switch ipynb -> py

benchmarks/df/dft_driver.py

@@ -88,16 +88,16 @@ def run_dft(path, filename):
 
     hess_time = -1
     if args.with_hessian:
-        #try:
-        start_time = time.time()
-        h = mf.Hessian()
-        h.auxbasis_response = 1
-        h.max_memory = 40000
-        hess = h.kernel().reshape([3*mol.natm, 3*mol.natm])
-        hess_time = time.time() - start_time
-        #except Exception:
-        #   hess_time = -1
-        #    hess = -1
+        try:
+            start_time = time.time()
+            h = mf.Hessian()
+            h.auxbasis_response = 1
+            h.max_memory = 40000
+            hess = h.kernel().reshape([3*mol.natm, 3*mol.natm])
+            hess_time = time.time() - start_time
+        except Exception:
+            hess_time = -1
+            hess = -1
 
     np.savez(args.output_path+filename+'.npz', e_dft=e_dft, grad=f, hess=hess)
     return mol.natm, mol.nao, scf_time, grad_time, hess_time, e_dft

benchmarks/df/generate_tables.py

@@ -0,0 +1,113 @@
+import pandas as pd
+import numpy as np
+
+# -------------------------------------------
+# |   Density fitting with different basis  |
+# -------------------------------------------
+
+A100_file = 'NVIDIA A100-SXM4-80GB.csv'
+V100_file = 'Tesla V100-SXM2-32GB.csv'
+qchem_file = 'qchem-32-cores-cpu.csv'
+
+keys = ['mol', 'natm']
+empty = {'mol':[], 'natm':[]}
+df_A100_scf = pd.DataFrame(empty)
+df_V100_scf = pd.DataFrame(empty)
+df_A100_grad = pd.DataFrame(empty)
+df_V100_grad = pd.DataFrame(empty)
+path = 'organic/basis/'
+
+for basis in ['sto-3g', '6-31g', 'def2-svp', 'def2-tzvpp', 'def2-tzvpd']:
+    df_qchem = pd.read_csv(path + basis + '/' + qchem_file)
+    df_qchem = df_qchem.rename(columns={'t_scf':'scf_qchem', 't_gradient':'grad_qchem'})
+
+    df_A100 = pd.read_csv(path + basis + '/' + A100_file)
+    df_A100 = df_A100.rename(columns={'t_scf':'scf_A100', 't_gradient':'grad_A100'})
+    df_A100 = df_A100.merge(df_qchem, how='outer', on='mol')
+
+    df_A100['scf_'+basis] = df_A100['scf_qchem']/df_A100['scf_A100']
+    df_A100['grad_'+basis] = df_A100['grad_qchem']/df_A100['grad_A100']
+    df_A100 = df_A100[keys+['scf_'+basis, 'grad_'+basis]]
+
+    df_A100_scf = df_A100_scf.merge(df_A100[keys+['scf_'+basis]], how='outer', on=keys)
+    df_A100_grad= df_A100_grad.merge(df_A100[keys+['grad_'+basis]], how='outer', on=keys)
+    df_A100_scf = df_A100_scf.rename(columns={'scf_'+basis:basis})
+    df_A100_grad = df_A100_grad.rename(columns={'grad_'+basis:basis})
+    df_A100_scf[basis] = df_A100_scf[basis].apply(lambda x: round(x,2))
+    df_A100_grad[basis] = df_A100_grad[basis].apply(lambda x: round(x,2))
+
+    df_V100 = pd.read_csv(path + basis + '/' + V100_file)
+    df_V100 = df_V100.rename(columns={'t_scf':'scf_V100', 't_gradient':'grad_V100'})
+    df_V100 = df_V100.merge(df_qchem, how='outer', on='mol')
+    df_V100['scf_'+basis] = df_V100['scf_qchem']/df_V100['scf_V100']
+    df_V100['grad_'+basis] = df_V100['grad_qchem']/df_V100['grad_V100']
+
+    df_V100_scf = df_V100_scf.merge(df_V100[keys+['scf_'+basis,]], how='outer', on=keys)
+    df_V100_grad= df_V100_grad.merge(df_V100[keys+['grad_'+basis]], how='outer', on=keys)
+    df_V100_scf = df_V100_scf.rename(columns={'scf_'+basis:basis})
+
+    df_V100_grad = df_V100_grad.rename(columns={'grad_'+basis:basis})
+    df_V100_scf[basis] = df_V100_scf[basis].apply(lambda x: round(x,2))
+    df_V100_grad[basis] = df_V100_grad[basis].apply(lambda x: round(x,2))
+
+print("\n============SCF speedup with A100-80G============\n")
+print(df_A100_scf.to_markdown(index=False))
+print("\n============SCF speedup with V100-32G============\n")
+print(df_V100_scf.to_markdown(index=False))
+print("\n============Gradient speedup with A100-80G=======\n")
+print(df_A100_grad.to_markdown(index=False))
+print("\n============Gradient speedup with V100-32G=======\n")
+print(df_V100_grad.to_markdown(index=False))
+
+# -----------------------------------------
+# |   Density fitting with different xc   |
+# -----------------------------------------
+
+keys = ['mol', 'natm']
+empty = {'mol':[], 'natm':[]}
+df_A100_scf = pd.DataFrame(empty)
+df_V100_scf = pd.DataFrame(empty)
+df_A100_grad = pd.DataFrame(empty)
+df_V100_grad = pd.DataFrame(empty)
+path = 'organic/xc/'
+for xc in ['LDA', 'PBE', 'B3LYP', 'M06', 'wB97m-v']:
+    df_qchem = pd.read_csv(path + xc + '/' + qchem_file)
+    df_qchem = df_qchem.rename(columns={'t_scf':'scf_qchem', 't_gradient':'grad_qchem'})
+
+    df_A100 = pd.read_csv(path + xc + '/' + A100_file)
+    df_A100 = df_A100.rename(columns={'t_scf':'scf_A100', 't_gradient':'grad_A100'})
+    df_A100 = df_A100.merge(df_qchem, how='outer', on='mol')
+
+    df_A100['scf_'+xc] = df_A100['scf_qchem']/df_A100['scf_A100']
+    df_A100['grad_'+xc] = df_A100['grad_qchem']/df_A100['grad_A100']
+    df_A100 = df_A100[keys+['scf_'+xc, 'grad_'+xc]]
+
+    df_A100_scf = df_A100_scf.merge(df_A100[keys+['scf_'+xc]], how='outer', on=keys)
+    df_A100_grad= df_A100_grad.merge(df_A100[keys+['grad_'+xc]], how='outer', on=keys)
+    df_A100_scf = df_A100_scf.rename(columns={'scf_'+xc:xc})
+    df_A100_grad = df_A100_grad.rename(columns={'grad_'+xc:xc})
+    df_A100_scf[xc] = df_A100_scf[xc].apply(lambda x: round(x,2))
+    df_A100_grad[xc] = df_A100_grad[xc].apply(lambda x: round(x,2))
+
+    df_V100 = pd.read_csv(path + xc + '/' + V100_file)
+    df_V100 = df_V100.rename(columns={'t_scf':'scf_V100', 't_gradient':'grad_V100'})
+    df_V100 = df_V100.merge(df_qchem, how='outer', on='mol')
+    df_V100['scf_'+xc] = df_V100['scf_qchem']/df_V100['scf_V100']
+    df_V100['grad_'+xc] = df_V100['grad_qchem']/df_V100['grad_V100']
+
+    df_V100_scf = df_V100_scf.merge(df_V100[keys+['scf_'+xc,]], how='outer', on=keys)
+    df_V100_grad= df_V100_grad.merge(df_V100[keys+['grad_'+xc]], how='outer', on=keys)
+    df_V100_scf = df_V100_scf.rename(columns={'scf_'+xc:xc})
+
+    df_V100_grad = df_V100_grad.rename(columns={'grad_'+xc:xc})
+    df_V100_scf[xc] = df_V100_scf[xc].apply(lambda x: round(x,2))
+    df_V100_grad[xc] = df_V100_grad[xc].apply(lambda x: round(x,2))
+
+print("\n============SCF speedup with A100-80G============\n")
+print(df_A100_scf.to_markdown(index=False))
+print("\n============SCF speedup with V100-32G============\n")
+print(df_V100_scf.to_markdown(index=False))
+print("\n============Gradient speedup with A100-80G=======\n")
+print(df_A100_grad.to_markdown(index=False))
+print("\n============Gradient speedup with V100-32G=======\n")
+print(df_V100_grad.to_markdown(index=False))