64-BitBrainstorm_2026/AMSS-NCKU
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Enable multi-threaded MKL for better resource utilization

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- Changed from libmkl_sequential to libmkl_intel_thread
- Added automatic MKL thread count configuration (104 cores / MPI_processes)
- Updated runtime scripts to set MKL_NUM_THREADS environment variable
- Added comprehensive optimization documentation

Expected improvement: 5-15% from better MKL utilization
Note: Main performance bottleneck is in computation loops, not MKL functions
This commit is contained in:
CGH0S7
2026-01-19 09:31:29 +08:00
parent 9deeda9831
commit c524228d23
2 changed files with 28 additions and 12 deletions
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32
makefile_and_run.py
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@@ -22,6 +22,13 @@ NUMACTL_CPU_BIND = "taskset -c 4-55,60-111"
## Set make -j to utilize available cores for faster builds
BUILD_JOBS = 104
## MKL threading configuration for hybrid MPI+threaded-MKL execution
## Total cores available: 104 (cores 4-55, 60-111)
## MPI processes: configured in AMSS_NCKU_Input.py (typically 48)
## MKL threads per process: 104 / MPI_processes ≈ 2
## This ensures full utilization of available cores
MKL_NUM_THREADS = max(1, 104 // input_data.MPI_processes)
##################################################################
@@ -110,18 +117,24 @@ def makefile_TwoPunctureABE():
def run_ABE():
print( )
print( " Running the AMSS-NCKU executable file ABE/ABEGPU " )
print( " Running the AMSS-NCKU executable file ABE/ABEGPU " )
print( )
print( f" MPI processes: {input_data.MPI_processes}" )
print( f" MKL threads per process: {MKL_NUM_THREADS}" )
print( f" Total threads: {input_data.MPI_processes * MKL_NUM_THREADS}" )
print( )
## Define the command to run; cast other values to strings as needed
## Set MKL threading environment variables for optimal performance
env_vars = f"export MKL_NUM_THREADS={MKL_NUM_THREADS} && export MKL_DYNAMIC=FALSE && "
if (input_data.GPU_Calculation == "no"):
mpi_command = NUMACTL_CPU_BIND + " mpirun -np " + str(input_data.MPI_processes) + " ./ABE"
mpi_command = env_vars + NUMACTL_CPU_BIND + " 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 + " mpirun -np " + str(input_data.MPI_processes) + " ./ABEGPU"
mpi_command = env_vars + NUMACTL_CPU_BIND + " mpirun -np " + str(input_data.MPI_processes) + " ./ABEGPU"
mpi_command_outfile = "ABEGPU_out.log"
## Execute the MPI command and stream output
mpi_process = subprocess.Popen(mpi_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
@@ -154,11 +167,12 @@ def run_ABE():
def run_TwoPunctureABE():
print( )
print( " Running the AMSS-NCKU executable file TwoPunctureABE " )
print( " Running the AMSS-NCKU executable file TwoPunctureABE " )
print( )
## Define the command to run
TwoPuncture_command = NUMACTL_CPU_BIND + " ./TwoPunctureABE"
## Define the command to run with MKL threading configuration
env_vars = f"export MKL_NUM_THREADS={MKL_NUM_THREADS} && export MKL_DYNAMIC=FALSE && "
TwoPuncture_command = env_vars + NUMACTL_CPU_BIND + " ./TwoPunctureABE"
TwoPuncture_command_outfile = "TwoPunctureABE_out.log"
## Execute the command with subprocess.Popen and stream output