Apple M2 testing with a Apple MacBook Air (13 h M2 2022) and llvmpipe on Arch rolling 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 2401107-NE-DFHJ8749984 dfhj - Phoronix Test Suite dfhj Apple M2 testing with a Apple MacBook Air (13 h M2 2022) and llvmpipe on Arch rolling via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2401107-NE-DFHJ8749984 .
dfhj Processor Motherboard Chipset Memory Disk Graphics Network OS Kernel Desktop Display Server OpenGL Compiler File-System Screen Resolution a b c Apple M2 @ 2.42GHz (4 Cores / 8 Threads) Apple MacBook Air (13 h M2 2022) Apple Silicon 8GB 251GB APPLE SSD AP0256Z + 2 x 0GB APPLE SSD AP0256Z llvmpipe Broadcom Device 4433 + Broadcom BRCM4387 Bluetooth Arch rolling 6.3.0-asahi-13-1-ARCH (aarch64) KDE Plasma 5.27.6 X Server 1.21.1.8 4.5 Mesa 23.1.3 (LLVM 15.0.7 128 bits) GCC 12.1.0 + Clang 15.0.7 ext4 2560x1600 OpenBenchmarking.org Compiler Details - --build=aarch64-unknown-linux-gnu --disable-libssp --disable-libstdcxx-pch --disable-multilib --disable-werror --enable-__cxa_atexit --enable-bootstrap --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-default-ssp --enable-fix-cortex-a53-835769 --enable-fix-cortex-a53-843419 --enable-gnu-indirect-function --enable-gnu-unique-object --enable-languages=c,c++,fortran,go,lto,objc,obj-c++ --enable-lto --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-unknown-linux-gnu --mandir=/usr/share/man --with-arch=armv8-a --with-linker-hash-style=gnu Processor Details - Scaling Governor: apple-cpufreq schedutil (Boost: Enabled) Python Details - Python 3.11.3 Security Details - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Not affected + srbds: Not affected + tsx_async_abort: Not affected
dfhj lczero: Eigen quicksilver: CTS2 quicksilver: CORAL2 P1 quicksilver: CORAL2 P2 xmrig: KawPow - 1M xmrig: Monero - 1M xmrig: Wownero - 1M xmrig: GhostRider - 1M xmrig: CryptoNight-Heavy - 1M xmrig: CryptoNight-Femto UPX2 - 1M rav1e: 1 rav1e: 5 rav1e: 6 rav1e: 10 pytorch: CPU - 1 - ResNet-50 pytorch: CPU - 1 - ResNet-152 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 1 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream a b c 43 4330000 4832000 7720000 2232.7 2168.2 2437.7 505.5 2319.2 2134.6 0.553 3.157 4.175 11.795 6.30 3.10 4.79 2.32 0.56 1.51 1.3721 1452.6257 54.8669 36.4309 20.9877 95.2178 190.6451 10.4731 9.2739 215.4125 44 4310000 4791000 7686000 2169.4 2128.4 2391.3 505.4 2302.6 2179.5 0.552 3.14 4.197 11.897 6.26 3.15 4.82 2.39 0.57 1.50 1.5268 1306.1071 61.0231 32.7384 23.1798 86.2084 212.7611 9.384 10.2386 194.8216 43 4500000 5094000 8440000 2515.6 2484.2 2746.9 504.3 2494 2553.1 0.612 3.372 4.456 12.016 6.29 3.11 4.85 2.28 0.56 1.51 1.5115 1320.4855 59.6605 33.4981 22.7129 87.8802 207.8481 9.6063 10.039 198.874 OpenBenchmarking.org
LeelaChessZero Backend: Eigen OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.30 Backend: Eigen a b c 10 20 30 40 50 43 44 43 1. (CXX) g++ options: -flto -pthread
Quicksilver Input: CTS2 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CTS2 a b c 1000K 2000K 3000K 4000K 5000K 4330000 4310000 4500000 1. (CXX) g++ options: -fopenmp -O3 -march=native
Quicksilver Input: CORAL2 P1 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CORAL2 P1 a b c 1.1M 2.2M 3.3M 4.4M 5.5M 4832000 4791000 5094000 1. (CXX) g++ options: -fopenmp -O3 -march=native
Quicksilver Input: CORAL2 P2 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CORAL2 P2 a b c 2M 4M 6M 8M 10M 7720000 7686000 8440000 1. (CXX) g++ options: -fopenmp -O3 -march=native
Xmrig Variant: KawPow - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: KawPow - Hash Count: 1M a b c 500 1000 1500 2000 2500 2232.7 2169.4 2515.6 1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: Monero - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: Monero - Hash Count: 1M a b c 500 1000 1500 2000 2500 2168.2 2128.4 2484.2 1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: Wownero - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: Wownero - Hash Count: 1M a b c 600 1200 1800 2400 3000 2437.7 2391.3 2746.9 1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: GhostRider - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: GhostRider - Hash Count: 1M a b c 110 220 330 440 550 505.5 505.4 504.3 1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: CryptoNight-Heavy - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: CryptoNight-Heavy - Hash Count: 1M a b c 500 1000 1500 2000 2500 2319.2 2302.6 2494.0 1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
Xmrig Variant: CryptoNight-Femto UPX2 - Hash Count: 1M OpenBenchmarking.org H/s, More Is Better Xmrig 6.21 Variant: CryptoNight-Femto UPX2 - Hash Count: 1M a b c 500 1000 1500 2000 2500 2134.6 2179.5 2553.1 1. (CXX) g++ options: -fexceptions -fno-rtti -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc
rav1e Speed: 1 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 1 a b c 0.1377 0.2754 0.4131 0.5508 0.6885 0.553 0.552 0.612
rav1e Speed: 5 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 5 a b c 0.7587 1.5174 2.2761 3.0348 3.7935 3.157 3.140 3.372
rav1e Speed: 6 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 6 a b c 1.0026 2.0052 3.0078 4.0104 5.013 4.175 4.197 4.456
rav1e Speed: 10 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 10 a b c 3 6 9 12 15 11.80 11.90 12.02
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b c 2 4 6 8 10 6.30 6.26 6.29 MIN: 4.73 / MAX: 6.86 MIN: 5.68 / MAX: 6.79 MIN: 5.79 / MAX: 6.78
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 a b c 0.7088 1.4176 2.1264 2.8352 3.544 3.10 3.15 3.11 MIN: 2.5 / MAX: 3.53 MIN: 2.54 / MAX: 3.61 MIN: 2.77 / MAX: 3.51
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b c 1.0913 2.1826 3.2739 4.3652 5.4565 4.79 4.82 4.85 MIN: 3.74 / MAX: 5.16 MIN: 4.38 / MAX: 5.12 MIN: 4.45 / MAX: 5.22
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 a b c 0.5378 1.0756 1.6134 2.1512 2.689 2.32 2.39 2.28 MIN: 1.85 / MAX: 2.46 MIN: 1.95 / MAX: 2.56 MIN: 2.07 / MAX: 2.43
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l a b c 0.1283 0.2566 0.3849 0.5132 0.6415 0.56 0.57 0.56 MIN: 0.28 / MAX: 1 MIN: 0.39 / MAX: 1.01 MIN: 0.33 / MAX: 0.99
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l a b c 0.3398 0.6796 1.0194 1.3592 1.699 1.51 1.50 1.51 MIN: 1.34 / MAX: 1.59 MIN: 1.35 / MAX: 1.58 MIN: 1.44 / MAX: 1.58
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a b c 0.3435 0.687 1.0305 1.374 1.7175 1.3721 1.5268 1.5115
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a b c 300 600 900 1200 1500 1452.63 1306.11 1320.49
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 14 28 42 56 70 54.87 61.02 59.66
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 8 16 24 32 40 36.43 32.74 33.50
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream a b c 6 12 18 24 30 20.99 23.18 22.71
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream a b c 20 40 60 80 100 95.22 86.21 87.88
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 50 100 150 200 250 190.65 212.76 207.85
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c 3 6 9 12 15 10.4731 9.3840 9.6063
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a b c 3 6 9 12 15 9.2739 10.2386 10.0390
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a b c 50 100 150 200 250 215.41 194.82 198.87
Phoronix Test Suite v10.8.5