EnerBe

Energy Benchmarks

Build instructions

Requirements

• C and C++ Compliers (GNU preferred)
• BLAS libraries (OpenBLAS preferred)
• ROCM or CUDA Compilers if compiling for AMD or NVIDIA GPUs
• CMake

Simplest example

git clone https://github.com/benczaja/EnerBe.git
cd EnerBe
mkdir build && cd build
cmake ../
make 
make install

With CUDA:

cmake -DENABLE_CUDA=1 ..
make && make install

With HIP:

cmake -DENABLE_HIP=1 ..
make && make install

With PMT:

If you do not have PMT, PMT will be installed (https://git.astron.nl/RD/pmt)

cmake -DENABLE_PMT=1 ..
make && make install

With PMT and CUDA

cmake -DENABLE_PMT=1 -DENABLE_CUDA=1 ..

or HIP

cmake -DENABLE_PMT=1 -DENABLE_HIP=1 ..

Running instuctions

There will be two executables sEnerBe of single FP precision and dEnerBe for double FP precision.

Each Algorithm will be run 20 times, or until 5 minutes is reached. The Standard deviation and variation of all runs will be calculated.

List the available algorithms

./bin/sEnerBe -h

cblas sgemm example (with PMT enabled)

Results are from a AMD EPYC 9654 96-Core Processor (Genoa) Dual socket processor

../bin/sEnerBe --xgemm-cblas 5000

output:

NAME: xgemm
ALGO: cblas
PRECISION: 4 bytes
OMP_THREADS: 192
MPI_RANKS: 0
NGPUs: 0
GPU ID: 99
SIZE: 5000
(RAPL) CPU_TIME: 0.241004 s
(RAPL) CPU_TIME_var: 0.00449523 s^2
(RAPL) CPU_TIME_std: 0.0670465 s
(RAPL) CPU_WATTS: 383.458 W
(RAPL) CPU_WATTS_var: 787.34 W^2
(RAPL) CPU_WATTS_std: 28.0596 W
(RAPL) CPU_JOULES: 92.6907 J
(RAPL) CPU_JOULES_var: 797.627 J^2
(RAPL) CPU_JOULES_std: 28.2423 J
NRUNS: 20