initial_commit
This commit is contained in:
commit
8b80f1fd28
11
.gitignore
vendored
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11
.gitignore
vendored
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# Ignore everything
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*
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# But not these!
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!.gitignore
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!README.md
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!*.py
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!*.template
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# Optional: Keep subdirectories and their Python files
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!*/
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79
launch_bench_multinode.py
Executable file
79
launch_bench_multinode.py
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import os
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import subprocess
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from datetime import datetime
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def load_template(template_path: str):
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output_template = ""
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with open(template_path, "r") as handle:
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output_template = handle.read()
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return output_template
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def write_batch(batch_fpath: str, batch_content: str):
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with open(batch_fpath, "w") as handle:
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_ = handle.write(batch_content)
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collectives = ["Reduce",
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# "Reduce_scatter",
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# "Reduce_scatter_block",
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# "Allreduce",
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# "Allgather",
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# "Allgatherv",
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# "Scatter",
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# "Scatterv",
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# "Gather",
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# "Gatherv",
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# "Alltoall",
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# "Bcast",
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# "Barrier"
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]
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procnt = [
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18,
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# 36,
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# 54,
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# 72
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]
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mpi1_bin = "/home/hpc/ihpc/ihpc136h/workspace/prototyping/bin"
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slurm_template = load_template("templates/bench.template")
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template_parameter = {"time_stamp": datetime.now().strftime("%y_%m_%d_%H-%M-%S"),
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"job_name": "",
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"output_dir": os.getcwd()+"/output/",
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"err_dir": os.getcwd()+"/error/",
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"data_dir": os.getcwd()+"/data/",
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"n_procs": 18,
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"off_mem_flag": "",
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"bin": mpi1_bin
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}
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output_dir = os.getcwd()+"/output/"
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err_dir = os.getcwd()+"/error/"
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batch_files_dir = os.getcwd()+"/batchs/"
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data_dir = os.getcwd()+"/data/"
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if os.path.isdir(output_dir) == False:
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os.mkdir(output_dir)
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if os.path.isdir(err_dir) == False:
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os.mkdir(err_dir)
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if os.path.isdir(data_dir) == False:
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os.mkdir(data_dir)
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if os.path.isdir(batch_files_dir) == False:
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os.mkdir(batch_files_dir)
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log = ""
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for n_procs in procnt:
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template_parameter["n_procs"] = n_procs
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for collective in collectives:
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template_parameter["job_name"] = collective
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write_batch(batch_files_dir+collective+".sh",
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slurm_template.format(**template_parameter))
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result = subprocess.run(["sbatch", batch_files_dir+collective+".sh"],
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capture_output=True, text=True)
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log += f"#{collective} {n_procs}" + "\n"
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log += "\tSTDOUT:" + result.stdout + "\n"
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log += "\tSTDERR:" + result.stderr + "\n"
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print(log)
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79
launch_bench_singlenode.py
Executable file
79
launch_bench_singlenode.py
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import os
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import subprocess
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from datetime import datetime
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def load_template(template_path: str):
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output_template = ""
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with open(template_path, "r") as handle:
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output_template = handle.read()
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return output_template
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def write_batch(batch_fpath: str, batch_content: str):
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with open(batch_fpath, "w") as handle:
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_ = handle.write(batch_content)
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collectives = ["Reduce",
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"Reduce_scatter",
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"Allreduce",
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"Allgather",
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"Allgatherv",
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"Scatter",
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"Scatterv",
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"Gather",
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"Gatherv",
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"Alltoall",
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"Bcast",
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# "Barrier"
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]
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procnt = [
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18,
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36,
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54,
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72
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]
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mpi1_bin = "/home/hpc/ihpc/ihpc136h/workspace/prototyping/bin/IMB-MPI1"
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slurm_template = load_template("templates/bench.template")
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template_parameter = {"time_stamp": datetime.now().strftime("%y_%m_%d_%H-%M-%S"),
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"job_name": "",
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"output_dir": os.getcwd()+"/output/",
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"err_dir": os.getcwd()+"/error/",
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"data_dir": os.getcwd()+"/data/",
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"n_procs": 18,
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"off_mem_flag": "",
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"bin": mpi1_bin
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}
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output_dir = os.getcwd()+"/output/"
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err_dir = os.getcwd()+"/error/"
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batch_files_dir = os.getcwd()+"/batchs/"
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data_dir = os.getcwd()+"/data/"
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if os.path.isdir(output_dir) == False:
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os.mkdir(output_dir)
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if os.path.isdir(err_dir) == False:
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os.mkdir(err_dir)
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if os.path.isdir(data_dir) == False:
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os.mkdir(data_dir)
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if os.path.isdir(batch_files_dir) == False:
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os.mkdir(batch_files_dir)
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log = ""
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for n_procs in procnt:
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template_parameter["n_procs"] = n_procs
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for collective in collectives:
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template_parameter["job_name"] = collective
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write_batch(batch_files_dir+collective+".sh",
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slurm_template.format(**template_parameter))
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result = subprocess.run(["sbatch", batch_files_dir+collective+".sh"],
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capture_output=True, text=True)
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log += f"#{collective} {n_procs}" + "\n"
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log += "\tSTDOUT:" + result.stdout + "\n"
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log += "\tSTDERR:" + result.stderr + "\n"
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print(log)
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_ = subprocess.run(["./clean.sh"])
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72
postprocess_data.py
Executable file
72
postprocess_data.py
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import pandas as pd
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import os
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data_markers = {
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"block_separator": "#----------------------------------------------------------------",
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"benchmark_type": "# Benchmarking",
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"processes_num": "# #processes = ",
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"min_bytelen": "# Minimum message length in bytes",
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"max_bytelen": "# Maximum message length in bytes",
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"mpi_datatype": "# MPI_Datatype :",
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"mpi_red_datatype": "# MPI_Datatype for reductions :",
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"mpi_red_op": "# MPI_Op",
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"end_of_table": "# All processes entering MPI_Finalize",
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}
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column_names = [
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"benchmark_type",
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"proc_num",
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"msg_size_bytes",
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"repetitions",
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"t_min_usec",
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"t_max_usec",
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"t_avg_usec",
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"mpi_datatype",
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"mpi_red_datatype",
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"mpi_red_op",
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]
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data = list()
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for file in os.listdir("data/"):
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with open("data/"+file, 'r') as f:
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lines = f.readlines()
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past_preheader = False
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in_header = False
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in_body = False
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btype = None
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proc_num = None
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mpi_datatype = None
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mpi_red_datatype = None
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mpi_red_op = None
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for line in lines:
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if data_markers["block_separator"] in line:
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if in_header and not past_preheader:
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past_preheader = True
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elif in_header and past_preheader:
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in_body = True
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in_header = not in_header
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continue
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if not in_header and not in_body and past_preheader:
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if data_markers["mpi_datatype"] in line:
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mpi_datatype = line.split()[-1]
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elif data_markers["mpi_red_datatype"] in line:
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mpi_red_datatype = line.split()[-1]
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elif data_markers["mpi_red_op"] in line:
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mpi_red_op = line.split()[-1]
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if past_preheader and in_header:
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if data_markers["benchmark_type"] in line:
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btype = line.split()[2]
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if data_markers["processes_num"] in line:
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proc_num = int(line.split()[3])
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if in_body:
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if "#" in line or "".join(line.split()) == "":
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continue
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if data_markers["end_of_table"] in line:
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break
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data.append([btype, proc_num]+[int(s) if s.isdigit()
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else float(s) for s in line.split()] + [mpi_datatype, mpi_red_datatype, mpi_red_op])
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df = pd.DataFrame(data, columns=column_names)
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df.to_csv("data.csv", index=False)
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18
templates/bench.template
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18
templates/bench.template
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#!/bin/bash -l
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#SBATCH --job-name={job_name}_{n_procs}
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#SBATCH --output={output_dir}{job_name}_{n_procs}.out
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#SBATCH --error={err_dir}{job_name}_{n_procs}.err
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#SBATCH --nodes=1
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#SBATCH --time=00:10:00
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#SBATCH --export=NONE
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unset SLURM_EXPORT_ENV
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module load intel intelmpi likwid
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unset I_MPI_PMI_LIBRARY
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export LIKWID_SILENT=1
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echo CREATION_TIME {time_stamp}
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srun --cpu-freq=2000000-2000000:performance ./likwid-mpirun -np {n_procs} -mpi intelmpi -omp intel -nperdomain M:18 {bin} {job_name} -npmin {n_procs} {off_mem_flag} > {data_dir}/{job_name}_{n_procs}.dat
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