Core i3 7100 Xmas Eve

Intel Core i3-7100 testing with a Gigabyte B250M-DS3H-CF (F9 BIOS) and Gigabyte Intel HD 630 3GB on Ubuntu 20.10 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 2012250-HA-COREI371034
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Audio Encoding 3 Tests
Bioinformatics 2 Tests
Chess Test Suite 3 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 5 Tests
CPU Massive 9 Tests
Creator Workloads 6 Tests
Encoding 4 Tests
HPC - High Performance Computing 5 Tests
Machine Learning 2 Tests
Multi-Core 8 Tests
NVIDIA GPU Compute 3 Tests
Programmer / Developer System Benchmarks 6 Tests
Scientific Computing 3 Tests
Server 4 Tests
Server CPU Tests 4 Tests
Vulkan Compute 3 Tests

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December 24 2020
  9 Hours, 21 Minutes
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December 24 2020
  9 Hours, 17 Minutes
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December 25 2020
  9 Hours, 16 Minutes
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Core i3 7100 Xmas Eve Suite 1.0.0 System Test suite extracted from Core i3 7100 Xmas Eve. pts/vkmark-1.2.0 --size 1920x1080 Resolution: 1920 x 1080 pts/vkmark-1.2.0 --size 1280x1024 Resolution: 1280 x 1024 pts/crafty-1.4.5 Elapsed Time pts/clomp-1.1.1 Static OMP Speedup pts/encode-ape-1.4.0 WAV To APE pts/encode-opus-1.1.1 WAV To Opus Encode pts/encode-wavpack-1.4.1 WAV To WavPack pts/astcenc-1.0.2 -fast Preset: Fast pts/astcenc-1.0.2 -medium Preset: Medium pts/astcenc-1.0.2 -thorough Preset: Thorough pts/astcenc-1.0.2 -exhaustive Preset: Exhaustive pts/hmmer-1.2.2 Pfam Database Search pts/mafft-1.6.2 Multiple Sequence Alignment - LSU RNA pts/hpcc-1.2.8 HPL Test / Class: G-HPL pts/hpcc-1.2.8 MPIFFT Test / Class: G-Ffte pts/hpcc-1.2.8 STARDGEMMFLOPS Test / Class: EP-DGEMM pts/hpcc-1.2.8 PTRANS Test / Class: G-Ptrans pts/hpcc-1.2.8 STARSTREAMTRIAD Test / Class: EP-STREAM Triad pts/hpcc-1.2.8 MPIRANDOMACCESS Test / Class: G-Random Access pts/hpcc-1.2.8 RRINGLATENCY Test / Class: Random Ring Latency pts/hpcc-1.2.8 RRINGBANDWIDTH Test / Class: Random Ring Bandwidth pts/hpcc-1.2.8 MAXPPBANDWIDTH Test / Class: Max Ping Pong Bandwidth pts/ncnn-1.1.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.1.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.1.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.1.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.1.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.1.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.1.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.1.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.1.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.1.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.1.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.1.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.1.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.1.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.1.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.1.0 Target: Vulkan GPU - Model: mobilenet pts/ncnn-1.1.0 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.1.0 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.1.0 Target: Vulkan GPU - Model: shufflenet-v2 pts/ncnn-1.1.0 Target: Vulkan GPU - Model: mnasnet pts/ncnn-1.1.0 Target: Vulkan GPU - Model: efficientnet-b0 pts/ncnn-1.1.0 Target: Vulkan GPU - Model: blazeface pts/ncnn-1.1.0 Target: Vulkan GPU - Model: googlenet pts/ncnn-1.1.0 Target: Vulkan GPU - Model: vgg16 pts/ncnn-1.1.0 Target: Vulkan GPU - Model: resnet18 pts/ncnn-1.1.0 Target: Vulkan GPU - Model: alexnet pts/ncnn-1.1.0 Target: Vulkan GPU - Model: resnet50 pts/ncnn-1.1.0 Target: Vulkan GPU - Model: yolov4-tiny pts/ncnn-1.1.0 Target: Vulkan GPU - Model: squeezenet_ssd pts/ncnn-1.1.0 Target: Vulkan GPU - Model: regnety_400m pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.1 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/coremark-1.0.1 CoreMark Size 666 - Iterations Per Second pts/build-ffmpeg-1.0.2 Time To Compile pts/stockfish-1.2.0 Total Time pts/asmfish-1.1.2 1024 Hash Memory, 26 Depth pts/rav1e-1.4.0 -s 1 -l 20 Speed: 1 pts/rav1e-1.4.0 -s 5 -l 60 Speed: 5 pts/rav1e-1.4.0 -s 6 -l 60 Speed: 6 pts/rav1e-1.4.0 -s 10 -l 90 Speed: 10 pts/build2-1.1.0 Time To Compile pts/build-eigen-1.1.0 Time To Compile pts/vkfft-1.1.0 pts/vkresample-1.0.0 -u 2 -p 1 Upscale: 2x - Precision: Double pts/vkresample-1.0.0 -u 2 -p 0 Upscale: 2x - Precision: Single pts/phpbench-1.1.6 PHP Benchmark Suite pts/sqlite-2.1.0 1 Threads / Copies: 1 pts/node-web-tooling-1.0.0 pts/simdjson-1.1.1 Kostya Throughput Test: Kostya pts/simdjson-1.1.1 LargeRandom Throughput Test: LargeRandom pts/simdjson-1.1.1 PartialTweets Throughput Test: PartialTweets pts/simdjson-1.1.1 DistinctUserID Throughput Test: DistinctUserID