eigen/bench/tensors
2023-02-08 01:25:06 +00:00
..
benchmark_main.cc Fixed the tensor benchmarks on apple devices 2016-01-28 21:08:07 -08:00
benchmark.h Updated the benchmarking code to print the number of flops processed instead of the number of bytes. 2016-01-28 16:51:40 -08:00
contraction_benchmarks_cpu.cc Added benchmarks for contraction on CPU. 2016-05-13 14:32:17 -07:00
eigen_sycl_bench_contract.sh Remove LGPL Code and references. 2023-02-08 01:25:06 +00:00
eigen_sycl_bench.sh Remove LGPL Code and references. 2023-02-08 01:25:06 +00:00
README Remove LGPL Code and references. 2023-02-08 01:25:06 +00:00
tensor_benchmarks_cpu.cc Extended the tensor benchmark suite to support types other than floats 2016-02-23 05:28:02 +00:00
tensor_benchmarks_fp16_gpu.cu Added a benchmark to measure the performance of full reductions of 16 bit floats 2016-05-05 14:15:11 -07:00
tensor_benchmarks_gpu.cu Added benchmarks for full reduction 2016-02-29 14:57:52 -08:00
tensor_benchmarks_sycl.cc Eigen moved the scanLauncehr function inside the internal namespace. 2020-05-11 16:10:33 +01:00
tensor_benchmarks.h Cleanup 2022-01-21 01:48:59 +00:00
tensor_contract_sycl_bench.cc [SYCL-2020 Support] Enabling Intel DPCPP Compiler support to Eigen 2023-01-16 07:04:08 +00:00

The tensor benchmark suite is made of several parts.

The first part is a generic suite, in which each benchmark comes in 2 flavors: one that runs on CPU, and one that runs on GPU.

To compile the floating point CPU benchmarks, simply call:
g++ tensor_benchmarks_cpu.cc benchmark_main.cc -I ../../ -std=c++11 -O3 -DNDEBUG -pthread -mavx -o benchmarks_cpu

To compile the floating point GPU benchmarks, simply call:
nvcc tensor_benchmarks_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBUG -use_fast_math -ftz=true -arch compute_35 -o benchmarks_gpu

We also provide a version of the generic GPU tensor benchmarks that uses half floats (aka fp16) instead of regular floats. To compile these benchmarks, simply call the command line below. You'll need a recent GPU that supports compute capability 5.3 or higher to run them and nvcc 7.5 or higher to compile the code.
nvcc tensor_benchmarks_fp16_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBUG -use_fast_math -ftz=true -arch compute_53 -o benchmarks_fp16_gpu

To compile and run the benchmark for SYCL, using ComputeCpp, simply run the
following commands:
1. export COMPUTECPP_PACKAGE_ROOT_DIR={PATH TO COMPUTECPP ROOT DIRECTORY}
2. bash eigen_sycl_bench.sh

To compile the floating point GPU benchmarks using Intel DPCPP compiler 
/path/to/dpcpp/bin/clang+  -DSYCL_COMPILER_IS_DPCPP -DNDEBUG -DEIGEN_USE_SYCL=1 -I ../../  -O3 -DNDEBUG -fsycl -fsycl-targets="supported backend in DPCPP. i.e. spir64 or nvptx64-nvidia-cuda"  -std=c++17  tensor_benchmarks_sycl.cc benchmark_main.cc  -lpthread -o eigen_dpcpp_sycl

Last but not least, we also provide a suite of benchmarks to measure the scalability of the contraction code on CPU. To compile these benchmarks, call
g++ contraction_benchmarks_cpu.cc benchmark_main.cc -I ../../ -std=c++11 -O3 -DNDEBUG -pthread -mavx -o benchmarks_cpu

To compile the contraction with DPCPP: 
/path/to/dpcpp/bin/clang++  -DSYCL_COMPILER_IS_DPCPP -DNDEBUG -DEIGEN_USE_SYCL=1 -I ../../  -O3 -DNDEBUG -fsycl -fsycl-targets="supported backend in DPCPP. i.e. spir64 or nvptx64-nvidia-cuda" -std=c++17   tensor_contract_sycl_bench.cc -lpthread -o eigen_dpcpp_contract