Core i5 Laptop

Intel Core i5-5300U testing with a HP 2216 (M71 Ver. 01.27 BIOS) and Intel HD 5500 3GB on Ubuntu 20.04 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 2008300-FI-COREI5LAP35
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August 28 2020
  7 Hours, 53 Minutes
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August 29 2020
  11 Hours, 6 Minutes
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August 29 2020
  8 Hours, 1 Minute
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Core i5 Laptop Suite 1.0.0 System Test suite extracted from Core i5 Laptop. pts/rodinia-1.3.1 OMP_LAVAMD Test: OpenMP LavaMD pts/astcenc-1.0.0 -exhaustive Preset: Exhaustive pts/ecp-candle-1.0.1 P3B1 Benchmark: P3B1 pts/rodinia-1.3.1 OMP_LEUKOCYTE Test: OpenMP Leukocyte pts/build-linux-kernel-1.10.2 Time To Compile pts/namd-1.2.1 ATPase Simulation - 327,506 Atoms pts/avifenc-1.0.0 -s 0 Encoder Speed: 0 pts/avifenc-1.0.0 -s 2 Encoder Speed: 2 pts/rodinia-1.3.1 OMP_HOTSPOT3D Test: OpenMP HotSpot3D pts/astcenc-1.0.0 -thorough Preset: Thorough pts/tensorflow-lite-1.0.0 --graph=inception_v4.tflite Model: Inception V4 pts/tensorflow-lite-1.0.0 --graph=inception_resnet_v2.tflite Model: Inception ResNet V2 pts/daphne-1.0.0 OpenMP points2image Backend: OpenMP - Kernel: Points2Image pts/montage-1.0.0 Mosaic of M17, K band, 1.5 deg x 1.5 deg pts/rodinia-1.3.1 OMP_CFD Test: OpenMP CFD Solver pts/geekbench-1.2.0 --multi-core Test: CPU Multi Core - Horizon Detection pts/geekbench-1.2.0 --multi-core Test: CPU Multi Core - Face Detection pts/geekbench-1.2.0 --multi-core Test: CPU Multi Core - Gaussian Blur pts/geekbench-1.2.0 --multi-core Test: CPU Multi Core pts/onednn-1.5.0 --rnn --batch=inputs/rnn/rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/build-apache-1.6.1 Time To Compile pts/tensorflow-lite-1.0.0 --graph=squeezenet.tflite Model: SqueezeNet pts/geekbench-1.2.0 --single-core Test: CPU Single Core - Horizon Detection pts/geekbench-1.2.0 --single-core Test: CPU Single Core - Face Detection pts/geekbench-1.2.0 --single-core Test: CPU Single Core - Gaussian Blur pts/geekbench-1.2.0 --single-core Test: CPU Single Core pts/tensorflow-lite-1.0.0 --graph=mobilenet_v1_1.0_224.tflite Model: Mobilenet Float pts/tensorflow-lite-1.0.0 --graph=nasnet_mobile.tflite Model: NASNet Mobile pts/tensorflow-lite-1.0.0 --graph=mobilenet_v1_1.0_224_quant.tflite Model: Mobilenet Quant pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_all --cfg=f32 --engine=cpu Harness: IP Batch All - Data Type: f32 - Engine: CPU pts/daphne-1.0.0 OpenMP euclidean_cluster Backend: OpenMP - Kernel: Euclidean Cluster pts/rodinia-1.3.1 OMP_STREAMCLUSTER Test: OpenMP Streamcluster pts/daphne-1.0.0 OpenMP ndt_mapping Backend: OpenMP - Kernel: NDT Mapping pts/onednn-1.5.0 --rnn --batch=inputs/rnn/rnn_inference --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/astcenc-1.0.0 -medium Preset: Medium pts/onednn-1.5.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/ecp-candle-1.0.1 P1B2 Benchmark: P1B2 pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU pts/avifenc-1.0.0 -s 8 Encoder Speed: 8 pts/astcenc-1.0.0 -fast Preset: Fast pts/onednn-1.5.0 --ip --batch=inputs/ip/ip_1d --cfg=f32 --engine=cpu Harness: IP Batch 1D - Data Type: f32 - Engine: CPU pts/avifenc-1.0.0 -s 10 Encoder Speed: 10 pts/onednn-1.5.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.5.0 --deconv --batch=inputs/deconv/deconv_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU