diff --git a/.gitignore b/.gitignore index cfdd3bd..13566e2 100644 --- a/.gitignore +++ b/.gitignore @@ -149,6 +149,9 @@ dmypy.json # pytype static type analyzer .pytype/ +# Ruff cache +.ruff_cache/ + # Cython debug symbols cython_debug/ diff --git a/30_perf_analysis.py b/30_perf_analysis.py index ae00277..0e69852 100644 --- a/30_perf_analysis.py +++ b/30_perf_analysis.py @@ -4,7 +4,7 @@ The generated plots are saved as a PNG file with the specified filename. Usage: - python 30_perf_analysis.py + python 30_perf_analysis.py """ import argparse @@ -20,14 +20,20 @@ parser = argparse.ArgumentParser( description="Generate benchmark plots and save to specified file." ) +parser.add_argument( + "path_to_folder_with_csv", type=str, help="Path to the folder with all your csv you want to show." +) +parser.add_argument( + "traj_dimension", type=int, help="Indicate if your trajectory is in 2D or 3D." +) parser.add_argument( "output_filename", type=str, help="Name of the output file (without extension)." ) args = parser.parse_args() # Directory where benchmark result files are stored -BENCHMARK_DIR = "./outputs" -results_files = glob.glob(BENCHMARK_DIR + "/CPU/**/*.csv", recursive=True) +BENCHMARK_DIR = args.path_to_folder_with_csv +results_files = glob.glob(BENCHMARK_DIR + "**/*.csv", recursive=True) # Read and concatenate all CSV files into a single DataFrame df = pd.concat(map(pd.read_csv, results_files)) @@ -63,14 +69,16 @@ # Custom palette with specified colors custom_palette = {1: "black", 12: "darkblue", 32: "purple"} -# Define x-axis limits for each metric -xlims = { - k: v - for k, v in zip( - metrics.keys(), - [(0, 35), (0, 80)] if num_metrics == 2 else [(0, 5), (0, 80), (0, 20)], - ) -} +# Define x-axis limits for each metric, based on the precedent range of values +# first time, then memory, then GPU memory in respective seconds, GB and GB +if num_metrics == 2: # if CPU only + limits = [(0, 35), (0, 80)] +elif args.traj_dimension == 2: # if 2D trajectory + limits = [(0, 0.3), (0, 7), (0, 1)] +else: + limits = [(0, 1), (0, 50), (0, 20)] + +xlims = {k: v for k, v in zip(metrics.keys(), limits)} # Generate bar plots for each task and metric for row, task in zip(axs, tasks): @@ -94,12 +102,13 @@ max_limit = xlims[k][1] for container in ax.containers: labels = [ - f"{v:.1f}" if v >= max_limit else "" for v in container.datavalues + f"{v:.3f}" if v >= max_limit else "" for v in container.datavalues ] ax.bar_label( container, labels=labels, label_type="center", color="white", fontsize=6 ) + # Set axis labels for ax, xlabel in zip(axs[-1, :], metrics.values()): ax.set_xlabel(xlabel) @@ -126,9 +135,12 @@ ax.set_xlim(xlim) xticks = ax.get_xticks() ax.set_xticks(xticks) - ax.set_xticklabels([f"{xt:.1f}" for xt in xticks]) + ax.set_xticklabels([f"{int(xt)}" if xt % 1 == 0 else f"{xt:.1f}" for xt in xticks]) # Save the figure to the specified directory with the provided filename output_file = BENCHMARK_DIR + f"/{args.output_filename}.png" plt.savefig(output_file) plt.show() + +# Save the DataFrame to a CSV file +df.to_csv( BENCHMARK_DIR + f"/{args.output_filename}.csv", index=False) \ No newline at end of file diff --git a/README.md b/README.md index 6646dff..bb4a366 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,6 @@ This are a collection of script to perform benchmarking of MRI-NUFFT operations. They rely on the hydra configuration package and hydra-callback for measuring statistics. (see `requirements.txt`) - To fully reproduce the benchmarks 4 steps are necessary: 0. Get a Cartesian Reference image file, name `cpx_cartesian.npy` @@ -14,24 +13,55 @@ To fully reproduce the benchmarks 4 steps are necessary: Elif a 3D traj , `python 00_trajectory3D.py` + shape of your data 2. Run the benchmarks. Currently are available: - The Performance benchmark, checking the CPU/GPU usage and memory footprint for the different backend and configuration `perf` folder. - If you have a configuration for 1 backend, 1 traj and 1 coil you can use `python 10_benchmark_perf.py` for you perf analysis. - If you want to make several benchmark in a row, you can run `python auto_benchmark_perf.py` - Backends, trajectories and coils can be managed directly at the start of this script. - - In every case don't forget to install the necessary dependencies for each backend + You can use `python 10_benchmark_perf.py` for you perf analysis. + You can change some parameters like : + `python 10_benchmark_perf.py data.n_coils=32 backend.name=cufinufft` + You can also do multirun like : + `python 10_benchmark_perf.py -m data.n_coils=1,12,32 backend.name=cufinufft,finufft trajectory=./trajs/radial_256x256_0.5.bin,./trajs/stack2D_of_spiral_256x256_0.5.bin ` + + For running on Jean Zay, follow [this step](https://github.com/zaccharieramzi/jz-hydra-submitit-launcher) for the installation. + Then you can run : + `hydra-submitit-launch 10_benchmark_perf.py dev max_time=5.0 data.n_coils=1,12,32 trajectory=./radial_256x256_0.5.bin,./stack2D_of_spiral_256x256_0.5.bin backend.name=tensorflow ` + + In every case don't forget to install the necessary dependencies for each backend. - The Quality benchmark that check how the pair trajectory/backend performs for the reconstruction. All the configuration is modifiable in `qual` folder. To launch the quality benchmark run `python 20_benchmark_quality.py` -3. Generate some analysis figures using `python 30_perf_analysis.py` + title of the figures - At the start of the script, you need to indicate which folder the performance files are in. - Caution: to get beautiful graphs, you'll probably have to change the plot parameters (bar colors, abscissa max, number of digits after the decimal point, text size on the plots, etc.). +3. Generate some analysis figures using `python 30_perf_analysis.py` + path to the folder with all .csv + trajectory dimension use (2 or 3) + title of the figures we gonna save. + +## Some results : +All axes are the same size for each benchmark, allowing for better comparison between them. + +# Benchmark backend performance on 2D images and trajs. + +On cuda11 : +![result2D_old](results/2D/result2D_cuda11.png) + +On cuda12, with the new version on (cu)finufft 2.3 install with 'pip install' : +![result2D_new](results/2D/result2D_cuda12_release_with_pip.png) + +On cuda12, with the new version on (cu)finufft 2.3 install with 'pip install --no-binary' : +![result2D_new0.1](results/2D/result2D_cuda12_release_with_no_binary.png) + + +# Benchmark for GPU backend performance on 3D images and trajs. + +On cuda11 : +![result3D](results/3D/result3D_cuda11_gpu.png) + +On cuda12, with the new version on (cu)finufft 2.3 install with 'pip install' : +![result3D](results/3D/result3D_cuda12_gpu_pip.png) + +On cuda12, with the new version on (cu)finufft 2.3 install with 'pip install --no-binary' : +![result3D](results/3D/result3D_cuda12_gpu_no_binary.png) + +# Benchmark for CPU backend performance on 3D images and trajs. -This is some result : -Benchmark backend performance on 2D images and trajs. -![result2D](results/result2D.png) +On cuda11 : +![result3D](results/3D/result3D_cuda11_cpu.png) -Benchmark for GPU backend performance on 3D images and trajs. -![result3D](results/result3D_gpu.png) +On cuda12, with the new version on (cu)finufft 2.3 install with pip : +![result3D](results/3D/result3D_cuda12_cpu_pip.png) -Benchmark for CPU backend performance on 3D images and trajs. -![result3D](results/result3D_cpu.png) +On cuda12, with the new version on (cu)finufft 2.3 install with 'pip install --no-binary' : +![result3D](results/3D/result3D_cuda12_cpu_no_binary.png) \ No newline at end of file diff --git a/auto_benchmark_perf.py b/auto_benchmark_perf.py deleted file mode 100644 index afda5ff..0000000 --- a/auto_benchmark_perf.py +++ /dev/null @@ -1,81 +0,0 @@ -""" -This script generates benchmark configurations for different MRI reconstruction backends and trajectories, -executes the benchmarking script, and cleans up temporary configuration files. - -Usage: - 1. Ensure that all necessary backends are installed and accessible. - 2. Run 'python auto_benchmark_perf.py' - -The script performs the following tasks: - - Reads a base configuration file (`benchmark_config.yaml`) that defines default settings. - - Generates all combinations of backend names, trajectories, and number of coils specified. - - For each combination, it creates a temporary YAML configuration file. - - Calls the benchmark script with the generated configuration. - - Cleans up by deleting all temporary configuration files after execution. - -Note: - The benchmark script should be designed to accept a configuration file path as an argument. -""" - -import itertools -import subprocess -import yaml -import os - -# Define parameter lists and dependencies to install before running this script -backend_names = [ # mrinufft of course - # "finufft", # finufft - "gpunufft", # gpuNUFFT, cupy-cuda11x - "cufinufft", # cufinufft, cupy-cuda11x - "tensorflow", # tensorflow, cupy-cuda11x, tensorflow_nufft, tensorflow_mri => start by tensorflow_mri - # "torchkbnufft-cpu", # torchkbnufft, torch - "torchkbnufft-gpu", # torchkbnufft, torch -] -trajectories = [ - "./trajs/floret_256x256x176_0.5.bin", - "./trajs/seiffert_256x256x176_0.5.bin", - "./trajs/stack_of_spiral_256x256x176_0.5.bin", - # "./trajs/radial_256x256_0.5.bin", - # "./trajs/stack2D_of_spiral_256x256_0.5.bin" -] -n_coils_list = [12] - -# Read the base configuration file to copy from -with open("./perf/benchmark_config.yaml", "r") as file: - base_config = yaml.safe_load(file) - - -combinations = list(itertools.product(backend_names, trajectories, n_coils_list)) -benchmark_script = "10_benchmark_perf.py" -os.makedirs("temp_configs", exist_ok=True) - -# For each combination, create a temporary configuration file and execute the script -for backend_name, trajectory, n_coils in combinations: - config = base_config.copy() - config["backend"]["name"] = backend_name - config["trajectory"] = trajectory - config["data"]["n_coils"] = n_coils - - temp_config_path = "temp_configs" - temp_config_file = f"config_{backend_name}_{trajectory.split('/')[-1].split('.')[0]}_{n_coils}.yaml" - - complete_file = os.path.join(temp_config_path, temp_config_file) - with open(complete_file, "w") as file: - yaml.dump(config, file) - - subprocess.run( - [ - "python", - benchmark_script, - "--config-name", - temp_config_file, - "--config-path", - temp_config_path, - ] - ) - -# Clean up temporary configuration files -for file in os.listdir("temp_configs"): - complete_file = os.path.join("temp_configs", file) - os.remove(complete_file) -os.removedirs("temp_configs") diff --git a/perf/benchmark_config.yaml b/perf/benchmark_config.yaml index 6626b16..60dfccd 100644 --- a/perf/benchmark_config.yaml +++ b/perf/benchmark_config.yaml @@ -2,20 +2,19 @@ defaults: - override hydra/job_logging: default - override hydra/hydra_logging: default -max_time: 10.0 +max_time: 5.0 data: n_coils: 1 smaps: false dtype: complex64 -trajectory: "./trajs/floret_176x256x256_0.5.bin" +trajectory: "./trajs/radial_256x256_0.5.bin" task: - forward - adjoint - grad - backend: name: finufft eps: 1e-3 @@ -29,4 +28,7 @@ hydra: job: chdir: true run: - dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}/ + dir: outputs/running/${backend.name}_${now:%H-%M-%S}/ + sweep: + dir: outputs/running/${backend.name}_${now:%H-%M-%S}/ + diff --git a/result_csv/2D/result2D_cuda11.csv b/result_csv/2D/result2D_cuda11.csv new file mode 100644 index 0000000..7ae9f39 --- /dev/null +++ b/result_csv/2D/result2D_cuda11.csv @@ -0,0 +1,1775 @@ +backend,eps,upsampfac,n_coils,shape,n_samples,dim,sense,task,run,run_time,mem_avg,mem_peak,cpu_avg,cpu_peak,gpu0_mem_GiB_avg,gpu0_mem_GiB_peak,gpu0_usage_avg,gpu0_usage_peak,coil_time,coil_mem +torchkbnufft-gpu,0.001,2.0,32,"(256, 256)",655296,2,False,forward,5,0.0689303370018024,1.0693490164620536,1.8732147216796875,18.02857142857143,126.2,1.0304129464285714,1.130859375,2.2857142857142856,4.0,0.002154073031306325,0.058537960052490234 +torchkbnufft-gpu,0.001,2.0,12,"(256, 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