bits
Processor: AMD EPYC 7543P 32-Core (4 Cores / 8 Threads), Motherboard: Blade Shadow ShadowM v2.0 (1.1.3 BIOS), Chipset: Intel 82G33/G31/P35/P31 + ICH9, Memory: 1 x 16GB RAM-2400MT/s Blade 2MEVPUH6DW9W57-PYL, Disk: 215GB QEMU HDD, Graphics: Red Hat QXL paravirtual graphic card 20GB, Network: Red Hat Virtio device
OS: Ubuntu 22.04, Kernel: 5.15.0-113-generic (x86_64), Display Server: X Server, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.131, Vulkan: 1.3.277, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1280x800, System Layer: KVM
Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: CPU Microcode: 0xa0011d1
Python Notes: Python 3.10.12
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 + retbleed: Not affected + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines; IBPB: conditional; IBRS_FW; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected
This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. 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.
This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.
Processor: AMD EPYC 7543P 32-Core (4 Cores / 8 Threads), Motherboard: Blade Shadow ShadowM v2.0 (1.1.3 BIOS), Chipset: Intel 82G33/G31/P35/P31 + ICH9, Memory: 1 x 16GB RAM-2400MT/s Blade 2MEVPUH6DW9W57-PYL, Disk: 215GB QEMU HDD, Graphics: Red Hat QXL paravirtual graphic card 20GB, Network: Red Hat Virtio device
OS: Ubuntu 22.04, Kernel: 5.15.0-113-generic (x86_64), Display Server: X Server, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.131, Vulkan: 1.3.277, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1280x800, System Layer: KVM
Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: CPU Microcode: 0xa0011d1
Python Notes: Python 3.10.12
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 + retbleed: Not affected + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines; IBPB: conditional; IBRS_FW; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 30 June 2024 20:27 by user ubuntu.