9900K Linux

Intel Core i9-9900K testing with a ASRock Z390M Pro4 (P4.20 BIOS) and Intel UHD 630 3GB on Ubuntu 20.04 via the Phoronix Test Suite.

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Performance Per
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Date
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  Test
  Duration
Core i9 9900K
September 06 2020
  2 Hours, 23 Minutes
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9900K LinuxOpenBenchmarking.orgPhoronix Test SuiteIntel Core i9-9900K @ 5.00GHz (8 Cores / 16 Threads)ASRock Z390M Pro4 (P4.20 BIOS)Intel Cannon Lake PCH16GB240GB Corsair Force MP510Intel UHD 630 3GB (1200MHz)Realtek ALC892G237HLIntel I219-VUbuntu 20.045.9.0-050900rc1daily20200819-generic (x86_64) 20200818GNOME Shell 3.36.4X Server 1.20.8modesetting 1.20.84.6 Mesa 20.0.4OpenCL 2.1GCC 9.3.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLOpenCLCompilerFile-SystemScreen Resolution9900K Linux BenchmarksSystem Logs- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none,hsa --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - Scaling Governor: intel_pstate powersave - CPU Microcode: 0xd6- Python 2.7.18rc1 + Python 3.8.2- itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Vulnerable; SMT vulnerable + meltdown: Not affected + spec_store_bypass: Vulnerable + spectre_v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers + spectre_v2: Vulnerable IBPB: disabled STIBP: disabled + srbds: Vulnerable + tsx_async_abort: Vulnerable

9900K Linuxai-benchmark: Device AI Scoreai-benchmark: Device Training Scoreai-benchmark: Device Inference Scoreecp-candle: P3B1perf-bench: Epoll Waittensorflow-lite: Inception V4perf-bench: Futex Lock-Pitensorflow-lite: Inception ResNet V2ecp-candle: P3B2namd: ATPase Simulation - 327,506 Atomscompress-zstd: 19tensorflow-lite: SqueezeNettensorflow-lite: Mobilenet Quanttensorflow-lite: Mobilenet Floattensorflow-lite: NASNet Mobileperf-bench: Futex Hashcompress-zstd: 3perf-bench: Sched Pipeperf-bench: Memcpy 1MBecp-candle: P1B2perf-bench: Memset 1MBperf-bench: Syscall BasicCore i9 9900K230311341169956.13613638830861705192796577402.4691.8513426.520856214794414503819064660843332907.828567832.75863535.04255.18430127194134OpenBenchmarking.org

AI Benchmark Alpha

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.

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device AI ScoreCore i9 9900K50010001500200025002303

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Training ScoreCore i9 9900K20040060080010001134

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Inference ScoreCore i9 9900K300600900120015001169

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P3B1Core i9 9900K2004006008001000956.14

perf-bench

OpenBenchmarking.orgops/sec, More Is Betterperf-benchBenchmark: Epoll WaitCore i9 9900K30K60K90K120K150KSE +/- 2508.36, N = 151363881. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. 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.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4Core i9 9900K700K1400K2100K2800K3500KSE +/- 9397.29, N = 33086170

perf-bench

OpenBenchmarking.orgops/sec, More Is Betterperf-benchBenchmark: Futex Lock-PiCore i9 9900K110220330440550SE +/- 5.16, N = 155191. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. 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.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2Core i9 9900K600K1200K1800K2400K3000KSE +/- 5549.48, N = 32796577

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P3B2Core i9 9900K90180270360450402.47

NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsCore i9 9900K0.41660.83321.24981.66642.083SE +/- 0.00363, N = 31.85134

Zstd Compression

This test measures the time needed to compress a sample file (an Ubuntu ISO) using Zstd compression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.4.5Compression Level: 19Core i9 9900K612182430SE +/- 0.00, N = 326.51. (CC) gcc options: -O3 -pthread -lz -llzma

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. 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.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetCore i9 9900K40K80K120K160K200KSE +/- 3496.41, N = 3208562

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantCore i9 9900K30K60K90K120K150KSE +/- 521.00, N = 3147944

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatCore i9 9900K30K60K90K120K150KSE +/- 616.67, N = 3145038

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileCore i9 9900K40K80K120K160K200KSE +/- 525.28, N = 3190646

perf-bench

OpenBenchmarking.orgops/sec, More Is Betterperf-benchBenchmark: Futex HashCore i9 9900K1.3M2.6M3.9M5.2M6.5MSE +/- 73012.88, N = 560843331. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma

Zstd Compression

This test measures the time needed to compress a sample file (an Ubuntu ISO) using Zstd compression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterZstd Compression 1.4.5Compression Level: 3Core i9 9900K6001200180024003000SE +/- 5.81, N = 32907.81. (CC) gcc options: -O3 -pthread -lz -llzma

perf-bench

OpenBenchmarking.orgops/sec, More Is Betterperf-benchBenchmark: Sched PipeCore i9 9900K60K120K180K240K300KSE +/- 1046.92, N = 32856781. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma

OpenBenchmarking.orgGB/sec, More Is Betterperf-benchBenchmark: Memcpy 1MBCore i9 9900K816243240SE +/- 0.01, N = 332.761. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P1B2Core i9 9900K81624324035.04

perf-bench

OpenBenchmarking.orgGB/sec, More Is Betterperf-benchBenchmark: Memset 1MBCore i9 9900K1224364860SE +/- 0.11, N = 355.181. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma

OpenBenchmarking.orgops/sec, More Is Betterperf-benchBenchmark: Syscall BasicCore i9 9900K6M12M18M24M30MSE +/- 54973.97, N = 3271941341. (CC) gcc options: -O6 -ggdb3 -funwind-tables -std=gnu99 -Xlinker -export-dynamic -lpthread -lrt -lm -ldl -lelf -lcrypto -lslang -lperl -lc -lcrypt -lpython2.7 -lutil -lz -llzma -lnuma