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
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

Timed Code Compilation 2 Tests
CPU Massive 6 Tests
Creator Workloads 4 Tests
HPC - High Performance Computing 6 Tests
Imaging 2 Tests
Machine Learning 3 Tests
Multi-Core 6 Tests
Programmer / Developer System Benchmarks 2 Tests
Server CPU Tests 5 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
Run 1
August 28 2020
  7 Hours, 53 Minutes
Run 2
August 29 2020
  11 Hours, 6 Minutes
Run 3
August 29 2020
  8 Hours, 1 Minute
Invert Hiding All Results Option
  9 Hours

Only show results where is faster than
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

Core i5 Laptop, "oneDNN 1.5 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",10.8462,10.6905,10.7686 "Run 2",10.298,10.2244,10.241 "Run 3",11.2484,10.3869,10.2578,10.3666,10.7872,11.4714,11.5023,11.6427,11.1586,10.9705,11.045,10.9144,11.2288,11.3895,11.6205 "Rodinia 3.1 - Test: OpenMP CFD Solver", Lower Results Are Better "Run 1",123.722,125.387,124.105 "Run 2",115.099,116.401,118.682 "Run 3",117.716,116.599,114.047 "oneDNN 1.5 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",1531.98,1530.57,1600.75 "Run 2",1544.61,1513.74,1538.18 "Run 3",1692.9,1533.4,1694.56,1622.84,1687.01,1560.55,1718.74,1558.73,1556.37,1682.98,1574.31,1559.61,1556.4,1742.98,1686.39 "oneDNN 1.5 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",34.643,35.0491,35.09 "Run 2",33.5915,33.8046,34.0428 "Run 3",35.207,35.3123,34.7667 "ECP-CANDLE 0.3 - Benchmark: P1B2", Lower Results Are Better "Run 1", "Run 2", "Run 3", "libavif avifenc 0.7.3 - Encoder Speed: 0", Lower Results Are Better "Run 1",494.899,495.893,477.634 "Run 2",478.091,476.841,476.022 "Run 3",476.439,477.788,477.199 "oneDNN 1.5 - Harness: IP Batch All - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",308.427,307.256,305.586 "Run 2",303.241,305.151,303.668 "Run 3",314.284,309.512,310.415 "oneDNN 1.5 - Harness: IP Batch 1D - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",23.2945,23.9319,22.6628 "Run 2",22.8504,22.8261,23.0288 "Run 3",22.6302,22.9285,22.7732 "oneDNN 1.5 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",808.908,806.074,817.231 "Run 2",800.207,824.306,809.463 "Run 3",821.667,833.72,817.687 "ECP-CANDLE 0.3 - Benchmark: P3B1", Lower Results Are Better "Run 1", "Run 2", "Run 3", "Timed Apache Compilation 2.4.41 - Time To Compile", Lower Results Are Better "Run 1",74.218,74.423,74.235 "Run 2",73.825,73.988,73.825 "Run 3",74.934,74.807,74.872 "Geekbench 5 - Test: CPU Single Core - Face Detection", Higher Results Are Better "Run 1",5.73,5.9,5.95 "Run 2",5.91,5.91,5.95 "Run 3",5.89,5.89,5.89 "Timed Linux Kernel Compilation 5.4 - Time To Compile", Lower Results Are Better "Run 1",569.99,566.582,566.738 "Run 2",566.754,562.6,563.739 "Run 3",572.357,568.791,568.808 "libavif avifenc 0.7.3 - Encoder Speed: 2", Lower Results Are Better "Run 1",284.74,284.326,285.758 "Run 2",283.273,283.171,283.536 "Run 3",282.595,282.714,282.674 "Rodinia 3.1 - Test: OpenMP HotSpot3D", Lower Results Are Better "Run 1",228.858,225.931,225.787 "Run 2",227.3,225.765,224.047 "Run 3",227.26,228.02,227.238 "Geekbench 5 - Test: CPU Multi Core - Face Detection", Higher Results Are Better "Run 1",12.7,12.7,12.7 "Run 2",12.6,12.6,12.7 "Run 3",12.7,12.7,12.6 "Geekbench 5 - Test: CPU Multi Core - Horizon Detection", Higher Results Are Better "Run 1",43.2,43.1,43.2 "Run 2",42.5,43,43.2 "Run 3",43.1,43.1,43.1 "Rodinia 3.1 - Test: OpenMP Streamcluster", Lower Results Are Better "Run 1",47.039,47.101,47.094 "Run 2",46.735,46.81,46.832 "Run 3",46.928,47.06,47.077 "oneDNN 1.5 - Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",37.809,37.8494,38.1635 "Run 2",37.7127,37.7581,37.7151 "Run 3",37.7665,37.8832,37.8761 "Darmstadt Automotive Parallel Heterogeneous Suite - Backend: OpenMP - Kernel: Points2Image", Higher Results Are Better "Run 1",14155.383646285,14223.199935521,14203.032347406 "Run 2",14184.430696054,14201.317882299,14205.487106153 "Run 3",14106.826293243,14114.259636511,14133.210217369 "Geekbench 5 - Test: CPU Single Core - Horizon Detection", Higher Results Are Better "Run 1",19.2,19.3,19.4 "Run 2",19.2,19.1,19.4 "Run 3",19.2,19.3,19.3 "Geekbench 5 - Test: CPU Single Core", Higher Results Are Better "Run 1",790,788,788 "Run 2",786,784,785 "Run 3",784,784,787 "oneDNN 1.5 - Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",27.9363,27.8817,27.7527 "Run 2",27.6987,27.7843,27.7834 "Run 3",27.6148,27.9809,27.7316 "Rodinia 3.1 - Test: OpenMP Leukocyte", Lower Results Are Better "Run 1",644.104,644.311,648.408 "Run 2",645.587,641.651,642.623 "Run 3",644.041,641.519,644.46 "TensorFlow Lite 2020-08-23 - Model: Mobilenet Quant", Lower Results Are Better "Run 1",944220,945113,953234 "Run 2",944584,945366,944824 "Run 3",944134,945033,944462 "Darmstadt Automotive Parallel Heterogeneous Suite - Backend: OpenMP - Kernel: Euclidean Cluster", Higher Results Are Better "Run 1",436.02116156037,437.19669479299,437.4166174573 "Run 2",435.04111138503,437.34009752684,437.35603697116 "Run 3",435.58433638726,435.65707979059,435.54955465058 "Geekbench 5 - Test: CPU Multi Core - Gaussian Blur", Higher Results Are Better "Run 1",77.4,77.9,78 "Run 2",77.9,77.6,77.4 "Run 3",77.5,78.1,77.7 "Geekbench 5 - Test: CPU Multi Core", Higher Results Are Better "Run 1",1636,1632,1632 "Run 2",1627,1634,1628 "Run 3", "Montage Astronomical Image Mosaic Engine 6.0 - Mosaic of M17, K band, 1.5 deg x 1.5 deg", Lower Results Are Better "Run 1",121.977,122.213,121.844 "Run 2",122.361,122.063,122.245 "Run 3",122.151,121.992,122.076 "ASTC Encoder 2.0 - Preset: Thorough", Lower Results Are Better "Run 1",196.26,195.96,196.38 "Run 2",196.82,196.16,196.34 "Run 3",196.28,196.11,196.13 "ASTC Encoder 2.0 - Preset: Medium", Lower Results Are Better "Run 1",28.61,28.64,28.64 "Run 2",28.62,28.66,28.68 "Run 3",28.61,28.63,28.62 "libavif avifenc 0.7.3 - Encoder Speed: 10", Lower Results Are Better "Run 1",14.738,14.752,14.713 "Run 2",14.698,14.774,14.748 "Run 3",14.757,14.765,14.725 "TensorFlow Lite 2020-08-23 - Model: Inception V4", Lower Results Are Better "Run 1",20825800,20823500,20834500 "Run 2",20830000,20828600,20886500 "Run 3",20837300,20881700,20825100 "ASTC Encoder 2.0 - Preset: Fast", Lower Results Are Better "Run 1",10.48,10.5,10.5 "Run 2",10.49,10.49,10.5 "Run 3",10.5,10.5,10.49 "ASTC Encoder 2.0 - Preset: Exhaustive", Lower Results Are Better "Run 1",1599.21,1598.83,1602.95 "Run 2",1601.89,1601.34,1601.08 "Run 3",1601.75,1602.89,1600.04 "Darmstadt Automotive Parallel Heterogeneous Suite - Backend: OpenMP - Kernel: NDT Mapping", Higher Results Are Better "Run 1",427.3747791897,428.06692112867,427.58492905653 "Run 2",426.95813675469,427.66721788219,428.0394367001 "Run 3",427.30781830872,429.29816904331,426.99763727974 "NAMD 2.14 - ATPase Simulation - 327,506 Atoms", Lower Results Are Better "Run 1",11.1019,11.1327,11.1554 "Run 2",11.1356,11.1297,11.1462 "Run 3",11.1164,11.1678,11.1038 "TensorFlow Lite 2020-08-23 - Model: NASNet Mobile", Lower Results Are Better "Run 1",1007600,1007080,1008060 "Run 2",1007150,1007830,1007980 "Run 3",1007910,1008410,1008310 "TensorFlow Lite 2020-08-23 - Model: SqueezeNet", Lower Results Are Better "Run 1",1439630,1439600,1440280 "Run 2",1439940,1441780,1439830 "Run 3",1439760,1439730,1439660 "libavif avifenc 0.7.3 - Encoder Speed: 8", Lower Results Are Better "Run 1",16.609,16.641,16.675 "Run 2",16.651,16.626,16.647 "Run 3",16.618,16.672,16.655 "TensorFlow Lite 2020-08-23 - Model: Inception ResNet V2", Lower Results Are Better "Run 1",18854400,18849800,18851800 "Run 2",18846900,18846700,18862300 "Run 3",18856800,18851900,18850900 "TensorFlow Lite 2020-08-23 - Model: Mobilenet Float", Lower Results Are Better "Run 1",976342,976860,976524 "Run 2",976508,976566,976813 "Run 3",976516,976546,976749 "Geekbench 5 - Test: CPU Single Core - Gaussian Blur", Higher Results Are Better "Run 1",34,34.3,30 "Run 2",31.4,32.6,32.4 "Run 3",31.3,31.5,32.4 "Rodinia 3.1 - Test: OpenMP LavaMD", Lower Results Are Better "Run 1",1838.636,1841.264,1836.601 "Run 2",2416.696,1989.739,1837.335,1839.259,1836.321,1838.81,1837.999,1836.734,1836.159 "Run 3",1838.811,1841.265,1844.466