Jetson AGX Xavier vs. Jetson TX2 TensorRT

NVIDIA Jetson TensorRT inference benchmarks by Michael Larabel for a future article on Phoronix.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 1812240-SP-XAVIER80657
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Jetson AGX Xavier
December 23 2018
  6 Hours, 30 Minutes
Jetson TX2
December 24 2018
  6 Hours, 26 Minutes
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  6 Hours, 28 Minutes
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Jetson AGX Xavier

Processor: ARMv8 rev 0 @ 2.27GHz (8 Cores), Motherboard: jetson-xavier, Memory: 16384MB, Disk: 31GB HBG4a2, Graphics: NVIDIA Tegra Xavier, Monitor: ASUS VP28U

OS: Ubuntu 18.04, Kernel: 4.9.108-tegra (aarch64), Desktop: Unity 7.5.0, Display Server: X Server 1.19.6, Display Driver: NVIDIA 31.0.2, OpenGL: 4.6.0, Vulkan: 1.1.76, Compiler: GCC 7.3.0 + CUDA 10.0, File-System: ext4, Screen Resolution: 1920x1080

Processor Notes: Scaling Governor: tegra_cpufreq schedutil

Jetson TX2

Processor: ARMv8 rev 3 @ 2.04GHz (4 Cores / 6 Threads), Motherboard: quill, Memory: 8192MB, Disk: 31GB 032G34, Graphics: NVIDIA Tegra X2, Monitor: VE228

OS: Ubuntu 16.04, Kernel: 4.4.38-tegra (aarch64), Desktop: Unity 7.4.0, Display Server: X Server 1.18.4, Display Driver: NVIDIA 28.2.1, OpenGL: 4.5.0, Compiler: GCC 5.4.0 20160609 + CUDA 9.0, File-System: ext4, Screen Resolution: 1920x1080

Jetson AGX Xavier vs. Jetson TX2 TensorRTProcessorMotherboardMemoryDiskGraphicsMonitorOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen ResolutionJetson AGX XavierJetson TX2ARMv8 rev 0 @ 2.27GHz (8 Cores)jetson-xavier16384MB31GB HBG4a2NVIDIA Tegra XavierASUS VP28UUbuntu 18.044.9.108-tegra (aarch64)Unity 7.5.0X Server 1.19.6NVIDIA 31.0.24.6.01.1.76GCC 7.3.0 + CUDA 10.0ext41920x1080ARMv8 rev 3 @ 2.04GHz (4 Cores / 6 Threads)quill8192MB31GB 032G34NVIDIA Tegra X2VE228Ubuntu 16.044.4.38-tegra (aarch64)Unity 7.4.0X Server 1.18.4NVIDIA 28.2.14.5.0GCC 5.4.0 20160609 + CUDA 9.0OpenBenchmarking.orgProcessor Details- Scaling Governor: tegra_cpufreq schedutil

Jetson AGX Xavier vs. Jetson TX2 ComparisonPhoronix Test SuiteBaseline+585.7%+585.7%+1171.4%+1171.4%+1757.1%+1757.1%GoogleNet - INT8 - 8796.6%AlexNet - INT8 - 32768.4%AlexNet - INT8 - 16628.3%VGG19 - FP16 - 8582.6%VGG19 - FP16 - 32581.5%VGG16 - FP16 - 32562.6%VGG19 - FP16 - 4549.9%VGG16 - FP16 - 8540.4%ResNet152 - FP16 - 8539.7%VGG16 - FP16 - 16527.7%VGG19 - FP16 - 16521.4%ResNet152 - FP16 - 4515.4%VGG16 - FP16 - 4505.1%ResNet152 - FP16 - 32505.1%ResNet50 - FP16 - 8487.9%ResNet50 - FP16 - 4479.9%GoogleNet - INT8 - 4471.9%ResNet50 - FP16 - 16459.4%ResNet152 - FP16 - 16458.8%ResNet50 - FP16 - 32457.3%AlexNet - INT8 - 8457.2%AlexNet - INT8 - 4444.7%GoogleNet - FP16 - 8335.9%AlexNet - FP16 - 8315.7%GoogleNet - FP16 - 32315.7%AlexNet - FP16 - 32302.5%GoogleNet - FP16 - 16293.6%AlexNet - FP16 - 16287.8%AlexNet - FP16 - 4206.1%VGG19 - INT8 - 322342.6%VGG16 - INT8 - 322164.5%VGG19 - INT8 - 162110.5%ResNet152 - INT8 - 322100.5%ResNet152 - INT8 - 162043.6%ResNet152 - INT8 - 81989.4%GoogleNet - FP16 - 4170.3%ResNet50 - INT8 - 321892.4%ResNet152 - INT8 - 41849.2%ResNet50 - INT8 - 161834.5%VGG16 - INT8 - 161760.1%VGG19 - INT8 - 81753.6%ResNet50 - INT8 - 81749.3%VGG19 - INT8 - 41693.2%ResNet50 - INT8 - 41617.5%VGG16 - INT8 - 81615.4%VGG16 - INT8 - 41494.2%GoogleNet - INT8 - 321147.7%GoogleNet - INT8 - 16972%NVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceNVIDIA TensorRT InferenceJetson AGX XavierJetson TX2

Jetson AGX Xavier vs. Jetson TX2 TensorRTtensorrt-inference: ResNet50 - INT8 - 4tensorrt-inference: GoogleNet - FP16 - 4tensorrt-inference: ResNet152 - INT8 - 16tensorrt-inference: ResNet50 - FP16 - 4tensorrt-inference: ResNet50 - INT8 - 16tensorrt-inference: GoogleNet - INT8 - 16tensorrt-inference: VGG19 - INT8 - 4tensorrt-inference: ResNet152 - INT8 - 4tensorrt-inference: VGG19 - FP16 - 4tensorrt-inference: VGG19 - FP16 - 8tensorrt-inference: VGG16 - INT8 - 8tensorrt-inference: VGG16 - INT8 - 4tensorrt-inference: VGG16 - FP16 - 8tensorrt-inference: GoogleNet - INT8 - 4tensorrt-inference: VGG19 - INT8 - 8tensorrt-inference: ResNet152 - FP16 - 4tensorrt-inference: VGG16 - FP16 - 16tensorrt-inference: ResNet50 - FP16 - 16tensorrt-inference: VGG16 - FP16 - 32tensorrt-inference: GoogleNet - FP16 - 16tensorrt-inference: VGG16 - INT8 - 16tensorrt-inference: ResNet152 - FP16 - 16tensorrt-inference: VGG16 - INT8 - 32tensorrt-inference: VGG16 - FP16 - 4tensorrt-inference: VGG19 - FP16 - 16tensorrt-inference: ResNet50 - FP16 - 8tensorrt-inference: VGG19 - FP16 - 32tensorrt-inference: ResNet50 - INT8 - 8tensorrt-inference: VGG19 - INT8 - 16tensorrt-inference: GoogleNet - FP16 - 8tensorrt-inference: VGG19 - INT8 - 32tensorrt-inference: GoogleNet - INT8 - 8tensorrt-inference: AlexNet - FP16 - 4tensorrt-inference: ResNet152 - FP16 - 8tensorrt-inference: AlexNet - FP16 - 8tensorrt-inference: ResNet152 - INT8 - 8tensorrt-inference: AlexNet - INT8 - 4tensorrt-inference: ResNet50 - FP16 - 32tensorrt-inference: AlexNet - INT8 - 8tensorrt-inference: ResNet50 - INT8 - 32tensorrt-inference: AlexNet - FP16 - 16tensorrt-inference: GoogleNet - FP16 - 32tensorrt-inference: AlexNet - FP16 - 32tensorrt-inference: GoogleNet - INT8 - 32tensorrt-inference: AlexNet - INT8 - 16tensorrt-inference: ResNet152 - FP16 - 32tensorrt-inference: AlexNet - INT8 - 32tensorrt-inference: ResNet152 - INT8 - 32glmark2: 1920 x 1080Jetson AGX XavierJetson TX2865.46546445.22542.801106.131340262.17350.28172.15184.43341.20286.64215.68652296.94219.08228.75593246.76858381.33224.60449.96195.45180.03582.36201.53944.46362.08863390.571049799234.841247407.0197561312371184.501435956190016221879253.342666485.22286150.3920220.7793.6157.1812514.6217.9726.4927.0219.8917.9833.6811416.0235.6036.4410637.2421820.5040.1919.8732.3028.9799.0529.5751.0716.3819815.9911726136.7130019.4817911022259.4537023047213025841.8730722.05OpenBenchmarking.org

NVIDIA TensorRT Inference

This test profile uses any existing system installation of NVIDIA TensorRT for carrying out inference benchmarks with various neural networks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 4Jetson AGX XavierJetson TX22004006008001000SE +/- 14.20, N = 3SE +/- 0.64, N = 3865.4650.39

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 4Jetson AGX XavierJetson TX2120240360480600SE +/- 96.56, N = 9SE +/- 0.88, N = 3546202

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 16Jetson AGX XavierJetson TX2100200300400500SE +/- 4.04, N = 3SE +/- 0.09, N = 3445.2220.77

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 4Jetson AGX XavierJetson TX2120240360480600SE +/- 0.39, N = 3SE +/- 1.46, N = 3542.8093.61

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 16Jetson AGX XavierJetson TX22004006008001000SE +/- 11.53, N = 12SE +/- 0.10, N = 31106.1357.18

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 16Jetson AGX XavierJetson TX230060090012001500SE +/- 152.29, N = 9SE +/- 1.16, N = 31340125

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 4Jetson AGX XavierJetson TX260120180240300SE +/- 0.96, N = 3SE +/- 0.10, N = 3262.1714.62

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 4Jetson AGX XavierJetson TX280160240320400SE +/- 5.48, N = 3SE +/- 0.19, N = 3350.2817.97

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 4Jetson AGX XavierJetson TX24080120160200SE +/- 1.25, N = 3SE +/- 0.15, N = 3172.1526.49

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 8Jetson AGX XavierJetson TX24080120160200SE +/- 2.36, N = 3SE +/- 0.14, N = 3184.4327.02

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 8Jetson AGX XavierJetson TX270140210280350SE +/- 1.08, N = 3SE +/- 0.05, N = 3341.2019.89

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 4Jetson AGX XavierJetson TX260120180240300SE +/- 3.98, N = 3SE +/- 0.06, N = 3286.6417.98

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 8Jetson AGX XavierJetson TX250100150200250SE +/- 3.36, N = 5SE +/- 0.24, N = 3215.6833.68

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 4Jetson AGX XavierJetson TX2140280420560700SE +/- 140.60, N = 12SE +/- 2.00, N = 3652114

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 8Jetson AGX XavierJetson TX260120180240300SE +/- 1.42, N = 3SE +/- 0.06, N = 3296.9416.02

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 4Jetson AGX XavierJetson TX250100150200250SE +/- 3.18, N = 3SE +/- 0.44, N = 3219.0835.60

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 16Jetson AGX XavierJetson TX250100150200250SE +/- 1.63, N = 3SE +/- 0.11, N = 3228.7536.44

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 16Jetson AGX XavierJetson TX2130260390520650SE +/- 7.03, N = 3SE +/- 0.59, N = 3593106

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 32Jetson AGX XavierJetson TX250100150200250SE +/- 0.17, N = 3SE +/- 0.14, N = 3246.7637.24

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 16Jetson AGX XavierJetson TX22004006008001000SE +/- 55.00, N = 9SE +/- 3.60, N = 3858218

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 16Jetson AGX XavierJetson TX280160240320400SE +/- 10.09, N = 12SE +/- 0.03, N = 3381.3320.50

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 16Jetson AGX XavierJetson TX250100150200250SE +/- 15.50, N = 9SE +/- 0.17, N = 3224.6040.19

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 32Jetson AGX XavierJetson TX2100200300400500SE +/- 4.97, N = 10SE +/- 0.03, N = 3449.9619.87

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 4Jetson AGX XavierJetson TX24080120160200SE +/- 3.17, N = 12SE +/- 0.30, N = 3195.4532.30

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 16Jetson AGX XavierJetson TX24080120160200SE +/- 11.67, N = 10SE +/- 0.11, N = 3180.0328.97

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 8Jetson AGX XavierJetson TX2130260390520650SE +/- 0.24, N = 3SE +/- 1.23, N = 3582.3699.05

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 32Jetson AGX XavierJetson TX24080120160200SE +/- 1.68, N = 3SE +/- 0.09, N = 3201.5329.57

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 8Jetson AGX XavierJetson TX22004006008001000SE +/- 40.28, N = 12SE +/- 0.54, N = 3944.4651.07

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 16Jetson AGX XavierJetson TX280160240320400SE +/- 0.66, N = 3SE +/- 0.02, N = 3362.0816.38

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 8Jetson AGX XavierJetson TX22004006008001000SE +/- 14.25, N = 12SE +/- 3.70, N = 3863198

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 32Jetson AGX XavierJetson TX280160240320400SE +/- 1.67, N = 3SE +/- 0.03, N = 3390.5715.99

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 8Jetson AGX XavierJetson TX22004006008001000SE +/- 121.56, N = 10SE +/- 2.12, N = 31049117

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 4Jetson AGX XavierJetson TX22004006008001000SE +/- 97.79, N = 9SE +/- 5.89, N = 12799261

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 8Jetson AGX XavierJetson TX250100150200250SE +/- 0.36, N = 3SE +/- 0.67, N = 9234.8436.71

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 8Jetson AGX XavierJetson TX230060090012001500SE +/- 45.66, N = 12SE +/- 7.60, N = 121247300

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 8Jetson AGX XavierJetson TX290180270360450SE +/- 6.98, N = 3SE +/- 0.27, N = 3407.0119.48

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 4Jetson AGX XavierJetson TX22004006008001000SE +/- 55.83, N = 12SE +/- 2.69, N = 4975179

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 32Jetson AGX XavierJetson TX2130260390520650SE +/- 9.12, N = 3SE +/- 1.29, N = 3613110

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 8Jetson AGX XavierJetson TX230060090012001500SE +/- 99.61, N = 12SE +/- 3.23, N = 31237222

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 32Jetson AGX XavierJetson TX230060090012001500SE +/- 6.54, N = 3SE +/- 0.19, N = 31184.5059.45

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 16Jetson AGX XavierJetson TX230060090012001500SE +/- 89.56, N = 9SE +/- 6.40, N = 121435370

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 32Jetson AGX XavierJetson TX22004006008001000SE +/- 14.46, N = 12SE +/- 3.59, N = 3956230

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 32Jetson AGX XavierJetson TX2400800120016002000SE +/- 23.33, N = 3SE +/- 6.74, N = 31900472

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 32Jetson AGX XavierJetson TX230060090012001500SE +/- 5.04, N = 3SE +/- 0.91, N = 31622130

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 16Jetson AGX XavierJetson TX2400800120016002000SE +/- 91.41, N = 12SE +/- 3.45, N = 31879258

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 32Jetson AGX XavierJetson TX260120180240300SE +/- 2.84, N = 3SE +/- 0.14, N = 3253.3441.87

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 32Jetson AGX XavierJetson TX26001200180024003000SE +/- 248.85, N = 9SE +/- 0.88, N = 32666307

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 32Jetson AGX XavierJetson TX2110220330440550SE +/- 1.47, N = 3SE +/- 0.03, N = 3485.2222.05

GLmark2

This is a test of any system-installed GLMark2 OpenGL benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterGLmark2Resolution: 1920 x 1080Jetson AGX Xavier60012001800240030002861

Jetson AGX Xavier

Processor: ARMv8 rev 0 @ 2.27GHz (8 Cores), Motherboard: jetson-xavier, Memory: 16384MB, Disk: 31GB HBG4a2, Graphics: NVIDIA Tegra Xavier, Monitor: ASUS VP28U

OS: Ubuntu 18.04, Kernel: 4.9.108-tegra (aarch64), Desktop: Unity 7.5.0, Display Server: X Server 1.19.6, Display Driver: NVIDIA 31.0.2, OpenGL: 4.6.0, Vulkan: 1.1.76, Compiler: GCC 7.3.0 + CUDA 10.0, File-System: ext4, Screen Resolution: 1920x1080

Processor Notes: Scaling Governor: tegra_cpufreq schedutil

Testing initiated at 23 December 2018 16:20 by user system.

Jetson TX2

Processor: ARMv8 rev 3 @ 2.04GHz (4 Cores / 6 Threads), Motherboard: quill, Memory: 8192MB, Disk: 31GB 032G34, Graphics: NVIDIA Tegra X2, Monitor: VE228

OS: Ubuntu 16.04, Kernel: 4.4.38-tegra (aarch64), Desktop: Unity 7.4.0, Display Server: X Server 1.18.4, Display Driver: NVIDIA 28.2.1, OpenGL: 4.5.0, Compiler: GCC 5.4.0 20160609 + CUDA 9.0, File-System: ext4, Screen Resolution: 1920x1080

Processor Notes: Scaling Governor: tegra_cpufreq schedutil

Testing initiated at 24 December 2018 11:20 by user NVIDIA.