AMD EPYC 7551 32-Core testing with a GIGABYTE MZ31-AR0-00 v01010101 (F10 BIOS) and ASPEED on Debian 11 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2205193-HA-RENAISSAN17 renaissance-tensorflow-epyc-1 - Phoronix Test Suite renaissance-tensorflow-epyc-1 AMD EPYC 7551 32-Core testing with a GIGABYTE MZ31-AR0-00 v01010101 (F10 BIOS) and ASPEED on Debian 11 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2205193-HA-RENAISSAN17&grt&sor .
renaissance-tensorflow-epyc-1 Processor Motherboard Chipset Memory Disk Graphics Network OS Kernel Compiler File-System Screen Resolution A AA B AMD EPYC 7551 32-Core @ 2.00GHz (32 Cores / 64 Threads) GIGABYTE MZ31-AR0-00 v01010101 (F10 BIOS) AMD 17h 8 x 4 GB DDR4-2667MT/s 9ASF51272PZ-2G6E1 Samsung SSD 960 EVO 500GB + 31GB SanDisk 3.2Gen1 ASPEED Realtek RTL8111/8168/8411 + 2 x Broadcom NetXtreme II BCM57810 10 Debian 11 5.10.0-9-amd64 (x86_64) GCC 10.2.1 20210110 ext4 1024x768 OpenBenchmarking.org Processor Details - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8001227 Java Details - OpenJDK Runtime Environment (build 11.0.12+7-post-Debian-2) Security Details - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Full AMD retpoline IBPB: conditional STIBP: disabled RSB filling + srbds: Not affected + tsx_async_abort: Not affected
renaissance-tensorflow-epyc-1 renaissance: Scala Dotty renaissance: Rand Forest renaissance: ALS Movie Lens renaissance: Apache Spark ALS renaissance: Apache Spark Bayes renaissance: Savina Reactors.IO tensorflow-lite: SqueezeNet tensorflow-lite: Inception V4 tensorflow-lite: NASNet Mobile tensorflow-lite: Mobilenet Float tensorflow-lite: Mobilenet Quant tensorflow-lite: Inception ResNet V2 A AA B 1441.3 1414.3 1328.5 1387.9 58423.5 103098.4 1275.2 17612.6 5726.94 71861.8 51438.5 5309.11 6179.21 77564.4 1462.9 1397.7 57933.2 102444.4 1289.3 17685.9 5839.22 71667.3 57310.2 4620.06 6141.14 76295.1 OpenBenchmarking.org
Renaissance Test: Scala Dotty OpenBenchmarking.org ms, Fewer Is Better Renaissance 0.14 Test: Scala Dotty AA A B 300 600 900 1200 1500 SE +/- 21.11, N = 15 1328.5 1441.3 1462.9 MIN: 1121.56 / MAX: 2187.94 MIN: 1095.95 / MAX: 2401.44 MIN: 1133.63 / MAX: 2180.02
Renaissance Test: Random Forest OpenBenchmarking.org ms, Fewer Is Better Renaissance 0.14 Test: Random Forest AA B A 300 600 900 1200 1500 SE +/- 7.88, N = 3 1387.9 1397.7 1414.3 MIN: 1212.94 / MAX: 1739.82 MIN: 1206.96 / MAX: 1694.18 MIN: 1199.02 / MAX: 1868.75
Renaissance Test: ALS Movie Lens OpenBenchmarking.org ms, Fewer Is Better Renaissance 0.14 Test: ALS Movie Lens B AA 13K 26K 39K 52K 65K 57933.2 58423.5 MAX: 65265.19 MAX: 65428.91
Renaissance Test: Apache Spark ALS OpenBenchmarking.org ms, Fewer Is Better Renaissance 0.14 Test: Apache Spark ALS B AA 20K 40K 60K 80K 100K 102444.4 103098.4 MIN: 101796.3 / MAX: 103007.18 MIN: 101916.49 / MAX: 105111.39
Renaissance Test: Apache Spark Bayes OpenBenchmarking.org ms, Fewer Is Better Renaissance 0.14 Test: Apache Spark Bayes AA B 300 600 900 1200 1500 1275.2 1289.3 MIN: 904.2 / MAX: 1299.52 MIN: 853.48 / MAX: 1682.54
Renaissance Test: Savina Reactors.IO OpenBenchmarking.org ms, Fewer Is Better Renaissance 0.14 Test: Savina Reactors.IO AA B 4K 8K 12K 16K 20K 17612.6 17685.9 MIN: 17612.56 / MAX: 27146.82 MAX: 31154.83
TensorFlow Lite Model: SqueezeNet OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: SqueezeNet AA B 1300 2600 3900 5200 6500 5726.94 5839.22
TensorFlow Lite Model: Inception V4 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Inception V4 B AA 15K 30K 45K 60K 75K 71667.3 71861.8
TensorFlow Lite Model: NASNet Mobile OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: NASNet Mobile AA B 12K 24K 36K 48K 60K 51438.5 57310.2
TensorFlow Lite Model: Mobilenet Float OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Mobilenet Float B AA 1100 2200 3300 4400 5500 4620.06 5309.11
TensorFlow Lite Model: Mobilenet Quant OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Mobilenet Quant B AA 1300 2600 3900 5200 6500 6141.14 6179.21
TensorFlow Lite Model: Inception ResNet V2 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Inception ResNet V2 B AA 17K 34K 51K 68K 85K 76295.1 77564.4
Phoronix Test Suite v10.8.5