20 seconds.

./burn -ggemm,5000 -d20

```



Sometimes we want to run multiple instances of node burn in a parallel job, e.g. 4 instances on a node with 4 GPUs, or one instance on every GPU in a cabinet, to ~see if anything catches on fire~ understand system behavior under load.

Use the `--batch` option to produce less verbose output that can be easily parsed by a post-processing script.



```bash

# on a system with 4 GPUs per node, use all 16 GPUs on 4 nodes to

# run GEMM with matrix dimension 10000*10000 on the GPU for 30 seconds

srun -n16 -N4 ./burn --batch -ggemm,10000 -d30

nid001272:gpu    584 iterations, 38930.92 GFlops,     30.0 seconds,    2.400 Gbytes

nid001272:gpu    579 iterations, 38555.99 GFlops,     30.0 seconds,    2.400 Gbytes

nid001272:gpu    561 iterations, 37348.14 GFlops,     30.0 seconds,    2.400 Gbytes

nid001272:gpu    600 iterations, 39939.47 GFlops,     30.0 seconds,    2.400 Gbytes

nid001278:gpu    585 iterations, 38994.35 GFlops,     30.0 seconds,    2.400 Gbytes

nid001278:gpu    584 iterations, 38914.98 GFlops,     30.0 seconds,    2.400 Gbytes

nid001278:gpu    589 iterations, 39200.59 GFlops,     30.1 seconds,    2.400 Gbytes

nid001278:gpu    589 iterations, 39204.37 GFlops,     30.0 seconds,    2.400 Gbytes

nid001274:gpu    557 iterations, 37091.74 GFlops,     30.0 seconds,    2.400 Gbytes

nid001274:gpu    560 iterations, 37289.96 GFlops,     30.0 seconds,    2.400 Gbytes

nid001274:gpu    542 iterations, 36090.85 GFlops,     30.0 seconds,    2.400 Gbytes

nid001274:gpu    503 iterations, 33473.36 GFlops,     30.1 seconds,    2.400 Gbytes

nid001276:gpu    584 iterations, 38929.67 GFlops,     30.0 seconds,    2.400 Gbytes

nid001276:gpu    589 iterations, 39253.24 GFlops,     30.0 seconds,    2.400 Gbytes

nid001276:gpu    588 iterations, 39170.08 GFlops,     30.0 seconds,    2.400 Gbytes

nid001276:gpu    589 iterations, 39224.21 GFlops,     30.0 seconds,    2.400 Gbytes

```



### Power Measurement on HPE-Cray systems



If running on a HPE Cray-EX system with `pm_counters`, nodeburn can be configured to generate a report of power consumption on each node. Enable it at build time with the `NB_PMCOUNTERS` CMake option (see below).



`node-burn` will generate power reports from all of the energy counters that it can detect on each node - the values reported will vary according to the node architecture.



## Requirements



C++20 for the C++ code, C++17 for the CUDA code.



It has only been tested with GCC 11+ and CUDA 11.8+.

* not tested with Clang, Intel, NVC, Cray compilers. It should work if the compiler is recent.



## Compiling



Node burn uses CMake to configure the build. There is currently one option, `NB_GPU` which can be used to to disable CUDA targets.



```bash

# by default node burn will attempt to build for CUDA devices.

CC=gcc CXX=g++ cmake $src_path



# explicitly disable building for CUDA

CC=gcc CXX=g++ cmake $src_path -DNB_GPU=off

```



On HPE Cray-EX systems, power readings from `pm_counters` can be generated using the `NB_PMCOUNTERS` option.

```bash

# enable pm counters for average power consumption

CC=gcc CXX=g++ cmake $src_path -DNB_PMCOUNTERS=on

```