64-BitBrainstorm_2026/AMSS-NCKU
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AMSS-NCKU/makefile_and_run.py

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##################################################################
##
## This file defines the commands used to build and run AMSS-NCKU
## Author: Xiaoqu
## 2025/01/24
##
##################################################################
import AMSS_NCKU_Input as input_data
import os
import shutil
import subprocess
import time
def get_last_n_cores_per_socket(n=32):
"""
Read CPU topology via lscpu and return a taskset -c string
selecting the last `n` cores of each NUMA node (socket).
Example: 2 sockets x 56 cores each, n=32 -> node0: 24-55, node1: 80-111
-> "taskset -c 24-55,80-111"
"""
result = subprocess.run(["lscpu", "--parse=NODE,CPU"], capture_output=True, text=True)
# Build a dict: node_id -> sorted list of CPU ids
node_cpus = {}
for line in result.stdout.splitlines():
if line.startswith("#") or not line.strip():
continue
parts = line.split(",")
if len(parts) < 2:
continue
node_id, cpu_id = int(parts[0]), int(parts[1])
node_cpus.setdefault(node_id, []).append(cpu_id)
segments = []
for node_id in sorted(node_cpus):
cpus = sorted(node_cpus[node_id])
selected = cpus[-n:] # last n cores of this socket
segments.append(f"{selected[0]}-{selected[-1]}")
cpu_str = ",".join(segments)
total = len(segments) * n
print(f" CPU binding: taskset -c {cpu_str} ({total} cores, last {n} per socket)")
#return f"taskset -c {cpu_str}"
return f""
## CPU core binding: dynamically select the last 32 cores of each socket (64 cores total)
NUMACTL_CPU_BIND = get_last_n_cores_per_socket(n=32)
## Build parallelism: match the number of bound cores
BUILD_JOBS = 64
##################################################################
def _truthy(value, default=False):
if value is None:
return default
if isinstance(value, bool):
return value
text = str(value).strip().lower()
if text == "":
return default
return text in ("1", "yes", "y", "true", "on", "enable", "enabled")
def _input_or_env(input_name, env_name, default=None):
if env_name in os.environ:
return os.environ[env_name]
return getattr(input_data, input_name, default)
def _start_cuda_mps_if_requested(runtime_env):
if input_data.GPU_Calculation != "yes":
return False
default_auto_mps = int(getattr(input_data, "MPI_processes", 1)) > 1
auto_mps = _truthy(
_input_or_env("CUDA_Auto_MPS", "AMSS_CUDA_AUTO_MPS", default_auto_mps),
default=default_auto_mps,
)
if not auto_mps:
return False
mps_control = shutil.which("nvidia-cuda-mps-control")
if not mps_control:
print(" CUDA MPS control command was not found; running without MPS.")
return False
uid = os.getuid()
pipe_dir = str(_input_or_env("CUDA_MPS_PIPE_DIRECTORY", "CUDA_MPS_PIPE_DIRECTORY",
f"/tmp/amss-ncku-mps-{uid}"))
log_dir = str(_input_or_env("CUDA_MPS_LOG_DIRECTORY", "CUDA_MPS_LOG_DIRECTORY",
f"/tmp/amss-ncku-mps-log-{uid}"))
os.makedirs(pipe_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
mps_env = runtime_env.copy()
mps_env["CUDA_MPS_PIPE_DIRECTORY"] = pipe_dir
mps_env["CUDA_MPS_LOG_DIRECTORY"] = log_dir
if os.path.exists(os.path.join(pipe_dir, "control")):
runtime_env.update({
"CUDA_MPS_PIPE_DIRECTORY": pipe_dir,
"CUDA_MPS_LOG_DIRECTORY": log_dir,
})
print(f" Reusing CUDA MPS daemon: {pipe_dir}")
return False
print(f" Starting CUDA MPS daemon for this run: {pipe_dir}")
result = subprocess.run([mps_control, "-d"], env=mps_env, text=True,
stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
if result.returncode != 0:
print(" CUDA MPS daemon did not start; running without MPS.")
if result.stdout:
print(result.stdout, end="")
return False
runtime_env.update({
"CUDA_MPS_PIPE_DIRECTORY": pipe_dir,
"CUDA_MPS_LOG_DIRECTORY": log_dir,
})
return True
def _stop_cuda_mps(runtime_env):
mps_control = shutil.which("nvidia-cuda-mps-control")
if not mps_control:
return
subprocess.run([mps_control], input="quit\n", env=runtime_env, text=True,
stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
def _gpu_runtime_env():
runtime_env = os.environ.copy()
finite_difference = str(getattr(input_data, "Finite_Diffenence_Method", "4th-order")).strip()
defaults = {
"AMSS_EVOLVE_TIMING": "1",
"AMSS_ESCALAR_STEP_TIMING": "0",
"AMSS_INTERP_FAST": "1",
"AMSS_INTERP_GPU": "1",
"AMSS_ANALYSIS_MAP_EVERY": "1000000",
"AMSS_CUDA_AWARE_MPI": "1",
"AMSS_CUDA_KEEP_RESIDENT_AFTER_STEP": "1",
"AMSS_CUDA_KEEP_ALL_LEVELS": "1",
"AMSS_CUDA_ESCALAR_KEEP_RESIDENT_AFTER_STEP": "1",
"AMSS_CUDA_ESCALAR_KEEP_ALL_LEVELS": "1",
"AMSS_CUDA_EM_CACHE_SOURCES": "1",
"AMSS_CUDA_EM_ZERO_FASTPATH": "1",
"AMSS_EM_ZERO_ANALYSIS_FASTPATH": "1",
"AMSS_EM_ZERO_RESIDENT_DOWNLOAD_FASTPATH": "1",
"AMSS_CUDA_AMR_HOST_STAGED": "1",
"AMSS_CUDA_AMR_RESTRICT_DEVICE": "0",
"AMSS_CUDA_AMR_RESTRICT_BATCH": "0",
"AMSS_CUDA_DEVICE_SEGMENT_BATCH": "0",
"AMSS_CUDA_UNCACHED_DEVICE_BUFFERS": "1",
"AMSS_SHELL_FAST_INTERP": "0",
"AMSS_SHELL_PARALLEL_INTERP": "0",
"AMSS_SHELL_CUDA_INTERP": "0",
}
if finite_difference in ("2nd-order", "8th-order"):
defaults.update({
"AMSS_INTERP_FAST": "0",
"AMSS_INTERP_GPU": "0",
"AMSS_CUDA_AWARE_MPI": "0",
})
if finite_difference == "8th-order" and getattr(input_data, "Equation_Class", "") == "BSSN-EM":
defaults.update({
"AMSS_CUDA_AMR_RESTRICT_DEVICE": "1",
"AMSS_CUDA_AMR_RESTRICT_BATCH": "1",
"AMSS_CUDA_DEVICE_SEGMENT_BATCH": "1",
})
if getattr(input_data, "basic_grid_set", "") == "Shell-Patch":
defaults.update({
"AMSS_CUDA_AWARE_MPI": "0",
"AMSS_SHELL_FAST_INTERP": "1",
"AMSS_SHELL_PARALLEL_INTERP": "1",
"AMSS_SHELL_INTERP_THREADS": "16",
})
if getattr(input_data, "Equation_Class", "") in ("BSSN", "BSSN-EScalar", "Z4C"):
defaults["AMSS_CUDA_AMR_RESTRICT_DEVICE"] = "1"
if getattr(input_data, "Equation_Class", "") == "Z4C":
defaults.update({
"AMSS_Z4C_CUDA_RESIDENT": "1",
"AMSS_CONSTRAINT_OUT_EVERY": "1000000",
})
for key, value in defaults.items():
runtime_env.setdefault(key, value)
optional_overrides = {
"AMSS_INTERP_FAST_COMPARE": "AMSS_Interp_Fast_Compare",
"AMSS_INTERP_FAST_COMPARE_LIMIT": "AMSS_Interp_Fast_Compare_Limit",
"AMSS_INTERP_FAST_COMPARE_TOL": "AMSS_Interp_Fast_Compare_Tol",
"AMSS_GPU_STAGE_TIMING": "AMSS_GPU_Stage_Timing",
"AMSS_GPU_STAGE_TIMING_EVERY": "AMSS_GPU_Stage_Timing_Every",
}
for env_name, input_name in optional_overrides.items():
if env_name not in runtime_env and hasattr(input_data, input_name):
runtime_env[env_name] = str(getattr(input_data, input_name))
return runtime_env
##################################################################
##################################################################
## Compile the AMSS-NCKU main program ABE
def makefile_ABE():
print( )
print( " Compiling the AMSS-NCKU executable file ABE/ABEGPU " )
print( )
## Build command with CPU binding to nohz_full cores
if (input_data.GPU_Calculation == "no"):
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} INTERP_LB_MODE=off USE_CUDA_BSSN=0 USE_CUDA_Z4C=0 ABE"
elif (input_data.GPU_Calculation == "yes"):
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} INTERP_LB_MODE=off USE_CUDA_BSSN=1 USE_CUDA_Z4C=1 ABE_CUDA"
else:
print( " CPU/GPU numerical calculation setting is wrong " )
print( )
## Execute the command with subprocess.Popen and stream output
makefile_process = subprocess.Popen(makefile_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
## Read and print output lines as they arrive
for line in makefile_process.stdout:
print(line, end='') # stream output in real time
## Wait for the process to finish
makefile_return_code = makefile_process.wait()
if makefile_return_code != 0:
raise subprocess.CalledProcessError(makefile_return_code, makefile_command)
print( )
print( " Compilation of the AMSS-NCKU executable file ABE is finished " )
print( )
return
##################################################################
##################################################################
## Compile the AMSS-NCKU TwoPuncture program TwoPunctureABE
def makefile_TwoPunctureABE():
print( )
print( " Compiling the AMSS-NCKU executable file TwoPunctureABE " )
print( )
## Build command with CPU binding to nohz_full cores
makefile_command = f"{NUMACTL_CPU_BIND} make -j{BUILD_JOBS} TwoPunctureABE"
## Execute the command with subprocess.Popen and stream output
makefile_process = subprocess.Popen(makefile_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
## Read and print output lines as they arrive
for line in makefile_process.stdout:
print(line, end='') # stream output in real time
## Wait for the process to finish
makefile_return_code = makefile_process.wait()
if makefile_return_code != 0:
raise subprocess.CalledProcessError(makefile_return_code, makefile_command)
print( )
print( " Compilation of the AMSS-NCKU executable file TwoPunctureABE is finished " )
print( )
return
##################################################################
##################################################################
## Run the AMSS-NCKU main program ABE
def run_ABE():
print( )
print( " Running the AMSS-NCKU executable file ABE/ABEGPU " )
print( )
## Define the command to run; cast other values to strings as needed
mpi_env = None
started_mps = False
mpi_processes = int(input_data.MPI_processes)
if (input_data.GPU_Calculation == "yes" and
getattr(input_data, "Equation_Class", "") == "Z4C"):
z4c_env_np = os.environ.get("AMSS_Z4C_GPU_MPI_PROCESSES")
if z4c_env_np and int(z4c_env_np) > 0:
mpi_processes = int(z4c_env_np)
elif mpi_processes < 4:
mpi_processes = 4
if (input_data.GPU_Calculation == "yes" and
getattr(input_data, "basic_grid_set", "") == "Shell-Patch"):
shell_env_np = os.environ.get("AMSS_SHELL_GPU_MPI_PROCESSES")
if shell_env_np and int(shell_env_np) > 0:
mpi_processes = int(shell_env_np)
elif mpi_processes < 4:
mpi_processes = 4
if (input_data.GPU_Calculation == "no"):
mpi_command = NUMACTL_CPU_BIND + " mpirun -np " + str(mpi_processes) + " ./ABE"
#mpi_command = " mpirun -np " + str(input_data.MPI_processes) + " ./ABE"
mpi_command_outfile = "ABE_out.log"
elif (input_data.GPU_Calculation == "yes"):
mpi_command = NUMACTL_CPU_BIND + " I_MPI_OFFLOAD=1 I_MPI_OFFLOAD_IPC=0 mpirun -np " + str(mpi_processes) + " ./ABE_CUDA"
mpi_command_outfile = "ABEGPU_out.log"
mpi_env = _gpu_runtime_env()
started_mps = _start_cuda_mps_if_requested(mpi_env)
print(" GPU optimized runtime switches:")
print(f" MPI processes={mpi_processes}")
print(f" AMSS_INTERP_FAST={mpi_env.get('AMSS_INTERP_FAST', '')}")
print(f" AMSS_INTERP_GPU={mpi_env.get('AMSS_INTERP_GPU', '')}")
print(f" AMSS_ANALYSIS_MAP_EVERY={mpi_env.get('AMSS_ANALYSIS_MAP_EVERY', '')}")
print(f" AMSS_EVOLVE_TIMING={mpi_env.get('AMSS_EVOLVE_TIMING', '')}")
print(f" AMSS_ESCALAR_STEP_TIMING={mpi_env.get('AMSS_ESCALAR_STEP_TIMING', '')}")
print(f" AMSS_CUDA_AWARE_MPI={mpi_env.get('AMSS_CUDA_AWARE_MPI', '')}")
print(f" AMSS_CUDA_KEEP_RESIDENT_AFTER_STEP={mpi_env.get('AMSS_CUDA_KEEP_RESIDENT_AFTER_STEP', '')}")
print(f" AMSS_CUDA_KEEP_ALL_LEVELS={mpi_env.get('AMSS_CUDA_KEEP_ALL_LEVELS', '')}")
print(f" AMSS_CUDA_ESCALAR_KEEP_RESIDENT_AFTER_STEP={mpi_env.get('AMSS_CUDA_ESCALAR_KEEP_RESIDENT_AFTER_STEP', '')}")
print(f" AMSS_CUDA_ESCALAR_KEEP_ALL_LEVELS={mpi_env.get('AMSS_CUDA_ESCALAR_KEEP_ALL_LEVELS', '')}")
print(f" AMSS_CUDA_EM_CACHE_SOURCES={mpi_env.get('AMSS_CUDA_EM_CACHE_SOURCES', '')}")
print(f" AMSS_CUDA_EM_ZERO_FASTPATH={mpi_env.get('AMSS_CUDA_EM_ZERO_FASTPATH', '')}")
print(f" AMSS_EM_ZERO_ANALYSIS_FASTPATH={mpi_env.get('AMSS_EM_ZERO_ANALYSIS_FASTPATH', '')}")
print(f" AMSS_EM_ZERO_RESIDENT_DOWNLOAD_FASTPATH={mpi_env.get('AMSS_EM_ZERO_RESIDENT_DOWNLOAD_FASTPATH', '')}")
print(f" AMSS_CUDA_AMR_HOST_STAGED={mpi_env.get('AMSS_CUDA_AMR_HOST_STAGED', '')}")
print(f" AMSS_CUDA_AMR_RESTRICT_DEVICE={mpi_env.get('AMSS_CUDA_AMR_RESTRICT_DEVICE', '')}")
print(f" AMSS_CUDA_AMR_RESTRICT_BATCH={mpi_env.get('AMSS_CUDA_AMR_RESTRICT_BATCH', '')}")
print(f" AMSS_CUDA_DEVICE_SEGMENT_BATCH={mpi_env.get('AMSS_CUDA_DEVICE_SEGMENT_BATCH', '')}")
print(f" AMSS_CUDA_UNCACHED_DEVICE_BUFFERS={mpi_env.get('AMSS_CUDA_UNCACHED_DEVICE_BUFFERS', '')}")
print(f" AMSS_SHELL_FAST_INTERP={mpi_env.get('AMSS_SHELL_FAST_INTERP', '')}")
print(f" AMSS_SHELL_PARALLEL_INTERP={mpi_env.get('AMSS_SHELL_PARALLEL_INTERP', '')}")
print(f" AMSS_SHELL_CUDA_INTERP={mpi_env.get('AMSS_SHELL_CUDA_INTERP', '')}")
print(f" AMSS_SHELL_INTERP_THREADS={mpi_env.get('AMSS_SHELL_INTERP_THREADS', '')}")
print(f" AMSS_Z4C_CUDA_RESIDENT={mpi_env.get('AMSS_Z4C_CUDA_RESIDENT', '')}")
print(f" AMSS_CONSTRAINT_OUT_EVERY={mpi_env.get('AMSS_CONSTRAINT_OUT_EVERY', '')}")
if "CUDA_MPS_PIPE_DIRECTORY" in mpi_env:
print(f" CUDA_MPS_PIPE_DIRECTORY={mpi_env['CUDA_MPS_PIPE_DIRECTORY']}")
try:
## Execute the MPI command and stream output
mpi_process = subprocess.Popen(mpi_command, shell=True, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, text=True, env=mpi_env)
## Write ABE run output to file while printing to stdout
with open(mpi_command_outfile, 'w') as file0:
## Read and print output lines; also write each line to file
for line in mpi_process.stdout:
print(line, end='') # stream output in real time
file0.write(line) # write the line to file
file0.flush() # flush to ensure each line is written immediately (optional)
## Wait for the process to finish
mpi_return_code = mpi_process.wait()
if mpi_return_code != 0:
raise subprocess.CalledProcessError(mpi_return_code, mpi_command)
finally:
if started_mps:
_stop_cuda_mps(mpi_env)
print( )
print( " The ABE/ABEGPU simulation is finished " )
print( )
return
##################################################################
##################################################################
## Run the AMSS-NCKU TwoPuncture program TwoPunctureABE
def run_TwoPunctureABE():
tp_time1=time.time()
print( )
print( " Running the AMSS-NCKU executable file TwoPunctureABE " )
print( )
## Define the command to run
#TwoPuncture_command = NUMACTL_CPU_BIND + " ./TwoPunctureABE"
TwoPuncture_command = " ./TwoPunctureABE"
TwoPuncture_command_outfile = "TwoPunctureABE_out.log"
## Execute the command with subprocess.Popen and stream output
TwoPuncture_process = subprocess.Popen(TwoPuncture_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
## Write TwoPunctureABE run output to file while printing to stdout
with open(TwoPuncture_command_outfile, 'w') as file0:
## Read and print output lines; also write each line to file
for line in TwoPuncture_process.stdout:
print(line, end='') # stream output in real time
file0.write(line) # write the line to file
file0.flush() # flush to ensure each line is written immediately (optional)
file0.close()
## Wait for the process to finish
TwoPuncture_command_return_code = TwoPuncture_process.wait()
print( )
print( " The TwoPunctureABE simulation is finished " )
print( )
tp_time2=time.time()
et=tp_time2-tp_time1
print(f"Used time: {et}")
return
##################################################################
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