dec

AMD EPYC 7F32 8-Core testing with a ASRockRack EPYCD8 (P2.40 BIOS) and ASPEED on Debian 12 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 2312047-NE-DEC10324710
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a
December 04 2023
  4 Hours, 16 Minutes
bn
December 04 2023
  4 Hours, 10 Minutes
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  4 Hours, 13 Minutes
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decOpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads)ASRockRack EPYCD8 (P2.40 BIOS)AMD Starship/Matisse28GBSamsung SSD 970 EVO Plus 250GBASPEED2 x Intel I350Debian 126.1.0-11-amd64 (x86_64)X ServerGCC 12.2.0ext41024x768ProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDisplay ServerCompilerFile-SystemScreen ResolutionDec BenchmarksSystem Logs- Transparent Huge Pages: always- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-bTRWOB/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-bTRWOB/gcc-12-12.2.0/debian/tmp-gcn/usr --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: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0x8301034 - OpenJDK Runtime Environment (build 17.0.9+9-Debian-1deb12u1)- Python 3.11.2- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Mitigation of untrained return thunk; SMT enabled with STIBP protection + spec_rstack_overflow: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

a vs. bn ComparisonPhoronix Test SuiteBaseline+9.9%+9.9%+19.8%+19.8%+29.7%+29.7%14.7%9.7%9.6%9%5.6%4.1%3.5%3.1%2.3%13%5.3%19.2%24%20.8%27.2%15.3%10.7%5.4%4.3%22.5%20.4%14.2%24.3%39.4%38.9%24.8%24.7%27.4%10%3.9%3%10.2%6.1%13.7%8%10.9%9.1%7.9%3.6%5.8%1 - G.M.O.A.QCPU - 16 - ResNet-5010%CPU - 16 - Efficientnet_v2_lCPU - 1 - ResNet-50CPU - 32 - ResNet-50S.M.M5.8%CPU - 1 - ResNet-152Mixed 1:3CPU - 16 - ResNet-152CPU - 32 - Efficientnet_v2_l10 - G.M.O.A.QCPU - 32 - ResNet-1522.2%1 - Q011 - Q021 - Q031 - Q041 - Q051 - Q061 - Q071 - Q081 - Q091 - Q101 - Q1114.8%1 - Q121 - Q141 - Q151 - Q166.9%1 - Q171 - Q181 - Q191 - Q201 - Q211 - Q2210 - Q015.9%10 - Q026.2%10 - Q0310 - Q0510 - Q067.4%10 - Q0710 - Q0810 - Q0910 - Q1010 - Q1210 - Q132.9%10 - Q1410 - Q1510 - Q1633.2%10 - Q174.9%10 - Q2010 - Q2110 - Q22Apache Spark TPC-HPyTorchPyTorchPyTorchPyTorchJava SciMarkPyTorchScyllaDBPyTorchPyTorchApache Spark TPC-HPyTorchApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-HApache Spark TPC-Habn

decjava-scimark2: Compositejava-scimark2: Monte Carlojava-scimark2: Fast Fourier Transformjava-scimark2: Sparse Matrix Multiplyjava-scimark2: Dense LU Matrix Factorizationjava-scimark2: Jacobi Successive Over-Relaxationwebp2: Defaultwebp2: Quality 75, Compression Effort 7webp2: Quality 95, Compression Effort 7webp2: Quality 100, Compression Effort 5webp2: Quality 100, Lossless Compressionxmrig: KawPow - 1Mxmrig: Monero - 1Mopenssl: AES-128-GCMopenssl: AES-256-GCMopenssl: RSA4096openssl: RSA4096openssl: SHA256openssl: SHA512xmrig: Wownero - 1Mxmrig: GhostRider - 1Mxmrig: CryptoNight-Heavy - 1Mxmrig: CryptoNight-Femto UPX2 - 1Mpytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 32 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lspark-tpch: 1 - Geometric Mean Of All Queriesspark-tpch: 10 - Geometric Mean Of All Queriesscylladb: Writesscylladb: Mixed 1:1scylladb: Mixed 1:3spark-tpch: 1 - Q01spark-tpch: 1 - Q02spark-tpch: 1 - Q03spark-tpch: 1 - Q04spark-tpch: 1 - Q05spark-tpch: 1 - Q06spark-tpch: 1 - Q07spark-tpch: 1 - Q08spark-tpch: 1 - Q09spark-tpch: 1 - Q10spark-tpch: 1 - Q11spark-tpch: 1 - Q12spark-tpch: 1 - Q13spark-tpch: 1 - Q14spark-tpch: 1 - Q15spark-tpch: 1 - Q16spark-tpch: 1 - Q17spark-tpch: 1 - Q18spark-tpch: 1 - Q19spark-tpch: 1 - Q20spark-tpch: 1 - Q21spark-tpch: 1 - Q22spark-tpch: 10 - Q01spark-tpch: 10 - Q02spark-tpch: 10 - Q03spark-tpch: 10 - Q04spark-tpch: 10 - Q05spark-tpch: 10 - Q06spark-tpch: 10 - Q07spark-tpch: 10 - Q08spark-tpch: 10 - Q09spark-tpch: 10 - Q10spark-tpch: 10 - Q11spark-tpch: 10 - Q12spark-tpch: 10 - Q13spark-tpch: 10 - Q14spark-tpch: 10 - Q15spark-tpch: 10 - Q16spark-tpch: 10 - Q17spark-tpch: 10 - Q18spark-tpch: 10 - Q19spark-tpch: 10 - Q20spark-tpch: 10 - Q21spark-tpch: 10 - Q22abn3353.911551.09208.592984.3410381.181644.336.150.120.053.140.0172837257.336787037530340498221702281.4146935.3813270884035358531007642.21699.47247.97222.623.8110.8018.8318.867.797.825.844.014.232.2205835710.15972546350859385594823.770149232.360486752.971126322.652733332.736775161.444051622.922660112.442706584.363090042.593747380.994470242.192754751.473070621.462298271.472357151.406862383.187914614.133283141.556506632.56852777.36233951.5088796611.455453873.7266223415.5402002312.0336494415.3145387.3914122612.1003770814.2428884517.9179363313.40667823.0479536110.263039595.299695019.0776318.475245483.8681838520.5452594821.659423839.5893678710.4688682640.580924994.144622333322.561541.63208.592821.9110396.361644.336.070.120.053.140.017367.57299.836966133490338452998802281.3146577.4818252311035378699507733.91702.27285.47293.526.1011.4017.1220.558.067.655.914.404.361.935280749.930870166239458423619103.335615632.241861822.491853712.138587712.264764791.135650282.534736392.207099684.139904022.486888171.141985771.790715341.489549521.214504961.289586781.503790972.565587042.964163541.12066772.057964565.905636311.1847896612.134295463.9573233114.1264972712.1029138614.733333597.9385809911.751972212.9197816816.8858280211.796113013.04151139.501013765.451005948.187623987.768936635.1504869521.5553245521.74818239.727069859.7035722739.153903963.91672444OpenBenchmarking.org

Java SciMark

This test runs the Java version of SciMark 2, which is a benchmark for scientific and numerical computing developed by programmers at the National Institute of Standards and Technology. This benchmark is made up of Fast Foruier Transform, Jacobi Successive Over-relaxation, Monte Carlo, Sparse Matrix Multiply, and dense LU matrix factorization benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Compositeabn70014002100280035003353.913322.56

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Monte Carloabn300600900120015001551.091541.63

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Fast Fourier Transformabn50100150200250208.59208.59

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Sparse Matrix Multiplyabn60012001800240030002984.342821.91

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Dense LU Matrix Factorizationabn2K4K6K8K10K10381.1810396.36

OpenBenchmarking.orgMflops, More Is BetterJava SciMark 2.2Computational Test: Jacobi Successive Over-Relaxationabn4008001200160020001644.331644.33

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Defaultabn2468106.156.071. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 75, Compression Effort 7abn0.0270.0540.0810.1080.1350.120.121. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 95, Compression Effort 7abn0.01130.02260.03390.04520.05650.050.051. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Compression Effort 5abn0.70651.4132.11952.8263.53253.143.141. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Lossless Compressionabn0.00230.00460.00690.00920.01150.010.011. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

Xmrig

Xmrig is an open-source cross-platform CPU/GPU miner for RandomX, KawPow, CryptoNight and AstroBWT. This test profile is setup to measure the Xmrig CPU mining performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: KawPow - Hash Count: 1Mabn160032004800640080007283.07367.51. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: Monero - Hash Count: 1Mabn160032004800640080007257.37299.81. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: AES-128-GCMabn8000M16000M24000M32000M40000M36787037530369661334901. OpenSSL 3.0.9 30 May 2023 (Library: OpenSSL 3.0.9 30 May 2023)

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: AES-256-GCMabn7000M14000M21000M28000M35000M34049822170338452998801. OpenSSL 3.0.9 30 May 2023 (Library: OpenSSL 3.0.9 30 May 2023)

OpenBenchmarking.orgsign/s, More Is BetterOpenSSLAlgorithm: RSA4096abn50010001500200025002281.42281.31. OpenSSL 3.0.9 30 May 2023 (Library: OpenSSL 3.0.9 30 May 2023)

OpenBenchmarking.orgverify/s, More Is BetterOpenSSLAlgorithm: RSA4096abn30K60K90K120K150K146935.3146577.41. OpenSSL 3.0.9 30 May 2023 (Library: OpenSSL 3.0.9 30 May 2023)

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: SHA256abn2000M4000M6000M8000M10000M813270884081825231101. OpenSSL 3.0.9 30 May 2023 (Library: OpenSSL 3.0.9 30 May 2023)

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: SHA512abn800M1600M2400M3200M4000M353585310035378699501. OpenSSL 3.0.9 30 May 2023 (Library: OpenSSL 3.0.9 30 May 2023)

Xmrig

Xmrig is an open-source cross-platform CPU/GPU miner for RandomX, KawPow, CryptoNight and AstroBWT. This test profile is setup to measure the Xmrig CPU mining performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: Wownero - Hash Count: 1Mabn170034005100680085007642.27733.91. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: GhostRider - Hash Count: 1Mabn4008001200160020001699.41702.21. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: CryptoNight-Heavy - Hash Count: 1Mabn160032004800640080007247.97285.41. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

OpenBenchmarking.orgH/s, More Is BetterXmrig 6.21Variant: CryptoNight-Femto UPX2 - Hash Count: 1Mabn160032004800640080007222.67293.51. (CXX) g++ options: -fexceptions -fno-rtti -maes -O3 -Ofast -static-libgcc -static-libstdc++ -rdynamic -lssl -lcrypto -luv -lpthread -lrt -ldl -lhwloc

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abn61218243023.8126.10MIN: 20.41 / MAX: 24.74MIN: 21.58 / MAX: 27.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abn369121510.8011.40MIN: 9.26 / MAX: 11.18MIN: 9.64 / MAX: 11.8

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abn51015202518.8317.12MIN: 15.57 / MAX: 19.37MIN: 12.9 / MAX: 18.03

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abn51015202518.8620.55MIN: 16.43 / MAX: 19.67MIN: 16.69 / MAX: 21.23

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abn2468107.798.06MIN: 6.42 / MAX: 8.07MIN: 6.7 / MAX: 8.27

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abn2468107.827.65MIN: 5.45 / MAX: 8.08MIN: 5.48 / MAX: 8.43

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_labn1.32982.65963.98945.31926.6495.845.91MIN: 5 / MAX: 5.95MIN: 5.23 / MAX: 5.99

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_labn0.991.982.973.964.954.014.40MIN: 3.52 / MAX: 4.18MIN: 3.46 / MAX: 4.55

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_labn0.9811.9622.9433.9244.9054.234.36MIN: 3.7 / MAX: 4.33MIN: 3.55 / MAX: 4.59

Apache Spark TPC-H

This is a benchmark of Apache Spark using TPC-H data-set. Apache Spark is an open-source unified analytics engine for large-scale data processing and dealing with big data. This test profile benchmarks the Apache Spark in a single-system configuration using spark-submit. The test makes use of https://github.com/ssavvides/tpch-spark/ for facilitating the TPC-H benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterApache Spark TPC-H 3.5Scale Factor: 1 - Geometric Mean Of All Queriesabn0.49960.99921.49881.99842.4982.220583571.93528074MIN: 0.99 / MAX: 7.36MIN: 1.12 / MAX: 5.91

OpenBenchmarking.orgSeconds, Fewer Is BetterApache Spark TPC-H 3.5Scale Factor: 10 - Geometric Mean Of All Queriesabn369121510.159725409.93087016MIN: 3.05 / MAX: 40.58MIN: 3.04 / MAX: 39.15

ScyllaDB

This is a benchmark of ScyllaDB and is making use of Apache Cassandra's cassandra-stress for conducting the benchmark. ScyllaDB is an open-source distributed NoSQL data store that is compatible with Apache Cassandra while focusing on higher throughput and lower latency. ScyllaDB uses a sharded design on each node. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgOp/s, More Is BetterScyllaDB 5.2.9Test: Writesabn14K28K42K56K70K6350862394

OpenBenchmarking.orgOp/s, More Is BetterScyllaDB 5.2.9Test: Mixed 1:1abn13K26K39K52K65K5938558423

OpenBenchmarking.orgOp/s, More Is BetterScyllaDB 5.2.9Test: Mixed 1:3abn13K26K39K52K65K5948261910