- Clone the repository
- Install (mkosi)[https://github.com/systemd/mkosi]
- Install python3-pandas, python3-numpy and python3-click
- Clone mkosi repository at
../mkosirelative to the fpbench repository root - build systemd-nspawn from source and make the executable at
../systemd/build/systemd-nspawnrelative to the fpbench repository root
# Run benchmarks in Fedora 37 with frame pointers container
sudo ../mkosi/bin/mkosi --default no-omit-fp/mkosi.conf boot systemd.unit=bench.service
# Run benchmarks in Fedora 37 without frame pointers container
sudo ../mkosi/bin/mkosi --default omit-fp/mkosi.conf boot systemd.unit=bench.service
# Analyze and display the results
./analyze.py bench.json
Results:
Benchmark Result Mean (omit / no-omit) Mean Difference Std Dev (omit / no-omit) Num Tests (omit / no-omit)
blender Duration (s) 392.75 / 400.2 1.9% 0.2111% / 0.4299% 4 / 5
botan AES-256 (MB/second) 5721.316 / 5694.8384 0.5% 0.486% / 0.6865% 8 / 10
botan AES-256 [openssl] (MB/second) 5598.8459 / 5566.2604 0.6% 1.1385% / 1.2932% 8 / 10
botan Blowfish (MB/second) 231.6743 / 231.6037 0.0% 1.2896% / 1.2925% 8 / 10
botan CAST-256 (MB/second) 90.6434 / 90.63 0.0% 0.1703% / 0.1823% 8 / 10
botan ChaCha20Poly1305 (MB/second) 642.0556 / 641.6031 0.1% 0.5257% / 0.8547% 8 / 10
botan KASUMI (MB/second) 78.1414 / 78.0889 0.1% 1.586% / 1.508% 8 / 10
botan Twofish (MB/second) 176.0366 / 177.4218 0.8% 2.3791% / 0.5486% 8 / 10
gcc Duration (s) 246.9607 / 252.9407 2.4% 0.0859% / 0.0395% 4 / 4
openssl aes-128-cbc (MB/second) 1688.5987 / 1688.0522 0.0% 0.0229% / 0.035% 4 / 5
openssl aes-192-cbc (MB/second) 1429.2144 / 1429.2428 0.0% 0.0433% / 0.0139% 4 / 5
openssl aes-256-cbc (MB/second) 1238.3488 / 1238.5301 0.0% 0.02% / 0.0204% 4 / 5
openssl camellia-128-cbc (MB/second) 204.8652 / 204.8495 0.0% 0.0537% / 0.0136% 4 / 5
openssl camellia-192-cbc (MB/second) 152.7494 / 152.742 0.0% 0.0177% / 0.0231% 4 / 5
openssl camellia-256-cbc (MB/second) 152.7787 / 152.8181 0.0% 0.0293% / 0.0424% 4 / 5
openssl des-ede3 (MB/second) 28.481 / 28.4732 0.0% 0.5417% / 0.3393% 4 / 5
openssl ghash (MB/second) 14061.5 / 14057.4878 0.0% 0.1272% / 0.0857% 4 / 5
openssl hmac(md5) (MB/second) 720.2127 / 720.1047 0.0% 0.0311% / 0.0631% 4 / 5
openssl md5 (MB/second) 724.5759 / 724.535 0.0% 0.0195% / 0.0279% 4 / 5
openssl rand (MB/second) 5909.9436 / 5928.8331 0.3% 0.0609% / 0.2412% 4 / 5
openssl sha1 (MB/second) 1665.9534 / 1666.3307 0.0% 0.0279% / 0.0259% 4 / 5
openssl sha256 (MB/second) 1317.9472 / 1317.9478 0.0% 0.0374% / 0.022% 4 / 5
openssl sha512 (MB/second) 644.6275 / 644.3334 0.0% 0.042% / 0.0419% 4 / 5
pgbench Average Latency (in ms) 2.7258 / 2.7633 1.4% 4.6027% / 4.5057% 12 / 15
pgbench Transactions per second 7356.0942 / 7252.962 1.4% 4.4527% / 4.4865% 12 / 15
pyperformance 2to3 0.2823 / 0.2886 2.2% 0.4236% / 0.2374% 4 / 5
pyperformance chameleon 0.0075 / 0.0077 2.9% 0.3635% / 0.8713% 4 / 5
pyperformance chaos 0.0757 / 0.0769 1.5% 0.3042% / 0.1542% 4 / 5
pyperformance crypto_pyaes 0.0808 / 0.0836 3.5% 0.3391% / 0.3949% 4 / 5
pyperformance deltablue 0.0041 / 0.0043 5.9% 0.0402% / 0.5238% 4 / 5
pyperformance django_template 0.0365 / 0.039 6.6% 0.4204% / 0.1759% 4 / 5
pyperformance dulwich_log 0.065 / 0.0673 3.5% 0.2735% / 0.5403% 4 / 5
pyperformance fannkuch 0.4175 / 0.4276 2.4% 0.1763% / 0.9582% 4 / 5
pyperformance float 0.0829 / 0.0862 3.9% 0.4386% / 0.8319% 4 / 5
pyperformance genshi_text 0.027 / 0.0269 0.6% 0.3068% / 0.5333% 4 / 5
pyperformance genshi_xml 0.0614 / 0.063 2.6% 0.2192% / 0.2014% 4 / 5
pyperformance go 0.1519 / 0.1552 2.2% 0.2472% / 1.1143% 4 / 5
pyperformance hexiom 0.007 / 0.0073 4.2% 0.214% / 0.2223% 4 / 5
pyperformance html5lib 0.0674 / 0.0698 3.5% 0.1654% / 0.2173% 4 / 5
pyperformance json_dumps 0.012 / 0.0126 5.2% 0.5807% / 0.7049% 4 / 5
pyperformance json_loads 0.0 / 0.0 5.6% 0.1126% / 0.4373% 4 / 5
pyperformance logging_format 0.0 / 0.0 4.1% 0.3382% / 0.1203% 4 / 5
pyperformance logging_silent 0.0 / 0.0 3.6% 0.321% / 0.3155% 4 / 5
pyperformance logging_simple 0.0 / 0.0 4.7% 0.3069% / 0.1819% 4 / 5
pyperformance mako 0.0106 / 0.011 3.7% 1.0228% / 0.3132% 4 / 5
pyperformance meteor_contest 0.1107 / 0.115 3.8% 0.1211% / 0.1522% 4 / 5
pyperformance nbody 0.0974 / 0.1066 8.6% 0.5261% / 1.6923% 4 / 5
pyperformance nqueens 0.0989 / 0.1007 1.8% 0.1031% / 0.318% 4 / 5
pyperformance pathlib 0.0174 / 0.0182 4.5% 0.8631% / 0.6443% 4 / 5
pyperformance pickle 0.0 / 0.0 7.1% 0.5275% / 0.4534% 4 / 5
pyperformance pickle_dict 0.0 / 0.0 3.8% 0.2149% / 0.3785% 4 / 5
pyperformance pickle_list 0.0 / 0.0 0.4% 0.6997% / 0.3421% 4 / 5
pyperformance pickle_pure_python 0.0003 / 0.0003 4.3% 0.1638% / 0.6242% 4 / 5
pyperformance pidigits 0.1953 / 0.1964 0.6% 0.0255% / 0.039% 4 / 5
pyperformance pyflate 0.4697 / 0.4817 2.5% 1.202% / 0.6028% 4 / 5
pyperformance python_startup 0.009 / 0.0091 1.4% 1.3195% / 0.8585% 4 / 5
pyperformance python_startup_no_site 0.0063 / 0.0064 0.8% 0.5466% / 0.9968% 4 / 5
pyperformance raytrace 0.3316 / 0.3454 4.0% 0.2206% / 0.1397% 4 / 5
pyperformance regex_compile 0.1515 / 0.1568 3.4% 0.095% / 0.2667% 4 / 5
pyperformance regex_dna 0.1636 / 0.1712 4.4% 0.1309% / 0.1551% 4 / 5
pyperformance regex_effbot 0.0025 / 0.0027 6.0% 0.072% / 0.2793% 4 / 5
pyperformance regex_v8 0.0185 / 0.0195 5.1% 0.0692% / 0.3962% 4 / 5
pyperformance richards 0.0478 / 0.051 6.3% 0.1424% / 0.3229% 4 / 5
pyperformance scimark_fft 0.3213 / 0.3517 8.6% 0.3755% / 0.3829% 4 / 5
pyperformance scimark_lu 0.1288 / 0.1366 5.7% 0.3641% / 0.8092% 4 / 5
pyperformance scimark_monte_carlo 0.0728 / 0.0781 6.8% 0.2093% / 0.8044% 4 / 5
pyperformance scimark_sor 0.1277 / 0.1337 4.5% 0.3221% / 0.2813% 4 / 5
pyperformance scimark_sparse_mat_mult 0.0047 / 0.0052 9.5% 0.3764% / 0.5924% 4 / 5
pyperformance spectral_norm 0.109 / 0.1182 7.7% 0.2437% / 1.1068% 4 / 5
pyperformance sqlalchemy_declarative 0.1323 / 0.1358 2.6% 0.1379% / 0.3609% 4 / 5
pyperformance sqlalchemy_imperative 0.0176 / 0.0183 3.4% 0.4265% / 0.2622% 4 / 5
pyperformance sqlite_synth 0.0 / 0.0 7.6% 0.4354% / 1.4469% 4 / 5
pyperformance sympy_expand 0.5166 / 0.5408 4.5% 0.0961% / 0.2924% 4 / 5
pyperformance sympy_integrate 0.0219 / 0.0227 3.4% 0.1906% / 0.4094% 4 / 5
pyperformance sympy_str 0.309 / 0.3218 4.0% 0.1631% / 0.1377% 4 / 5
pyperformance sympy_sum 0.1654 / 0.1717 3.7% 0.1806% / 0.2936% 4 / 5
pyperformance telco 0.0075 / 0.0079 5.1% 0.7319% / 0.536% 4 / 5
pyperformance tornado_http 0.1148 / 0.1165 1.5% 0.299% / 0.5194% 4 / 5
pyperformance unpack_sequence 0.0 / 0.0 1.2% 0.2169% / 1.0563% 4 / 5
pyperformance unpickle 0.0 / 0.0 4.4% 0.48% / 0.3702% 4 / 5
pyperformance unpickle_list 0.0 / 0.0 3.8% 0.0715% / 0.8382% 4 / 5
pyperformance unpickle_pure_python 0.0003 / 0.0003 3.2% 0.0958% / 0.1659% 4 / 5
pyperformance xml_etree_generate 0.0845 / 0.0902 6.3% 0.0764% / 0.5246% 4 / 5
pyperformance xml_etree_iterparse 0.1093 / 0.1131 3.3% 0.4933% / 0.648% 4 / 5
pyperformance xml_etree_parse 0.1671 / 0.173 3.4% 0.2738% / 0.797% 4 / 5
pyperformance xml_etree_process 0.0617 / 0.0658 6.2% 0.3025% / 1.196% 4 / 5
redis GET (requests per second) 2236723.2778 / 2214881.0667 1.0% 0.3753% / 0.5794% 9 / 15
redis LPOP (requests per second) 1604092.6667 / 1596965.076 0.4% 0.5202% / 0.4415% 9 / 15
redis LPUSH (requests per second) 1699474.6533 / 1698218.7593 0.1% 2.1367% / 0.4933% 9 / 15
redis SADD (requests per second) 2041983.3333 / 2063767.1413 1.1% 0.5224% / 0.4361% 9 / 15
redis SET (requests per second) 1843387.11 / 1835161.7687 0.4% 0.8817% / 0.6366% 9 / 15
zstd Compression Speed (MB/s) 259.7067 / 256.09 1.4% 1.6995% / 1.6875% 30 / 50
zstd Decompression Speed (MB/s) 1820.99 / 1829.85 0.5% 0.2523% / 0.4443% 30 / 50