Core i7 4770K Xmas

Intel Core i7-4770K testing with a Gigabyte Z97-HD3 (F10c BIOS) and Gigabyte Intel HD 4600 2GB 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 2012256-HA-COREI747702
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Audio Encoding 3 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 4 Tests
CPU Massive 4 Tests
Creator Workloads 5 Tests
Encoding 3 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 2 Tests
Multi-Core 5 Tests
Programmer / Developer System Benchmarks 6 Tests
Server 3 Tests

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December 25 2020
  5 Hours, 48 Minutes
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December 25 2020
  5 Hours, 39 Minutes
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December 25 2020
  5 Hours, 54 Minutes
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  5 Hours, 47 Minutes

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Core i7 4770K Xmas Suite 1.0.0 System Test suite extracted from Core i7 4770K Xmas. pts/brl-cad-1.1.2 VGR Performance Metric pts/build2-1.1.0 Time To Compile pts/clomp-1.1.1 Static OMP Speedup pts/coremark-1.0.1 CoreMark Size 666 - Iterations Per Second pts/encode-ape-1.4.0 WAV To APE 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/node-web-tooling-1.0.0 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/encode-opus-1.1.1 WAV To Opus Encode 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 pts/sqlite-speedtest-1.0.1 Timed Time - Size 1,000 pts/build-eigen-1.1.0 Time To Compile pts/build-ffmpeg-1.0.2 Time To Compile pts/hmmer-1.2.2 Pfam Database Search pts/vkmark-1.2.0 --size 1920x1080 Resolution: 1920 x 1080 pts/encode-wavpack-1.4.1 WAV To WavPack