templating algsupport

launch_alg_bench.py

@@ -0,0 +1,206 @@
+import os
+import subprocess
+from datetime import datetime
+
+################ HELPER FUNCTIONS ################
+
+
+def load_template(template_path: str):
+    output_template = ""
+    with open(template_path, "r") as handle:
+        output_template = handle.read()
+    return output_template
+
+
+def write_batch(batch_fpath: str, batch_content: str):
+    with open(batch_fpath, "w") as handle:
+        _ = handle.write(batch_content)
+
+
+################### SETUP DIRS ###################
+output_dir = os.getcwd()+"/output/"
+err_dir = os.getcwd()+"/error/"
+batch_files_dir = os.getcwd()+"/batchs/"
+data_dir = os.getcwd()+"/data/"
+
+if os.path.isdir(output_dir) == False:
+    os.mkdir(output_dir)
+if os.path.isdir(err_dir) == False:
+    os.mkdir(err_dir)
+if os.path.isdir(data_dir) == False:
+    os.mkdir(data_dir)
+if os.path.isdir(batch_files_dir) == False:
+    os.mkdir(batch_files_dir)
+
+################ GLOBAL DEFAULTS #################
+mpi1_bin = "/home/hpc/ihpc/ihpc136h/workspace/prototyping/bin/IMB-MPI1"
+default_parameter = {
+    "time_stamp": datetime.now().strftime("%y_%m_%d_%H-%M-%S"),
+    "job_name": "",
+    "output_dir": os.getcwd()+"/output/",
+    "err_dir": os.getcwd()+"/error/",
+    "data_dir": os.getcwd()+"/data/",
+    "n_procs": 18,
+    "off_cache_flag": "",
+    "bin": mpi1_bin,
+    "n_nodes": 1
+}
+
+algs_dic = [{'name': "Allgather",
+             'flag': "I_MPI_ADJUST_ALLGATHER",
+             'algs': [
+                 "Recursive doubling ",
+                 "Bruck`s ",
+                 "Ring ",
+                 "Topology aware Gatherv + Bcast ",
+                 "Knomial ",
+             ]},
+            {'name': "Allreduce",
+             'flag': "I_MPI_ADJUST_ALLREDUCE",
+             'algs': [
+                 "Recursive doubling ",
+                 "Rabenseifner`s ",
+                 "Reduce + Bcast ",
+                 "Topology aware Reduce + Bcast ",
+                 "Binomial gather + scatter ",
+                 "Topology aware binominal gather + scatter ",
+                 "Shumilin`s ring ",
+                 "Ring ",
+                 "Knomial ",
+                 "Topology aware SHM-based flat ",
+                 "Topology aware SHM-based Knomial ",
+                 "Topology aware SHM-based Knary ",
+             ]},
+
+            {'name': "Alltoall",
+             'flag': "I_MPI_ADJUST_ALLTOALL",
+             'algs': [
+                 "Bruck`s ",
+                 "Isend/Irecv + waitall ",
+                 "Pair wise exchange ",
+                 "Plum`s ",
+             ]},
+            {'name': "Barrier",
+             'flag': "I_MPI_ADJUST_BARRIER",
+             'algs': [
+                 "Dissemination ",
+                 "Recursive doubling ",
+                 "Topology aware dissemination ",
+                 "Topology aware recursive doubling ",
+                 "Binominal gather + scatter ",
+                 "Topology aware binominal gather + scatter ",
+                 "Topology aware SHM-based flat ",
+                 "Topology aware SHM-based Knomial ",
+                 "Topology aware SHM-based Knary ",
+             ]},
+            {'name': "Bcast",
+             'flag': "I_MPI_ADJUST_BCAST",
+             'algs': [
+                 "Binomial ",
+                 "Recursive doubling ",
+                 "Ring ",
+                 "Topology aware binomial ",
+                 "Topology aware recursive doubling ",
+                 "Topology aware ring ",
+                 "Shumilin`s ",
+                 "Knomial ",
+                 "Topology aware SHM-based flat ",
+                 "Topology aware SHM-based Knomial ",
+                 "Topology aware SHM-based Knary ",
+                 "NUMA aware SHM-based (SSE4.2) ",
+                 "NUMA aware SHM-based (AVX2) ",
+                 "NUMA aware SHM-based (AVX512) ",
+             ]},
+
+            {'name': "Gather",
+             'flag': "I_MPI_ADJUST_GATHER",
+             'algs': [
+                 "Binomial ",
+                 "Topology aware binomial ",
+                 "Shumilin`s ",
+                 "Binomial with segmentation ",
+             ]},
+
+            {'name': "Reduce_scatter",
+             'flag': "I_MPI_ADJUST_REDUCE_SCATTER",
+             'algs': [
+                 "Recursive halving ",
+                 "Pair wise exchange ",
+                 "Recursive doubling ",
+                 "Reduce + Scatterv ",
+                 "Topology aware Reduce + Scatterv ",
+             ]},
+
+            {'name': "Reduce",
+             'flag': "I_MPI_ADJUST_REDUCE",
+             'algs': [
+                 "Shumilin`s ",
+                 "Binomial ",
+                 "Topology aware Shumilin`s ",
+                 "Topology aware binomial ",
+                 "Rabenseifner`s ",
+                 "Topology aware Rabenseifner`s ",
+                 "Knomial ",
+                 "Topology aware SHM-based flat ",
+                 "Topology aware SHM-based Knomial ",
+                 "Topology aware SHM-based Knary ",
+                 "Topology aware SHM-based binomial ",
+             ]},
+
+            {'name': "Scatter",
+             'flag': "I_MPI_ADJUST_SCATTER",
+             'algs': [
+                 "Binomial ",
+                 "Topology aware binomial ",
+                 "Shumilin`s ",
+             ]},
+            ]
+log = ""
+
+############## MULTIPLE-NODE LAUNCH ##############
+off_cache_flags = [
+    "-off_cache -1",
+    "-off_cache 50",
+    ""
+]
+
+ndcnt = [
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10
+]
+
+proc_per_node = 72
+multiple_node_parameter = dict(default_parameter)
+multiple_node_template = load_template("./templates/multinode_algs.template")
+
+for flag in off_cache_flags:
+    multiple_node_parameter["off_cache_flag"] = flag
+    for n_nodes in ndcnt:
+        n_procs = n_nodes*proc_per_node
+        multiple_node_parameter["n_procs"] = int(n_procs)
+        multiple_node_parameter["n_nodes"] = n_nodes
+        for alg_conf in algs_dic:
+            collective = alg_conf['name']
+            multiple_node_parameter["job_name"] = collective
+            multiple_node_parameter["alg_flag"] = alg_conf['flag']
+            algs = alg_conf["algs"]
+            for idx, alg in enumerate(algs):
+                multiple_node_parameter["alg_name"] = alg
+                multiple_node_parameter["alg_idx"] = idx
+                batch_file = os.path.join(batch_files_dir,
+                                          f"{collective}_{alg.strip().replace('`','').replace(' ','_').replace('/','_')}.sh")
+                write_batch(batch_file,
+                            multiple_node_template.format(**multiple_node_parameter))
+                # result = subprocess.run(["sbatch", batch_files_dir+collective+".sh"],
+                #                         capture_output=True, text=True)
+                # log += f"#{collective} {n_procs}" + "\n"
+                # log += "\tSTDOUT:" + result.stdout + "\n"
+                # log += "\tSTDERR:" + result.stderr + "\n"
+print(log)

templates/multinode_algs.template

@@ -1,11 +1,12 @@
 #!/bin/bash -l
-#SBATCH --job-name={job_name}_{n_procs}
+#SBATCH --job-name={job_name}_{n_procs}_{alg_idx}
 #SBATCH --output={output_dir}{job_name}_{n_procs}.out
 #SBATCH --error={err_dir}{job_name}_{n_procs}.err
 #SBATCH --nodes={n_nodes}
 #SBATCH --nodelist=f01[01-64]
 #SBATCH --time=00:30:00
 #SBATCH --export=NONE
+
 # SwitchName=fswibl01 Level=0 LinkSpeed=1 Nodes=f01[01-64]
 # SwitchName=fswibl02 Level=0 LinkSpeed=1 Nodes=f02[01-64]
 # SwitchName=fswibl03 Level=0 LinkSpeed=1 Nodes=f03[01-64]
@@ -19,17 +20,19 @@
 # SwitchName=fswibl11 Level=0 LinkSpeed=1 Nodes=f08[01-64]
 # SwitchName=fswibl12 Level=0 LinkSpeed=1 Nodes=f09[01-64]
 # SwitchName=fswibl13 Level=0 LinkSpeed=1 Nodes=f10[01-64]
+
 unset SLURM_EXPORT_ENV
 
 module load intel intelmpi 
 
-export I_MPI_ADJUST_{capital_jobname}={algnumber}
+export {alg_flag}={alg_idx}
 
 OUTPUT_FILENAME="{data_dir}/{job_name}_$SLURM_JOB_ID.dat"
 
 echo  "# CREATION_TIME : {time_stamp}" > $OUTPUT_FILENAME
 echo  "# N_NODES       : {n_nodes}" >> $OUTPUT_FILENAME
 echo  "# OFF_CACHE_FLAG  : {off_cache_flag}">> $OUTPUT_FILENAME
+echo  "# ALGORITHM  : {alg_name}">> $OUTPUT_FILENAME
 
 srun --cpu-freq=2000000-2000000:performance -N {n_nodes} -n{n_procs} {bin} {job_name} -npmin {n_procs} {off_cache_flag} -mem 2 -time 60 >> $OUTPUT_FILENAME