rt

AMD Ryzen 7 3700X 8-Core testing with a MSI B450 TOMAHAWK MAX (MS-7C02) (3.50 BIOS) and NVIDIA GeForce RTX 2060 6GB on Microsoft Windows 10 Education Build 19042 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 2103210-GOUL-RT6058036
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
NVIDIA GeForce RTX 2060
March 21 2021
  3 Minutes

NVIDIA GeForce RTX 2060

Processor: AMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads), Motherboard: MSI B450 TOMAHAWK MAX (MS-7C02) (3.50 BIOS), Memory: 2 x 16384 MB 3600MHz F4-3600C17-16GTZSW, Disk: 224GB KINGSTON SA400S37240G + 298GB WDC WD3200BEVT-00A0RT0 + 932GB CT1000P1SSD8 + 3726GB WDC WD40EZRZ-75GXCB0, Graphics: NVIDIA GeForce RTX 2060 6GB, Network: Realtek PCIe GbE

OS: Microsoft Windows 10 Education Build 19042, Kernel: 10.0 (x86_64), Display Driver: 461.72 (27.21.14.6172), File-System: NTFS, Screen Resolution: 3840x2160

Java Notes: Java SE Runtime Environment (build 1.8.0_281-b09)
Python Notes: Python 3.7.1
Security Notes: __user pointer sanitization: Disabled + Retpoline: Full + IBPB: Always + STIBP: Enabled

rtOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads)MSI B450 TOMAHAWK MAX (MS-7C02) (3.50 BIOS)2 x 16384 MB 3600MHz F4-3600C17-16GTZSW224GB KINGSTON SA400S37240G + 298GB WDC WD3200BEVT-00A0RT0 + 932GB CT1000P1SSD8 + 3726GB WDC WD40EZRZ-75GXCB0NVIDIA GeForce RTX 2060 6GBRealtek PCIe GbEMicrosoft Windows 10 Education Build 1904210.0 (x86_64)461.72 (27.21.14.6172)NTFS3840x2160ProcessorMotherboardMemoryDiskGraphicsNetworkOSKernelDisplay DriverFile-SystemScreen ResolutionRt BenchmarksSystem Logs- Java SE Runtime Environment (build 1.8.0_281-b09)- Python 3.7.1- __user pointer sanitization: Disabled + Retpoline: Full + IBPB: Always + STIBP: Enabled

RealSR-NCNN

RealSR-NCNN is an NCNN neural network implementation of the RealSR project and accelerated using the Vulkan API. RealSR is the Real-World Super Resolution via Kernel Estimation and Noise Injection. NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. This test profile times how long it takes to increase the resolution of a sample image by a scale of 4x with Vulkan. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRealSR-NCNN 20200818Scale: 4x - TAA: NoNVIDIA GeForce RTX 20603691215SE +/- 0.13, N = 1212.37

NVIDIA GeForce RTX 2060

Processor: AMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads), Motherboard: MSI B450 TOMAHAWK MAX (MS-7C02) (3.50 BIOS), Memory: 2 x 16384 MB 3600MHz F4-3600C17-16GTZSW, Disk: 224GB KINGSTON SA400S37240G + 298GB WDC WD3200BEVT-00A0RT0 + 932GB CT1000P1SSD8 + 3726GB WDC WD40EZRZ-75GXCB0, Graphics: NVIDIA GeForce RTX 2060 6GB, Network: Realtek PCIe GbE

OS: Microsoft Windows 10 Education Build 19042, Kernel: 10.0 (x86_64), Display Driver: 461.72 (27.21.14.6172), File-System: NTFS, Screen Resolution: 3840x2160

Java Notes: Java SE Runtime Environment (build 1.8.0_281-b09)
Python Notes: Python 3.7.1
Security Notes: __user pointer sanitization: Disabled + Retpoline: Full + IBPB: Always + STIBP: Enabled

Testing initiated at 21 March 2021 03:02 by user goulxars.