core-i5-4670-december Intel Core i5-4670 testing with a MSI B85M-P33 (MS-7817) v1.0 (V4.9 BIOS) and MSI Intel HD 4600 2GB on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Core i5-4670 @ 3.80GHz (4 Cores), Motherboard: MSI B85M-P33 (MS-7817) v1.0 (V4.9 BIOS), Chipset: Intel 4th Gen Core DRAM, Memory: 8GB, Disk: 2000GB Samsung SSD 860, Graphics: MSI Intel HD 4600 2GB (1200MHz), Audio: Intel Xeon E3-1200 v3/4th, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 1a: Processor: Intel Core i5-4670 @ 3.80GHz (4 Cores), Motherboard: MSI B85M-P33 (MS-7817) v1.0 (V4.9 BIOS), Chipset: Intel 4th Gen Core DRAM, Memory: 8GB, Disk: 2000GB Samsung SSD 860, Graphics: MSI Intel HD 4600 2GB (1200MHz), Audio: Intel Xeon E3-1200 v3/4th, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Core i5-4670 @ 3.80GHz (4 Cores), Motherboard: MSI B85M-P33 (MS-7817) v1.0 (V4.9 BIOS), Chipset: Intel 4th Gen Core DRAM, Memory: 8GB, Disk: 2000GB Samsung SSD 860, Graphics: MSI Intel HD 4600 2GB (1200MHz), Audio: Intel Xeon E3-1200 v3/4th, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Core i5-4670 @ 3.80GHz (4 Cores), Motherboard: MSI B85M-P33 (MS-7817) v1.0 (V4.9 BIOS), Chipset: Intel 4th Gen Core DRAM, Memory: 8GB, Disk: 2000GB Samsung SSD 860, Graphics: MSI Intel HD 4600 2GB (1200MHz), Audio: Intel Xeon E3-1200 v3/4th, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 4: Processor: Intel Core i5-4670 @ 3.80GHz (4 Cores), Motherboard: MSI B85M-P33 (MS-7817) v1.0 (V4.9 BIOS), Chipset: Intel 4th Gen Core DRAM, Memory: 8GB, Disk: 2000GB Samsung SSD 860, Graphics: MSI Intel HD 4600 2GB (1200MHz), Audio: Intel Xeon E3-1200 v3/4th, Monitor: DELL S2409W, Network: Realtek RTL8111/8168/8411 OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc7daily20201002-generic (x86_64) 20201001, Desktop: GNOME Shell 3.36.3, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8, Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 Timed HMMer Search 3.3.1 Pfam Database Search Seconds < Lower Is Better 1 . 140.06 |=================================================================== 2 . 140.51 |=================================================================== 3 . 140.26 |=================================================================== 4 . 140.24 |=================================================================== Timed MAFFT Alignment 7.471 Multiple Sequence Alignment - LSU RNA Seconds < Lower Is Better 1 . 12.62 |==================================================================== 2 . 12.52 |=================================================================== 3 . 12.44 |=================================================================== 4 . 12.46 |=================================================================== simdjson 0.7.1 Throughput Test: Kostya GB/s > Higher Is Better 1 . 0.59 |===================================================================== 2 . 0.59 |===================================================================== 3 . 0.59 |===================================================================== 4 . 0.59 |===================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 10.77 |=============================================================== 2 .. 11.24 |================================================================== 3 .. 11.08 |================================================================= 4 .. 11.40 |=================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 13.64 |===================================================== 2 .. 16.37 |================================================================ 3 .. 17.27 |=================================================================== 4 .. 17.10 |================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 6.28097 |================================================================ 2 .. 6.35599 |================================================================= 3 .. 6.37852 |================================================================= 4 .. 6.33031 |================================================================= oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 4.92392 |======================================================== 2 .. 5.65917 |================================================================ 3 .. 5.72441 |================================================================= 4 .. 5.73439 |================================================================= oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 30.30 |================================================================= 2 .. 31.44 |=================================================================== 3 .. 31.43 |=================================================================== 4 .. 31.37 |=================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 12.59 |================================================================== 2 .. 12.68 |=================================================================== 3 .. 12.69 |=================================================================== 4 .. 12.64 |=================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 17.55 |================================================================ 2 .. 17.69 |================================================================= 3 .. 17.73 |================================================================= 4 .. 18.30 |=================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 30.54 |================================================================= 2 .. 31.27 |=================================================================== 3 .. 31.28 |=================================================================== 4 .. 31.21 |=================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 15.20 |=================================================================== 2 .. 15.15 |=================================================================== 3 .. 15.16 |=================================================================== 4 .. 15.15 |=================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 13.89 |================================================================== 2 .. 14.03 |=================================================================== 3 .. 13.92 |================================================================== 4 .. 14.06 |=================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 8703.90 |=============================================================== 2 .. 8840.59 |================================================================ 3 .. 8933.93 |================================================================= 4 .. 8939.74 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 5460.15 |================================================================ 2 .. 5558.77 |================================================================= 3 .. 5557.65 |================================================================= 4 .. 5587.25 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 9031.76 |================================================================ 2 .. 9114.82 |================================================================ 3 .. 9232.94 |================================================================= 4 .. 9209.44 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 5407.38 |============================================================== 2 .. 5542.58 |================================================================ 3 .. 5624.84 |================================================================= 4 .. 5562.33 |================================================================ oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1a . 7.70013 |================================================================ 2 .. 7.81978 |================================================================= 3 .. 7.84505 |================================================================= 4 .. 7.82675 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1a . 9078.49 |================================================================ 2 .. 9159.95 |================================================================= 3 .. 9174.75 |================================================================= 4 .. 9171.84 |================================================================= oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1a . 5466.34 |=============================================================== 2 .. 5604.52 |================================================================ 3 .. 5563.07 |================================================================ 4 .. 5652.74 |================================================================= oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1a . 7.51442 |================================================================ 2 .. 7.51399 |================================================================ 3 .. 7.51466 |================================================================ 4 .. 7.57690 |================================================================= Coremark 1.0 CoreMark Size 666 - Iterations Per Second Iterations/Sec > Higher Is Better 1a . 95428.24 |================================================================ 2 .. 95542.96 |================================================================ 3 .. 94952.49 |=============================================================== 4 .. 95883.77 |================================================================ Timed FFmpeg Compilation 4.2.2 Time To Compile Seconds < Lower Is Better 1a . 163.12 |================================================================== 2 .. 162.23 |================================================================== 3 .. 162.08 |================================================================== 4 .. 162.66 |================================================================== Build2 0.13 Time To Compile Seconds < Lower Is Better 1a . 364.77 |================================================================= 2 .. 362.00 |================================================================= 3 .. 364.16 |================================================================= 4 .. 368.11 |================================================================== Node.js V8 Web Tooling Benchmark runs/s > Higher Is Better 1a . 9.70 |=================================================================== 2 .. 9.80 |==================================================================== 3 .. 9.84 |==================================================================== 4 .. 9.74 |=================================================================== SQLite Speedtest 3.30 Timed Time - Size 1,000 Seconds < Lower Is Better 1a . 78.85 |=================================================================== 2 .. 78.65 |=================================================================== 3 .. 79.19 |=================================================================== 4 .. 79.11 |=================================================================== NCNN 20201218 Target: CPU - Model: mobilenet ms < Lower Is Better 1a . 36.33 |================================================================== 2 .. 36.95 |=================================================================== 3 .. 36.87 |=================================================================== 4 .. 36.93 |=================================================================== NCNN 20201218 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 1a . 9.30 |================================================================== 2 .. 9.32 |================================================================== 3 .. 9.59 |==================================================================== 4 .. 9.58 |==================================================================== NCNN 20201218 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 1a . 8.10 |=================================================================== 2 .. 8.21 |==================================================================== 3 .. 8.20 |==================================================================== 4 .. 8.22 |==================================================================== NCNN 20201218 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 1a . 10.78 |=================================================================== 2 .. 10.78 |=================================================================== 3 .. 10.81 |=================================================================== 4 .. 10.79 |=================================================================== NCNN 20201218 Target: CPU - Model: mnasnet ms < Lower Is Better 1a . 7.93 |=================================================================== 2 .. 7.92 |=================================================================== 3 .. 8.06 |==================================================================== 4 .. 7.77 |================================================================== NCNN 20201218 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 1a . 13.24 |================================================================== 2 .. 13.41 |=================================================================== 3 .. 13.39 |=================================================================== 4 .. 13.37 |=================================================================== NCNN 20201218 Target: CPU - Model: blazeface ms < Lower Is Better 1a . 2.68 |==================================================================== 2 .. 2.68 |==================================================================== 3 .. 2.69 |==================================================================== 4 .. 2.68 |==================================================================== NCNN 20201218 Target: CPU - Model: googlenet ms < Lower Is Better 1a . 28.73 |=================================================================== 2 .. 28.59 |=================================================================== 3 .. 28.61 |=================================================================== 4 .. 28.60 |=================================================================== NCNN 20201218 Target: CPU - Model: vgg16 ms < Lower Is Better 1a . 134.38 |================================================================== 2 .. 134.04 |================================================================== 3 .. 133.93 |================================================================== 4 .. 134.48 |================================================================== NCNN 20201218 Target: CPU - Model: resnet18 ms < Lower Is Better 1a . 31.23 |=================================================================== 2 .. 31.34 |=================================================================== 3 .. 30.91 |================================================================== 4 .. 31.09 |================================================================== NCNN 20201218 Target: CPU - Model: alexnet ms < Lower Is Better 1a . 25.19 |================================================================== 2 .. 25.71 |=================================================================== 3 .. 25.53 |=================================================================== 4 .. 25.32 |================================================================== NCNN 20201218 Target: CPU - Model: resnet50 ms < Lower Is Better 1a . 63.23 |=================================================================== 2 .. 63.30 |=================================================================== 3 .. 62.56 |================================================================== 4 .. 62.54 |================================================================== NCNN 20201218 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 1a . 52.31 |================================================================= 2 .. 52.27 |================================================================= 3 .. 53.56 |=================================================================== 4 .. 52.44 |================================================================== NCNN 20201218 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 1a . 51.94 |=================================================================== 2 .. 52.25 |=================================================================== 3 .. 52.00 |=================================================================== 4 .. 51.40 |================================================================== NCNN 20201218 Target: CPU - Model: regnety_400m ms < Lower Is Better 1a . 16.15 |================================================================== 2 .. 16.20 |================================================================== 3 .. 16.51 |=================================================================== 4 .. 16.17 |==================================================================