ARMv8 Cortex-A76 testing with a Raspberry Pi 5 Model B Rev 1.0 and V3D 7.1.7 8GB on Ubuntu 24.04 via the Phoronix Test Suite.
Processor: ARMv8 Cortex-A76 @ 2.40GHz (4 Cores), Motherboard: Raspberry Pi 5 Model B Rev 1.0, Chipset: Broadcom BCM2712, Memory: 8GB, Disk: 500GB KINGSTON SNV2S500G, Graphics: V3D 7.1.7 8GB, Monitor: PA247CV, Network: Raspberry Pi RP1 PCIe 2.0 South Bridge
OS: Ubuntu 24.04, Kernel: 6.8.0-1012-raspi (aarch64), Desktop: KDE Plasma 5.27.11, Display Server: X Server 1.21.1.11, OpenGL: 3.1 Mesa 24.0.9-0ubuntu0.1, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: Scaling Governor: cpufreq-dt ondemand
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Mitigation of CSV2 BHB + srbds: Not affected + tsx_async_abort: Not affected
Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU
ARMv8 Cortex-A76: The test quit with a non-zero exit status.
Test: Meta-Llama-3-8B-Instruct.F16 - Acceleration: CPU
ARMv8 Cortex-A76: The test quit with a non-zero exit status.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
Test: Graph API
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: AbsExact error: G-API output and reference output matrixes are not bitexact equal.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
This test times how long it takes to build the FFmpeg multimedia library. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.
NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
Test: Meta-Llama-3-8B-Instruct.F16 - Acceleration: GPU AUTO
ARMv8 Cortex-A76: The test quit with a non-zero exit status.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
This test times how long it takes to build the Apache HTTPD web server. Learn more via the OpenBenchmarking.org test page.
Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: GPU AUTO
ARMv8 Cortex-A76: The test quit with a non-zero exit status.
This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.
Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.
Device: CPU - Batch Size: 256 - Model: ResNet-152
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 64 - Model: ResNet-152
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 512 - Model: ResNet-152
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 32 - Model: ResNet-152
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 16 - Model: ResNet-152
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 1 - Model: ResNet-152
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 1 - Model: ResNet-50
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 256 - Model: ResNet-50
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 512 - Model: ResNet-50
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 16 - Model: ResNet-50
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 64 - Model: ResNet-50
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
Device: CPU - Batch Size: 32 - Model: ResNet-50
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: KeyError: 'brand_raw'
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.
Benchmark: scikit_svm
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'
Benchmark: scikit_qda
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'
Benchmark: scikit_linearridgeregression
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'
Benchmark: scikit_ica
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'imp'
Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.
Detector: Bayesian Changepoint
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'
Detector: Contextual Anomaly Detector OSE
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'
Detector: KNN CAD
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'
Detector: Earthgecko Skyline
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'
AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'
Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.
Detector: Relative Entropy
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'
Detector: Windowed Gaussian
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'pandas'
Backend: Eigen
ARMv8 Cortex-A76: The test run did not produce a result.
Backend: BLAS
ARMv8 Cortex-A76: The test run did not produce a result.
Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "resnet100/resnet100.onnx" failed: No such file or directory
Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "FasterRCNN-12-int8/FasterRCNN-12-int8.onnx" failed: No such file or directory
Model: GPT-2 - Device: CPU - Executor: Parallel
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "GPT2/model.onnx" failed: No such file or directory
Model: bertsquad-12 - Device: CPU - Executor: Parallel
ARMv8 Cortex-A76: The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const std::filesystem::__cxx11::path&) open file "bertsquad-12/bertsquad-12.onnx" failed: No such file or directory
Processor: ARMv8 Cortex-A76 @ 2.40GHz (4 Cores), Motherboard: Raspberry Pi 5 Model B Rev 1.0, Chipset: Broadcom BCM2712, Memory: 8GB, Disk: 500GB KINGSTON SNV2S500G, Graphics: V3D 7.1.7 8GB, Monitor: PA247CV, Network: Raspberry Pi RP1 PCIe 2.0 South Bridge
OS: Ubuntu 24.04, Kernel: 6.8.0-1012-raspi (aarch64), Desktop: KDE Plasma 5.27.11, Display Server: X Server 1.21.1.11, OpenGL: 3.1 Mesa 24.0.9-0ubuntu0.1, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: Scaling Governor: cpufreq-dt ondemand
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of __user pointer sanitization + spectre_v2: Mitigation of CSV2 BHB + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 27 September 2024 21:29 by user bench.