64-BitBrainstorm_2026/AMSS-NCKU
8
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Refactor verification method and optimize numerical kernels with oneMKL BLAS

Browse Source 
This commit transitions the verification approach from post-Newtonian theory
   comparison to regression testing against baseline simulations, and optimizes
   critical numerical kernels using Intel oneMKL BLAS routines.

   Verification Changes:
   - Replace PN theory-based RMS calculation with trajectory-based comparison
   - Compare optimized results against baseline (GW150914-origin) on XY plane
   - Compute RMS independently for BH1 and BH2, report maximum as final metric
   - Update documentation to reflect new regression test methodology

   Performance Optimizations:
   - Replace manual vector operations with oneMKL BLAS routines:
     * norm2() and scalarproduct() now use cblas_dnrm2/cblas_ddot (C++)
     * L2 norm calculations use DDOT for dot products (Fortran)
     * Interpolation weighted sums use DDOT (Fortran)
   - Disable OpenMP threading (switch to sequential MKL) for better performance

   Build Configuration:
   - Switch from lmkl_intel_thread to lmkl_sequential
   - Remove -qopenmp flags from compiler options
   - Maintain aggressive optimization flags (-O3, -xHost, -fp-model fast=2, -fma)

   Other Changes:
   - Update .gitignore for GW150914-origin, docs, and temporary files
This commit is contained in:
CGH0S7
2026-01-18 14:25:21 +08:00
parent 3a7bce3af2
commit 9deeda9831
5 changed files with 170 additions and 99 deletions
Download Patch File Download Diff File Expand all files Collapse all files

148
AMSS_NCKU_Verify_ASC26.py
View File

@@ -1,13 +1,19 @@
#!/usr/bin/env python3
"""
AMSS-NCKU GW150914 Simulation Accuracy Verification Script
AMSS-NCKU GW150914 Simulation Regression Test Script
Verification Requirements:
1. RMS error < 1% (Black hole trajectory vs. post-Newtonian theory)
1. XY-plane trajectory RMS error < 1% (Optimized vs. baseline, max of BH1 and BH2)
2. ADM constraint violation < 2 (Grid Level 0)
Usage: python3 verify_accuracy.py [output_dir]
RMS Calculation Method:
- Computes trajectory deviation on the XY plane independently for BH1 and BH2
- For each black hole: RMS = sqrt((1/M) * sum((Δr_i / r_i^max)^2)) × 100%
- Final RMS = max(RMS_BH1, RMS_BH2)
Usage: python3 AMSS_NCKU_Verify_ASC26.py [output_dir]
Default: output_dir = GW150914/AMSS_NCKU_output
Reference: GW150914-origin (baseline simulation)
"""
import numpy as np
@@ -52,51 +58,77 @@ def load_constraint_data(filepath):
return np.array(data)
def calculate_rms_error(bh_data, M1=0.5538461539, M2=0.4461538472):
def calculate_rms_error(bh_data_ref, bh_data_target):
"""
Calculate RMS error
Compare numerical orbit with post-Newtonian (PN) theory prediction
Calculate trajectory-based RMS error on the XY plane between baseline and optimized simulations.
This function computes the RMS error independently for BH1 and BH2 trajectories,
then returns the maximum of the two as the final RMS error metric.
For each black hole, the RMS is calculated as:
RMS = sqrt( (1/M) * sum( (Δr_i / r_i^max)^2 ) ) × 100%
where:
Δr_i = sqrt((x_ref,i - x_new,i)^2 + (y_ref,i - y_new,i)^2)
r_i^max = max(sqrt(x_ref,i^2 + y_ref,i^2), sqrt(x_new,i^2 + y_new,i^2))
Args:
bh_data_ref: Reference (baseline) trajectory data
bh_data_target: Target (optimized) trajectory data
Returns:
rms_value: Final RMS error as a percentage (max of BH1 and BH2)
error: Error message if any
"""
time = bh_data['time']
x1, y1, z1 = bh_data['x1'], bh_data['y1'], bh_data['z1']
x2, y2, z2 = bh_data['x2'], bh_data['y2'], bh_data['z2']
# Align data: truncate to the length of the shorter dataset
M = min(len(bh_data_ref['time']), len(bh_data_target['time']))
# Calculate separation distance
r_num = np.sqrt((x2-x1)**2 + (y2-y1)**2 + (z2-z1)**2)
if M < 10:
return None, "Insufficient data points for comparison"
# Mass parameters
M_total = M1 + M2
eta = M1 * M2 / M_total**2
# Extract XY coordinates for both black holes
x1_ref = bh_data_ref['x1'][:M]
y1_ref = bh_data_ref['y1'][:M]
x2_ref = bh_data_ref['x2'][:M]
y2_ref = bh_data_ref['y2'][:M]
# Use inspiral phase data (r > 2M)
inspiral_mask = r_num > 2.0
t_insp = time[inspiral_mask]
r_insp = r_num[inspiral_mask]
x1_new = bh_data_target['x1'][:M]
y1_new = bh_data_target['y1'][:M]
x2_new = bh_data_target['x2'][:M]
y2_new = bh_data_target['y2'][:M]
if len(t_insp) < 10:
return None, None, "Insufficient data points"
# Calculate RMS for BH1
delta_r1 = np.sqrt((x1_ref - x1_new)**2 + (y1_ref - y1_new)**2)
r1_ref = np.sqrt(x1_ref**2 + y1_ref**2)
r1_new = np.sqrt(x1_new**2 + y1_new**2)
r1_max = np.maximum(r1_ref, r1_new)
# Post-Newtonian theory prediction
r0 = r_insp[0]
t0 = t_insp[0]
t_coal_PN = (5.0/256.0) * (r0**4) / (eta * M_total**3)
# Calculate RMS for BH2
delta_r2 = np.sqrt((x2_ref - x2_new)**2 + (y2_ref - y2_new)**2)
r2_ref = np.sqrt(x2_ref**2 + y2_ref**2)
r2_new = np.sqrt(x2_new**2 + y2_new**2)
r2_max = np.maximum(r2_ref, r2_new)
# Calculate PN predicted separation distance
tau = 1.0 - (t_insp - t0) / t_coal_PN
tau = np.maximum(tau, 1e-10) # Avoid negative values
r_PN = r0 * np.power(tau, 0.25)
# Avoid division by zero for BH1
valid_mask1 = r1_max > 1e-15
if np.sum(valid_mask1) < 10:
return None, "Insufficient valid data points for BH1"
# Use only valid data (t < 0.95 * t_coal)
valid_mask = (t_insp - t0) < t_coal_PN * 0.95
r_num_valid = r_insp[valid_mask]
r_PN_valid = r_PN[valid_mask]
terms1 = (delta_r1[valid_mask1] / r1_max[valid_mask1])**2
rms_bh1 = np.sqrt(np.mean(terms1)) * 100
# Calculate RMS error
residual = r_num_valid - r_PN_valid
rms_abs = np.sqrt(np.mean(residual**2))
rms_rel = rms_abs / np.mean(r_num_valid) * 100
# Avoid division by zero for BH2
valid_mask2 = r2_max > 1e-15
if np.sum(valid_mask2) < 10:
return None, "Insufficient valid data points for BH2"
return rms_abs, rms_rel, None
terms2 = (delta_r2[valid_mask2] / r2_max[valid_mask2])**2
rms_bh2 = np.sqrt(np.mean(terms2)) * 100
# Final RMS is the maximum of BH1 and BH2
rms_final = max(rms_bh1, rms_bh2)
return rms_final, None
def analyze_constraint_violation(constraint_data, n_levels=9):
@@ -125,13 +157,13 @@ def analyze_constraint_violation(constraint_data, n_levels=9):
def print_header():
"""Print report header"""
print("\n" + Color.BLUE + Color.BOLD + "=" * 65 + Color.RESET)
print(Color.BOLD + " AMSS-NCKU GW150914 Simulation Accuracy Verification Report" + Color.RESET)
print(Color.BOLD + " AMSS-NCKU GW150914 Simulation Regression Test Report" + Color.RESET)
print(Color.BLUE + Color.BOLD + "=" * 65 + Color.RESET)
def print_rms_results(rms_abs, rms_rel, error, threshold=1.0):
def print_rms_results(rms_rel, error, threshold=1.0):
"""Print RMS error results"""
print(f"\n{Color.BOLD}1. RMS Error Analysis (Black Hole Trajectory){Color.RESET}")
print(f"\n{Color.BOLD}1. RMS Error Analysis (Baseline vs Optimized){Color.RESET}")
print("-" * 45)
if error:
@@ -139,8 +171,7 @@ def print_rms_results(rms_abs, rms_rel, error, threshold=1.0):
return False
passed = rms_rel < threshold
print(f" RMS absolute error: {rms_abs:.4e} M")
print(f" RMS relative error: {rms_rel:.4f}%")
print(f" Requirement: < {threshold}%")
print(f" Status: {get_status_text(passed)}")
@@ -190,20 +221,29 @@ def print_summary(rms_passed, constraint_passed):
def main():
# Determine output directory
# Determine target (optimized) output directory
if len(sys.argv) > 1:
output_dir = sys.argv[1]
target_dir = sys.argv[1]
else:
script_dir = os.path.dirname(os.path.abspath(__file__))
output_dir = os.path.join(script_dir, "GW150914/AMSS_NCKU_output")
target_dir = os.path.join(script_dir, "GW150914/AMSS_NCKU_output")
# Determine reference (baseline) directory
script_dir = os.path.dirname(os.path.abspath(__file__))
reference_dir = os.path.join(script_dir, "GW150914-origin/AMSS_NCKU_output")
# Data file paths
bh_file = os.path.join(output_dir, "bssn_BH.dat")
constraint_file = os.path.join(output_dir, "bssn_constraint.dat")
bh_file_ref = os.path.join(reference_dir, "bssn_BH.dat")
bh_file_target = os.path.join(target_dir, "bssn_BH.dat")
constraint_file = os.path.join(target_dir, "bssn_constraint.dat")
# Check if files exist
if not os.path.exists(bh_file):
print(f"{Color.RED}{Color.BOLD}Error:{Color.RESET} Black hole trajectory file not found: {bh_file}")
if not os.path.exists(bh_file_ref):
print(f"{Color.RED}{Color.BOLD}Error:{Color.RESET} Baseline trajectory file not found: {bh_file_ref}")
sys.exit(1)
if not os.path.exists(bh_file_target):
print(f"{Color.RED}{Color.BOLD}Error:{Color.RESET} Target trajectory file not found: {bh_file_target}")
sys.exit(1)
if not os.path.exists(constraint_file):
@@ -212,15 +252,17 @@ def main():
# Print header
print_header()
print(f"\n{Color.BOLD}Target Directory:{Color.RESET} {Color.BLUE}{output_dir}{Color.RESET}")
print(f"\n{Color.BOLD}Reference (Baseline):{Color.RESET} {Color.BLUE}{reference_dir}{Color.RESET}")
print(f"{Color.BOLD}Target (Optimized): {Color.RESET} {Color.BLUE}{target_dir}{Color.RESET}")
# Load data
bh_data = load_bh_trajectory(bh_file)
bh_data_ref = load_bh_trajectory(bh_file_ref)
bh_data_target = load_bh_trajectory(bh_file_target)
constraint_data = load_constraint_data(constraint_file)
# Calculate RMS error
rms_abs, rms_rel, error = calculate_rms_error(bh_data)
rms_passed = print_rms_results(rms_abs, rms_rel, error)
rms_rel, error = calculate_rms_error(bh_data_ref, bh_data_target)
rms_passed = print_rms_results(rms_rel, error)
# Analyze constraint violation
constraint_results = analyze_constraint_violation(constraint_data)