5960X April Linux Benchmarks Intel Core i7-5960X testing with a Gigabyte X99-UD4-CF (F24c BIOS) and NVIDIA GeForce 6600 GT on Debian 11 via the Phoronix Test Suite. A: Processor: Intel Core i7-5960X @ 3.50GHz (8 Cores / 16 Threads), Motherboard: Gigabyte X99-UD4-CF (F24c BIOS), Chipset: Intel Xeon E7 v3/Xeon, Memory: 32GB, Disk: 120GB INTEL SSDSC2BW12, Graphics: NVIDIA GeForce 6600 GT, Audio: Realtek ALC1150, Network: Intel I218-V OS: Debian 11, Kernel: 5.10.0-10-amd64 (x86_64), Vulkan: 1.0.2, Compiler: GCC 10.2.1 20210110, File-System: ext4 B: Processor: Intel Core i7-5960X @ 3.50GHz (8 Cores / 16 Threads), Motherboard: Gigabyte X99-UD4-CF (F24c BIOS), Chipset: Intel Xeon E7 v3/Xeon, Memory: 32GB, Disk: 120GB INTEL SSDSC2BW12, Graphics: NVIDIA GeForce 6600 GT, Audio: Realtek ALC1150, Network: Intel I218-V OS: Debian 11, Kernel: 5.10.0-10-amd64 (x86_64), Vulkan: 1.0.2, Compiler: GCC 10.2.1 20210110, File-System: ext4 C: Processor: Intel Core i7-5960X @ 3.50GHz (8 Cores / 16 Threads), Motherboard: Gigabyte X99-UD4-CF (F24c BIOS), Chipset: Intel Xeon E7 v3/Xeon, Memory: 32GB, Disk: 120GB INTEL SSDSC2BW12, Graphics: NVIDIA GeForce 6600 GT, Audio: Realtek ALC1150, Network: Intel I218-V OS: Debian 11, Kernel: 5.10.0-10-amd64 (x86_64), Vulkan: 1.0.2, Compiler: GCC 10.2.1 20210110, File-System: ext4 SVT-AV1 1.0 Encoder Mode: Preset 4 - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 1.282 |==================================================================== B . 1.282 |==================================================================== C . 1.280 |==================================================================== SVT-AV1 1.0 Encoder Mode: Preset 8 - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 17.11 |==================================================================== B . 17.16 |==================================================================== C . 17.15 |==================================================================== SVT-AV1 1.0 Encoder Mode: Preset 10 - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 44.03 |==================================================================== B . 43.81 |==================================================================== C . 43.60 |=================================================================== SVT-AV1 1.0 Encoder Mode: Preset 12 - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 58.55 |============================================================== B . 60.27 |================================================================ C . 64.26 |==================================================================== SVT-AV1 1.0 Encoder Mode: Preset 4 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 3.740 |==================================================================== B . 3.723 |==================================================================== C . 3.719 |==================================================================== SVT-AV1 1.0 Encoder Mode: Preset 8 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 56.28 |==================================================================== B . 56.45 |==================================================================== C . 56.41 |==================================================================== SVT-AV1 1.0 Encoder Mode: Preset 10 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 107.49 |================================================================= B . 110.94 |=================================================================== C . 110.20 |=================================================================== SVT-AV1 1.0 Encoder Mode: Preset 12 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 186.50 |================================================================== B . 190.13 |=================================================================== C . 183.30 |================================================================= libavif avifenc 0.10 Encoder Speed: 0 Seconds < Lower Is Better A . 265.61 |=================================================================== B . 265.20 |=================================================================== C . 264.88 |=================================================================== libavif avifenc 0.10 Encoder Speed: 2 Seconds < Lower Is Better A . 125.44 |=================================================================== B . 125.51 |=================================================================== C . 125.31 |=================================================================== libavif avifenc 0.10 Encoder Speed: 6 Seconds < Lower Is Better A . 16.13 |=================================================================== B . 16.33 |==================================================================== C . 16.31 |==================================================================== libavif avifenc 0.10 Encoder Speed: 6, Lossless Seconds < Lower Is Better A . 20.98 |==================================================================== B . 20.70 |=================================================================== C . 20.57 |=================================================================== libavif avifenc 0.10 Encoder Speed: 10, Lossless Seconds < Lower Is Better A . 10.28 |==================================================================== B . 10.22 |==================================================================== C . 10.27 |==================================================================== oneDNN 2.6 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 4.09387 |================================================================= B . 4.11187 |================================================================== C . 4.14144 |================================================================== oneDNN 2.6 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 6.66146 |================================================================== B . 6.61348 |================================================================== C . 6.64132 |================================================================== oneDNN 2.6 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2.82924 |================================================================== B . 2.74499 |================================================================ C . 2.74866 |================================================================ oneDNN 2.6 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2.72288 |================================================================== B . 2.36463 |========================================================= C . 2.34867 |========================================================= oneDNN 2.6 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 10.63 |==================================================================== B . 10.62 |==================================================================== C . 10.64 |==================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 7.71578 |================================================================ B . 7.93496 |================================================================== C . 7.73184 |================================================================ oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 9.76576 |================================================================== B . 9.09280 |============================================================= C . 9.08975 |============================================================= oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 9.74527 |================================================================= B . 9.70341 |================================================================= C . 9.86792 |================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3.98471 |================================================================== B . 3.98267 |================================================================== C . 4.00904 |================================================================== oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 5.71111 |================================================================== B . 5.73810 |================================================================== C . 5.70909 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 4496.63 |================================================================== B . 4333.67 |================================================================ C . 4339.42 |================================================================ oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 2374.69 |=============================================================== B . 2481.80 |================================================================== C . 2463.98 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 4412.77 |================================================================= B . 4409.97 |================================================================= C . 4456.62 |================================================================== oneDNN 2.6 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 2398.00 |================================================================== B . 2368.18 |================================================================= C . 2388.70 |================================================================== oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 2.71059 |======================================================= B . 3.25069 |================================================================== C . 2.97823 |============================================================ oneDNN 2.6 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 4431.23 |================================================================ B . 4520.63 |================================================================= C . 4563.87 |================================================================== oneDNN 2.6 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 2408.09 |================================================================= B . 2441.72 |================================================================== C . 2397.52 |================================================================= oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 1.70529 |================================================================== B . 1.69885 |================================================================== C . 1.69171 |================================================================= oneDNN 2.6 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better Java JMH Throughput Ops/s > Higher Is Better A . 18510040175.20 |=========================================================== B . 18481496507.55 |=========================================================== C . 18504082119.82 |=========================================================== ONNX Runtime 1.11 Model: GPT-2 - Device: CPU - Executor: Standard Inferences Per Minute > Higher Is Better A . 5650 |=========================================================== B . 5650 |=========================================================== C . 6628 |===================================================================== ONNX Runtime 1.11 Model: yolov4 - Device: CPU - Executor: Standard Inferences Per Minute > Higher Is Better A . 272 |============================================== B . 414 |====================================================================== C . 409 |===================================================================== ONNX Runtime 1.11 Model: bertsquad-12 - Device: CPU - Executor: Standard Inferences Per Minute > Higher Is Better A . 439 |============================================= B . 442 |============================================= C . 685 |====================================================================== ONNX Runtime 1.11 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard Inferences Per Minute > Higher Is Better A . 44 |=========================================== B . 72 |======================================================================= C . 71 |====================================================================== ONNX Runtime 1.11 Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard Inferences Per Minute > Higher Is Better A . 1274 |===================================================================== B . 939 |=================================================== C . 1272 |===================================================================== ONNX Runtime 1.11 Model: super-resolution-10 - Device: CPU - Executor: Standard Inferences Per Minute > Higher Is Better A . 2506 |========================================= B . 4199 |===================================================================== C . 4163 |====================================================================