templating algsupport
This commit is contained in:
Erik Fabrizzi
206
launch_alg_bench.py
Executable file
206
launch_alg_bench.py
Executable file
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import os
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import subprocess
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from datetime import datetime
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################ HELPER FUNCTIONS ################
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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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################### SETUP DIRS ###################
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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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################ GLOBAL DEFAULTS #################
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mpi1_bin = "/home/hpc/ihpc/ihpc136h/workspace/prototyping/bin/IMB-MPI1"
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default_parameter = {
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"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_cache_flag": "",
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"bin": mpi1_bin,
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"n_nodes": 1
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}
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algs_dic = [{'name': "Allgather",
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'flag': "I_MPI_ADJUST_ALLGATHER",
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'algs': [
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"Recursive doubling ",
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"Bruck`s ",
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"Ring ",
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"Topology aware Gatherv + Bcast ",
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"Knomial ",
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]},
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{'name': "Allreduce",
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'flag': "I_MPI_ADJUST_ALLREDUCE",
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'algs': [
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"Recursive doubling ",
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"Rabenseifner`s ",
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"Reduce + Bcast ",
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"Topology aware Reduce + Bcast ",
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"Binomial gather + scatter ",
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"Topology aware binominal gather + scatter ",
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"Shumilin`s ring ",
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"Ring ",
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"Knomial ",
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"Topology aware SHM-based flat ",
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"Topology aware SHM-based Knomial ",
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"Topology aware SHM-based Knary ",
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]},
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{'name': "Alltoall",
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'flag': "I_MPI_ADJUST_ALLTOALL",
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'algs': [
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"Bruck`s ",
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"Isend/Irecv + waitall ",
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"Pair wise exchange ",
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"Plum`s ",
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]},
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{'name': "Barrier",
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'flag': "I_MPI_ADJUST_BARRIER",
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'algs': [
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"Dissemination ",
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"Recursive doubling ",
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"Topology aware dissemination ",
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"Topology aware recursive doubling ",
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"Binominal gather + scatter ",
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"Topology aware binominal gather + scatter ",
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"Topology aware SHM-based flat ",
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"Topology aware SHM-based Knomial ",
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"Topology aware SHM-based Knary ",
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]},
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{'name': "Bcast",
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'flag': "I_MPI_ADJUST_BCAST",
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'algs': [
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"Binomial ",
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"Recursive doubling ",
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"Ring ",
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"Topology aware binomial ",
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"Topology aware recursive doubling ",
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"Topology aware ring ",
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"Shumilin`s ",
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"Knomial ",
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"Topology aware SHM-based flat ",
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"Topology aware SHM-based Knomial ",
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"Topology aware SHM-based Knary ",
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"NUMA aware SHM-based (SSE4.2) ",
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"NUMA aware SHM-based (AVX2) ",
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"NUMA aware SHM-based (AVX512) ",
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]},
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{'name': "Gather",
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'flag': "I_MPI_ADJUST_GATHER",
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'algs': [
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"Binomial ",
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"Topology aware binomial ",
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"Shumilin`s ",
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"Binomial with segmentation ",
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]},
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{'name': "Reduce_scatter",
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'flag': "I_MPI_ADJUST_REDUCE_SCATTER",
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'algs': [
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"Recursive halving ",
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"Pair wise exchange ",
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"Recursive doubling ",
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"Reduce + Scatterv ",
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"Topology aware Reduce + Scatterv ",
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]},
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{'name': "Reduce",
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'flag': "I_MPI_ADJUST_REDUCE",
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'algs': [
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"Shumilin`s ",
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"Binomial ",
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"Topology aware Shumilin`s ",
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"Topology aware binomial ",
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"Rabenseifner`s ",
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"Topology aware Rabenseifner`s ",
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"Knomial ",
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"Topology aware SHM-based flat ",
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"Topology aware SHM-based Knomial ",
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"Topology aware SHM-based Knary ",
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"Topology aware SHM-based binomial ",
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]},
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{'name': "Scatter",
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'flag': "I_MPI_ADJUST_SCATTER",
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'algs': [
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"Binomial ",
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"Topology aware binomial ",
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"Shumilin`s ",
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]},
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]
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log = ""
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############## MULTIPLE-NODE LAUNCH ##############
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off_cache_flags = [
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"-off_cache -1",
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"-off_cache 50",
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""
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]
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ndcnt = [
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2,
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3,
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4,
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5,
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6,
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7,
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8,
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9,
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10
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]
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proc_per_node = 72
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multiple_node_parameter = dict(default_parameter)
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multiple_node_template = load_template("./templates/multinode_algs.template")
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for flag in off_cache_flags:
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multiple_node_parameter["off_cache_flag"] = flag
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for n_nodes in ndcnt:
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n_procs = n_nodes*proc_per_node
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multiple_node_parameter["n_procs"] = int(n_procs)
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multiple_node_parameter["n_nodes"] = n_nodes
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for alg_conf in algs_dic:
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collective = alg_conf['name']
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multiple_node_parameter["job_name"] = collective
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multiple_node_parameter["alg_flag"] = alg_conf['flag']
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algs = alg_conf["algs"]
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for idx, alg in enumerate(algs):
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multiple_node_parameter["alg_name"] = alg
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multiple_node_parameter["alg_idx"] = idx
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batch_file = os.path.join(batch_files_dir,
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f"{collective}_{alg.strip().replace('`','').replace(' ','_').replace('/','_')}.sh")
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write_batch(batch_file,
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multiple_node_template.format(**multiple_node_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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