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
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.
,,"1","2","3"
Processor,,Intel Core i7-4770K @ 3.90GHz (4 Cores / 8 Threads),Intel Core i7-4770K @ 3.90GHz (4 Cores / 8 Threads),Intel Core i7-4770K @ 3.90GHz (4 Cores / 8 Threads)
Motherboard,,Gigabyte Z97-HD3 (F10c BIOS),Gigabyte Z97-HD3 (F10c BIOS),Gigabyte Z97-HD3 (F10c BIOS)
Chipset,,Intel 4th Gen Core DRAM,Intel 4th Gen Core DRAM,Intel 4th Gen Core DRAM
Memory,,8GB,8GB,8GB
Disk,,120GB ADATA SU700,120GB ADATA SU700,120GB ADATA SU700
Graphics,,Gigabyte Intel HD 4600 2GB (1250MHz),Gigabyte Intel HD 4600 2GB (1250MHz),Gigabyte Intel HD 4600 2GB (1250MHz)
Audio,,Intel Xeon E3-1200 v3/4th,Intel Xeon E3-1200 v3/4th,Intel Xeon E3-1200 v3/4th
Monitor,,DELL S2409W,DELL S2409W,DELL S2409W
Network,,Realtek RTL8111/8168/8411,Realtek RTL8111/8168/8411,Realtek RTL8111/8168/8411
OS,,Ubuntu 20.10,Ubuntu 20.10,Ubuntu 20.10
Kernel,,5.8.0-31-generic (x86_64),5.8.0-31-generic (x86_64),5.8.0-31-generic (x86_64)
Desktop,,GNOME Shell 3.38.1,GNOME Shell 3.38.1,GNOME Shell 3.38.1
Display Server,,X Server 1.20.9,X Server 1.20.9,X Server 1.20.9
Display Driver,,modesetting 1.20.9,modesetting 1.20.9,modesetting 1.20.9
OpenGL,,4.5 Mesa 20.2.1,4.5 Mesa 20.2.1,4.5 Mesa 20.2.1
Vulkan,,1.2.145,1.2.145,1.2.145
Compiler,,GCC 10.2.0,GCC 10.2.0,GCC 10.2.0
File-System,,ext4,ext4,ext4
Screen Resolution,,1920x1080,1920x1080,1920x1080
,,"1","2","3"
"simdjson - Throughput Test: Kostya (GB/s)",HIB,0.60,0.60,0.60
"simdjson - Throughput Test: LargeRandom (GB/s)",HIB,0.4,0.4,0.4
"simdjson - Throughput Test: PartialTweets (GB/s)",HIB,0.66,0.66,0.66
"simdjson - Throughput Test: DistinctUserID (GB/s)",HIB,0.68,0.68,0.68
"Coremark - CoreMark Size 666 - Iterations Per Second (Iterations/Sec)",HIB,144430.967517,144054.759207,144381.891641
"Node.js V8 Web Tooling Benchmark - (runs/s)",HIB,9.30,9.29,9.24
"CLOMP - Static OMP Speedup (Speedup)",HIB,0.9,1.1,1.1
"BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,42373,42365,42169
"VKMark - Resolution: 1920 x 1080 (VKMark Score)",HIB,292,292,290
"oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,11.1734,11.3617,11.5933
"oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,15.9142,15.6513,18.8928
"oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,5.97793,5.94146,5.99872
"oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,4.72613,4.69355,4.99786
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,32.1198,32.1261,32.5780
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,13.5497,13.3557,13.2922
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,18.9265,18.9535,19.0737
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,31.0265,30.9250,30.9474
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,14.4191,14.8307,14.5672
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,12.9561,12.9542,12.9019
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,10419.2,10472.4,10683.5
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,5784.04,5922.99,5971.44
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,10639.4,10420.8,10853.5
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,5730.72,5954.52,6041.90
"oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,8.10435,8.09766,8.85443
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,10641.5,10647.0,10925.1
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,5907.30,5822.13,5981.80
"oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,7.32951,7.33567,7.23631
"NCNN - Target: CPU - Model: mobilenet (ms)",LIB,39.30,39.29,39.77
"NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,10.42,10.66,11.04
"NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,8.74,8.66,8.91
"NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,11.41,11.42,11.44
"NCNN - Target: CPU - Model: mnasnet (ms)",LIB,8.37,8.42,8.65
"NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,14.26,14.32,14.43
"NCNN - Target: CPU - Model: blazeface (ms)",LIB,3.29,3.17,3.28
"NCNN - Target: CPU - Model: googlenet (ms)",LIB,29.65,30.43,30.19
"NCNN - Target: CPU - Model: vgg16 (ms)",LIB,127.46,127.55,128.02
"NCNN - Target: CPU - Model: resnet18 (ms)",LIB,30.48,31.42,30.75
"NCNN - Target: CPU - Model: alexnet (ms)",LIB,25.10,25.16,25.17
"NCNN - Target: CPU - Model: resnet50 (ms)",LIB,61.85,63.27,64.10
"NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,51.58,52.98,54.23
"NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,40.61,40.30,41.57
"NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,20.69,20.55,20.90
"NCNN - Target: Vulkan GPU - Model: mobilenet (ms)",LIB,39.24,39.55,39.64
"NCNN - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,10.56,10.50,10.89
"NCNN - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,8.77,8.72,8.84
"NCNN - Target: Vulkan GPU - Model: shufflenet-v2 (ms)",LIB,11.51,11.42,11.49
"NCNN - Target: Vulkan GPU - Model: mnasnet (ms)",LIB,8.53,8.41,8.43
"NCNN - Target: Vulkan GPU - Model: efficientnet-b0 (ms)",LIB,14.40,14.28,14.26
"NCNN - Target: Vulkan GPU - Model: blazeface (ms)",LIB,3.25,3.18,3.24
"NCNN - Target: Vulkan GPU - Model: googlenet (ms)",LIB,29.81,30.53,30.37
"NCNN - Target: Vulkan GPU - Model: vgg16 (ms)",LIB,126.48,128.34,127.97
"NCNN - Target: Vulkan GPU - Model: resnet18 (ms)",LIB,30.47,30.64,30.69
"NCNN - Target: Vulkan GPU - Model: alexnet (ms)",LIB,25.09,25.62,25.11
"NCNN - Target: Vulkan GPU - Model: resnet50 (ms)",LIB,63.21,63.55,63.34
"NCNN - Target: Vulkan GPU - Model: yolov4-tiny (ms)",LIB,51.83,53.05,53.45
"NCNN - Target: Vulkan GPU - Model: squeezenet_ssd (ms)",LIB,40.27,41.27,42.10
"NCNN - Target: Vulkan GPU - Model: regnety_400m (ms)",LIB,20.70,20.75,20.88
"Timed HMMer Search - Pfam Database Search (sec)",LIB,137.774,137.939,137.903
"Timed FFmpeg Compilation - Time To Compile (sec)",LIB,145.942,146.295,146.978
"Build2 - Time To Compile (sec)",LIB,388.393,385.584,388.953
"Timed Eigen Compilation - Time To Compile (sec)",LIB,96.656,97.289,97.299
"Monkey Audio Encoding - WAV To APE (sec)",LIB,14.059,13.873,13.953
"Opus Codec Encoding - WAV To Opus Encode (sec)",LIB,9.132,9.133,9.113
"SQLite Speedtest - Timed Time - Size 1,000 (sec)",LIB,80.186,81.524,80.100
"WavPack Audio Encoding - WAV To WavPack (sec)",LIB,15.548,15.544,15.562