tests

AMD Ryzen 5 5500U testing with a NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS) and AMD Lucienne 512MB on Tuxedo 22.04 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 2403275-NE-TESTS110635
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

CPU Massive 2 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 2 Tests
Python Tests 3 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
March 26
  5 Hours, 17 Minutes
b
March 26
  5 Hours, 11 Minutes
Invert Hiding All Results Option
  5 Hours, 14 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):


{ "title": "tests", "last_modified": "2024-03-27 06:40:09", "description": "AMD Ryzen 5 5500U testing with a NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS) and AMD Lucienne 512MB on Tuxedo 22.04 via the Phoronix Test Suite.", "systems": { "a": { "identifier": "a", "hardware": { "Processor": "AMD Ryzen 5 5500U @ 4.06GHz (6 Cores \/ 12 Threads)", "Motherboard": "NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS)", "Chipset": "AMD Renoir\/Cezanne", "Memory": "2 x 8GB DDR4-3200MT\/s Samsung M471A1K43DB1-CWE", "Disk": "Samsung SSD 970 EVO Plus 500GB", "Graphics": "AMD Lucienne 512MB (1800\/1333MHz)", "Audio": "AMD Renoir Radeon HD Audio", "Network": "Realtek RTL8111\/8168\/8211\/8411 + Intel Wi-Fi 6 AX200" }, "software": { "OS": "Tuxedo 22.04", "Kernel": "6.5.0-10027-tuxedo (x86_64)", "Desktop": "KDE Plasma 5.27.10", "Display Server": "X Server 1.21.1.4", "OpenGL": "4.6 Mesa 24.0.3-0tux2 (LLVM 15.0.7 DRM 3.54)", "Vulkan": "1.3.274", "Compiler": "GCC 11.4.0", "File-System": "ext4", "Screen Resolution": "1920x1080" }, "user": "phoronix", "timestamp": "2024-03-26 13:48:37", "client_version": "10.8.4", "data": { "compiler-configuration": "--build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --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++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/debian\/tmp-nvptx\/usr,amdgcn-amdhsa=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/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-build-config=bootstrap-lto-lean --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", "cpu-scaling-governor": "amd-pstate-epp powersave (EPP: balance_performance)", "cpu-microcode": "0x8608103", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.10.12", "security": "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" } }, "b": { "identifier": "b", "hardware": { "Processor": "AMD Ryzen 5 5500U @ 4.06GHz (6 Cores \/ 12 Threads)", "Motherboard": "NB01 TUXEDO Aura 15 Gen2 NL5xNU (1.07.11RTR1 BIOS)", "Chipset": "AMD Renoir\/Cezanne", "Memory": "2 x 8GB DDR4-3200MT\/s Samsung M471A1K43DB1-CWE", "Disk": "Samsung SSD 970 EVO Plus 500GB", "Graphics": "AMD Lucienne 512MB (1800\/1333MHz)", "Audio": "AMD Renoir Radeon HD Audio", "Network": "Realtek RTL8111\/8168\/8211\/8411 + Intel Wi-Fi 6 AX200" }, "software": { "OS": "Tuxedo 22.04", "Kernel": "6.5.0-10027-tuxedo (x86_64)", "Desktop": "KDE Plasma 5.27.10", "Display Server": "X Server 1.21.1.4", "OpenGL": "4.6 Mesa 24.0.3-0tux2 (LLVM 15.0.7 DRM 3.54)", "Vulkan": "1.3.274", "Compiler": "GCC 11.4.0", "File-System": "ext4", "Screen Resolution": "1920x1080" }, "user": "phoronix", "timestamp": "2024-03-26 20:50:09", "client_version": "10.8.4", "data": { "compiler-configuration": "--build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --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++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/debian\/tmp-nvptx\/usr,amdgcn-amdhsa=\/build\/gcc-11-XeT9lY\/gcc-11-11.4.0\/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-build-config=bootstrap-lto-lean --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", "cpu-scaling-governor": "amd-pstate-epp powersave (EPP: balance_performance)", "cpu-microcode": "0x8608103", "kernel-extra-details": "Transparent Huge Pages: madvise", "python": "Python 3.10.12", "security": "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" } } }, "results": { "6fa0bb21f313981869d5b7153599c8cd97c5852a": { "identifier": "pts\/blender-4.1.0", "title": "Blender", "app_version": "4.1", "arguments": "-b ..\/bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU", "description": "Blend File: BMW27 - Compute: CPU-Only", "scale": "Seconds", "proportion": "LIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 321.20999999999997953636921010911464691162109375, "test_run_times": [ 322.67000000000001591615728102624416351318359375 ] }, "b": { "value": 319.31000000000000227373675443232059478759765625, "test_run_times": [ 319.8600000000000136424205265939235687255859375 ] } } }, "d642e29ba0ab924a63605dc5d18ef3966c809dce": { "identifier": "pts\/blender-4.1.0", "title": "Blender", "app_version": "4.1", "arguments": "-b ..\/classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU", "description": "Blend File: Classroom - Compute: CPU-Only", "scale": "Seconds", "proportion": "LIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 841.76999999999998181010596454143524169921875, "test_run_times": [ 842.2899999999999636202119290828704833984375 ] }, "b": { "value": 827.48000000000001818989403545856475830078125, "test_run_times": [ 827.970000000000027284841053187847137451171875 ] } } }, "e14b90554ad557bd8220d6925e62f507c49196e0": { "identifier": "pts\/blender-4.1.0", "title": "Blender", "app_version": "4.1", "arguments": "-b ..\/fishy_cat_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU", "description": "Blend File: Fishy Cat - Compute: CPU-Only", "scale": "Seconds", "proportion": "LIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 385.470000000000027284841053187847137451171875, "test_run_times": [ 386.3600000000000136424205265939235687255859375 ] }, "b": { "value": 372.07999999999998408384271897375583648681640625, "test_run_times": [ 372.990000000000009094947017729282379150390625 ] } } }, "458a4eb2c6c84b80913e52a3b3e727db73a5af11": { "identifier": "pts\/blender-4.1.0", "title": "Blender", "app_version": "4.1", "arguments": "-b ..\/barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU", "description": "Blend File: Barbershop - Compute: CPU-Only", "scale": "Seconds", "proportion": "LIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3327.010000000000218278728425502777099609375, "test_run_times": [ 3332.48000000000001818989403545856475830078125 ] }, "b": { "value": 3128.57000000000016370904631912708282470703125, "test_run_times": [ 3133.69000000000005456968210637569427490234375 ] } } }, "30a7337e8926a086e67a974609766f8885e04e46": { "identifier": "pts\/blender-4.1.0", "title": "Blender", "app_version": "4.1", "arguments": "-b ..\/pavillon_barcelone_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU", "description": "Blend File: Pabellon Barcelona - Compute: CPU-Only", "scale": "Seconds", "proportion": "LIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 1067.4600000000000363797880709171295166015625, "test_run_times": [ 1068.180000000000063664629124104976654052734375 ] }, "b": { "value": 994.19000000000005456968210637569427490234375, "test_run_times": [ 994.98000000000001818989403545856475830078125 ] } } }, "d0adcab531e05f3db8c970385d100006ac333e7f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 1 resnet50", "description": "Device: CPU - Batch Size: 1 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 21, "raw_values": [ 21.003093773644000208378201932646334171295166015625 ], "min_result": [ "18.97" ], "max_result": [ "22.06" ], "test_run_times": [ 55.97999999999999687361196265555918216705322265625 ] }, "b": { "value": 21.309999999999998721023075631819665431976318359375, "raw_values": [ 21.30910175261499972521050949580967426300048828125 ], "min_result": [ "19.02" ], "max_result": [ "22.37" ], "test_run_times": [ 55.530000000000001136868377216160297393798828125 ] } } }, "2bc391ee0b594811f657300072fb2a46f2a71e6e": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 1 resnet152", "description": "Device: CPU - Batch Size: 1 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 9.3499999999999996447286321199499070644378662109375, "raw_values": [ 9.351457087757300001840121694840490818023681640625 ], "min_result": [ "8.95" ], "max_result": [ "9.56" ], "test_run_times": [ 121.969999999999998863131622783839702606201171875 ] }, "b": { "value": 9.1500000000000003552713678800500929355621337890625, "raw_values": [ 9.148588631768600265559143736027181148529052734375 ], "min_result": [ "7.71" ], "max_result": [ "9.45" ], "test_run_times": [ 125.06000000000000227373675443232059478759765625 ] } } }, "1817b719ec4714ac77d1b79b309bdc2361beb3cf": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 16 resnet50", "description": "Device: CPU - Batch Size: 16 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 13.0299999999999993605115378159098327159881591796875, "raw_values": [ 13.034448113324000217971843085251748561859130859375 ], "min_result": [ "10.67" ], "max_result": [ "13.24" ], "test_run_times": [ 138.69999999999998863131622783839702606201171875 ] }, "b": { "value": 13.1899999999999995026200849679298698902130126953125, "raw_values": [ 13.18550093196000005946189048700034618377685546875 ], "min_result": [ "12.63" ], "max_result": [ "13.62" ], "test_run_times": [ 137.960000000000007958078640513122081756591796875 ] } } }, "5bb5428bac71de14e9e94ef4b2c074689a36c369": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 32 resnet50", "description": "Device: CPU - Batch Size: 32 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 12.839999999999999857891452847979962825775146484375, "raw_values": [ 12.8392974929299992226106041925959289073944091796875 ], "min_result": [ "11.98" ], "max_result": [ "13.14" ], "test_run_times": [ 139.80000000000001136868377216160297393798828125 ] }, "b": { "value": 13.0099999999999997868371792719699442386627197265625, "raw_values": [ 13.0051355449850003509482121444307267665863037109375 ], "min_result": [ "10.31" ], "max_result": [ "13.63" ], "test_run_times": [ 139.039999999999992041921359486877918243408203125 ] } } }, "e53ed50df6ab47811484cbc3c159c79dc2966b78": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 64 resnet50", "description": "Device: CPU - Batch Size: 64 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 12.910000000000000142108547152020037174224853515625, "raw_values": [ 12.9107430929639992456259278696961700916290283203125 ], "min_result": [ "12.22" ], "max_result": [ "13.13" ], "test_run_times": [ 141.520000000000010231815394945442676544189453125 ] }, "b": { "value": 12.75, "raw_values": [ 12.745811062527000245836461544968187808990478515625 ], "min_result": [ "11.96" ], "max_result": [ "13.06" ], "test_run_times": [ 139.759999999999990905052982270717620849609375 ] } } }, "75c834417bf0059ea75b1d19b03766a190e2dc13": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 16 resnet152", "description": "Device: CPU - Batch Size: 16 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.6699999999999999289457264239899814128875732421875, "raw_values": [ 5.67337835120739963912228631670586764812469482421875 ], "min_result": [ "4.92" ], "max_result": [ "5.85" ], "test_run_times": [ 319.509999999999990905052982270717620849609375 ] }, "b": { "value": 5.5, "raw_values": [ 5.50225873496210038382514539989642798900604248046875 ], "min_result": [ "4.52" ], "max_result": [ "5.67" ], "test_run_times": [ 324.55000000000001136868377216160297393798828125 ] } } }, "c0ebee3de3af3f6bb30ca7bfd912294570db05fa": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 256 resnet50", "description": "Device: CPU - Batch Size: 256 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 12.839999999999999857891452847979962825775146484375, "raw_values": [ 12.837502471183000807286589406430721282958984375 ], "min_result": [ "11.4" ], "max_result": [ "13.12" ], "test_run_times": [ 139.56000000000000227373675443232059478759765625 ] }, "b": { "value": 12.9000000000000003552713678800500929355621337890625, "raw_values": [ 12.901957872097000290523283183574676513671875 ], "min_result": [ "11.21" ], "max_result": [ "13.23" ], "test_run_times": [ 139.960000000000007958078640513122081756591796875 ] } } }, "56fa1ea70f6128a460fab8eadbc9d03e19a68f1f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 32 resnet152", "description": "Device: CPU - Batch Size: 32 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.54999999999999982236431605997495353221893310546875, "raw_values": [ 5.54901386080909997389198906603269279003143310546875 ], "min_result": [ "5.27" ], "max_result": [ "5.69" ], "test_run_times": [ 324.07999999999998408384271897375583648681640625 ] }, "b": { "value": 5.4900000000000002131628207280300557613372802734375, "raw_values": [ 5.48774868230000034685645005083642899990081787109375 ], "min_result": [ "4.9" ], "max_result": [ "5.65" ], "test_run_times": [ 328.6200000000000045474735088646411895751953125 ] } } }, "0d499a4b318c513750ceefe20d7b87453d9d336f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 512 resnet50", "description": "Device: CPU - Batch Size: 512 - Model: ResNet-50", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 12.67999999999999971578290569595992565155029296875, "raw_values": [ 12.681313968994000873635741299949586391448974609375 ], "min_result": [ "11.1" ], "max_result": [ "13.05" ], "test_run_times": [ 142.270000000000010231815394945442676544189453125 ] }, "b": { "value": 12.21000000000000085265128291212022304534912109375, "raw_values": [ 12.211414899463999716999751399271190166473388671875 ], "min_result": [ "11.73" ], "max_result": [ "12.93" ], "test_run_times": [ 145.5 ] } } }, "16296c993bdfa97a6848d705ddcb761a04402680": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 64 resnet152", "description": "Device: CPU - Batch Size: 64 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.45999999999999996447286321199499070644378662109375, "raw_values": [ 5.45823627724370030733780367881990969181060791015625 ], "min_result": [ "5.13" ], "max_result": [ "5.76" ], "test_run_times": [ 328.20999999999997953636921010911464691162109375 ] }, "b": { "value": 5.5099999999999997868371792719699442386627197265625, "raw_values": [ 5.5090471785629997469868612824939191341400146484375 ], "min_result": [ "4.73" ], "max_result": [ "5.69" ], "test_run_times": [ 323.3500000000000227373675443232059478759765625 ] } } }, "e3737a48218cdf9ba397f2538c1ec5ba8ba6bf81": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 256 resnet152", "description": "Device: CPU - Batch Size: 256 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.25, "raw_values": [ 5.24630963467370037278669769875705242156982421875 ], "min_result": [ "4.56" ], "max_result": [ "5.61" ], "test_run_times": [ 340.6399999999999863575794734060764312744140625 ] }, "b": { "value": 5.5, "raw_values": [ 5.50017276243350039521828875876963138580322265625 ], "min_result": [ "5.23" ], "max_result": [ "5.75" ], "test_run_times": [ 326.1100000000000136424205265939235687255859375 ] } } }, "1e1b7ee4b6d0a9be046b48132617a756ba63cb19": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 512 resnet152", "description": "Device: CPU - Batch Size: 512 - Model: ResNet-152", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.230000000000000426325641456060111522674560546875, "raw_values": [ 5.22789447738680035371316989767365157604217529296875 ], "min_result": [ "4.63" ], "max_result": [ "5.55" ], "test_run_times": [ 342.1000000000000227373675443232059478759765625 ] }, "b": { "value": 5.5999999999999996447286321199499070644378662109375, "raw_values": [ 5.597197312678400038521431270055472850799560546875 ], "min_result": [ "4.7" ], "max_result": [ "5.82" ], "test_run_times": [ 321.259999999999990905052982270717620849609375 ] } } }, "91f57edf4d0566d1c42799517ef9c6f11b556d59": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 1 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.160000000000000142108547152020037174224853515625, "raw_values": [ 5.1620783198225002053050047834403812885284423828125 ], "min_result": [ "4.63" ], "max_result": [ "5.38" ], "test_run_times": [ 219.020000000000010231815394945442676544189453125 ] }, "b": { "value": 5.54999999999999982236431605997495353221893310546875, "raw_values": [ 5.55404686638649991436977870762348175048828125 ], "min_result": [ "5.16" ], "max_result": [ "5.76" ], "test_run_times": [ 203.909999999999996589394868351519107818603515625 ] } } }, "11aa33c4a0cf33904eaf5622d1a19d243b999579": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 16 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.430000000000000159872115546022541821002960205078125, "raw_values": [ 3.4343498708651001294356319704093039035797119140625 ], "min_result": [ "3.22" ], "max_result": [ "3.62" ], "test_run_times": [ 536.1100000000000136424205265939235687255859375 ] }, "b": { "value": 3.649999999999999911182158029987476766109466552734375, "raw_values": [ 3.64849251049959999448901726282201707363128662109375 ], "min_result": [ "3.29" ], "max_result": [ "3.78" ], "test_run_times": [ 507.05000000000001136868377216160297393798828125 ] } } }, "06c69cec0fca59101a0c4df029f8fd067643dd03": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 32 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.399999999999999911182158029987476766109466552734375, "raw_values": [ 3.397234944351999796907648487831465899944305419921875 ], "min_result": [ "3.17" ], "max_result": [ "3.52" ], "test_run_times": [ 539.3899999999999863575794734060764312744140625 ] }, "b": { "value": 3.600000000000000088817841970012523233890533447265625, "raw_values": [ 3.595225460165300201964555526501499116420745849609375 ], "min_result": [ "3.43" ], "max_result": [ "3.75" ], "test_run_times": [ 511.93000000000000682121026329696178436279296875 ] } } }, "e471e6d59124e53e1c89d2df76ee3bd8192bf205": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 64 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.4199999999999999289457264239899814128875732421875, "raw_values": [ 3.418665152569300147433750680647790431976318359375 ], "min_result": [ "3.31" ], "max_result": [ "3.62" ], "test_run_times": [ 540.3899999999999863575794734060764312744140625 ] }, "b": { "value": 3.62000000000000010658141036401502788066864013671875, "raw_values": [ 3.623148723574999952035113892634399235248565673828125 ], "min_result": [ "3.48" ], "max_result": [ "3.74" ], "test_run_times": [ 508.79000000000002046363078989088535308837890625 ] } } }, "4cac0e56d23e0dfbd71412a15e30666ef513d10f": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 256 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.3300000000000000710542735760100185871124267578125, "raw_values": [ 3.329578003448700140864957575104199349880218505859375 ], "min_result": [ "3.07" ], "max_result": [ "3.54" ], "test_run_times": [ 552.9600000000000363797880709171295166015625 ] }, "b": { "value": 3.5800000000000000710542735760100185871124267578125, "raw_values": [ 3.58393951856279979750752318068407475948333740234375 ], "min_result": [ "3.45" ], "max_result": [ "3.76" ], "test_run_times": [ 511.80000000000001136868377216160297393798828125 ] } } }, "6a4e9ff171c98091c4eac6a3be870c63a4c07d1b": { "identifier": "pts\/pytorch-1.1.0", "title": "PyTorch", "app_version": "2.2.1", "arguments": "cpu 512 efficientnet_v2_l", "description": "Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l", "scale": "batches\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.430000000000000159872115546022541821002960205078125, "raw_values": [ 3.43052757074060021835748557350598275661468505859375 ], "min_result": [ "3.08" ], "max_result": [ "3.65" ], "test_run_times": [ 537.44000000000005456968210637569427490234375 ] }, "b": { "value": 3.600000000000000088817841970012523233890533447265625, "raw_values": [ 3.599236541717500070802771006128750741481781005859375 ], "min_result": [ "3.45" ], "max_result": [ "3.74" ], "test_run_times": [ 511.06000000000000227373675443232059478759765625 ] } } }, "c3b5387998e4006084a331673013309ebc420552": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=1 --model=vgg16", "description": "Device: CPU - Batch Size: 1 - Model: VGG-16", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 1.3899999999999999023003738329862244427204132080078125, "test_run_times": [ 81.8700000000000045474735088646411895751953125 ] }, "b": { "value": 1.37999999999999989341858963598497211933135986328125, "test_run_times": [ 83.3900000000000005684341886080801486968994140625 ] } } }, "4aa2a8dc9d9beef6165ea03da7a36fbc2fc7b3af": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=1 --model=alexnet", "description": "Device: CPU - Batch Size: 1 - Model: AlexNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 5.1500000000000003552713678800500929355621337890625, "test_run_times": [ 23.6099999999999994315658113919198513031005859375 ] }, "b": { "value": 5.0999999999999996447286321199499070644378662109375, "test_run_times": [ 23.82000000000000028421709430404007434844970703125 ] } } }, "29ad2dd362dad2842eaa589bcb10d92457d4742b": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=16 --model=vgg16", "description": "Device: CPU - Batch Size: 16 - Model: VGG-16", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.20000000000000017763568394002504646778106689453125, "test_run_times": [ 552.5700000000000500222085975110530853271484375 ] }, "b": { "value": 3.089999999999999857891452847979962825775146484375, "test_run_times": [ 571.75 ] } } }, "d8cbe80fca45933149672c23d1c95cbdab4d8ca5": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=32 --model=vgg16", "description": "Device: CPU - Batch Size: 32 - Model: VGG-16", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 3.270000000000000017763568394002504646778106689453125, "test_run_times": [ 1078.34999999999990905052982270717620849609375 ] }, "b": { "value": 3.04999999999999982236431605997495353221893310546875, "test_run_times": [ 1155.799999999999954525264911353588104248046875 ] } } }, "08449982a4f5f924d45208f47b9d8c62067b2e23": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=16 --model=alexnet", "description": "Device: CPU - Batch Size: 16 - Model: AlexNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 36.3900000000000005684341886080801486968994140625, "test_run_times": [ 50.719999999999998863131622783839702606201171875 ] }, "b": { "value": 36.02000000000000312638803734444081783294677734375, "test_run_times": [ 51.28999999999999914734871708787977695465087890625 ] } } }, "4042d31bc5aa3c11c217e669c15bfcdfecd2af39": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=32 --model=alexnet", "description": "Device: CPU - Batch Size: 32 - Model: AlexNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 45.5499999999999971578290569595992565155029296875, "test_run_times": [ 79.650000000000005684341886080801486968994140625 ] }, "b": { "value": 44.96000000000000085265128291212022304534912109375, "test_run_times": [ 80.68000000000000682121026329696178436279296875 ] } } }, "cc9e31e984b8806636e79ff6e5e21c1a6f766e29": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=64 --model=alexnet", "description": "Device: CPU - Batch Size: 64 - Model: AlexNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 51.530000000000001136868377216160297393798828125, "test_run_times": [ 138.919999999999987494447850622236728668212890625 ] }, "b": { "value": 51.25, "test_run_times": [ 139.759999999999990905052982270717620849609375 ] } } }, "efd4132b4ca34777b8be0494c36cc1edae74ee0d": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=1 --model=googlenet", "description": "Device: CPU - Batch Size: 1 - Model: GoogLeNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 10.839999999999999857891452847979962825775146484375, "test_run_times": [ 13.449999999999999289457264239899814128875732421875 ] }, "b": { "value": 10.8300000000000000710542735760100185871124267578125, "test_run_times": [ 13.480000000000000426325641456060111522674560546875 ] } } }, "1eff874d2ed99fbec15252604d5788236f12724f": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=1 --model=resnet50", "description": "Device: CPU - Batch Size: 1 - Model: ResNet-50", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 4.55999999999999960920149533194489777088165283203125, "test_run_times": [ 28.469999999999998863131622783839702606201171875 ] }, "b": { "value": 4.54000000000000003552713678800500929355621337890625, "test_run_times": [ 28.6700000000000017053025658242404460906982421875 ] } } }, "3fc0ca1d5a0746bef2ec915696cd1e7e450f0486": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=256 --model=alexnet", "description": "Device: CPU - Batch Size: 256 - Model: AlexNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 56.5, "test_run_times": [ 500.43999999999999772626324556767940521240234375 ] }, "b": { "value": 56.5499999999999971578290569595992565155029296875, "test_run_times": [ 500.240000000000009094947017729282379150390625 ] } } }, "5c98d60e307e91e3fa4a166cb951aedf30820571": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=512 --model=alexnet", "description": "Device: CPU - Batch Size: 512 - Model: AlexNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 57.3900000000000005684341886080801486968994140625, "test_run_times": [ 984.220000000000027284841053187847137451171875 ] }, "b": { "value": 57.5, "test_run_times": [ 982.01999999999998181010596454143524169921875 ] } } }, "0c7ca7413dad7058da6c1b7ecf451e37c79a852f": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=16 --model=googlenet", "description": "Device: CPU - Batch Size: 16 - Model: GoogLeNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 19.85000000000000142108547152020037174224853515625, "test_run_times": [ 92.099999999999994315658113919198513031005859375 ] }, "b": { "value": 20.190000000000001278976924368180334568023681640625, "test_run_times": [ 90.7099999999999937472239253111183643341064453125 ] } } }, "113aa8ffcc7b9a5e5921242a3c219cbcf10a56c1": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=16 --model=resnet50", "description": "Device: CPU - Batch Size: 16 - Model: ResNet-50", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 6.410000000000000142108547152020037174224853515625, "test_run_times": [ 279.18000000000000682121026329696178436279296875 ] }, "b": { "value": 6.5, "test_run_times": [ 274.98000000000001818989403545856475830078125 ] } } }, "fbe5c59152314e76bd9f9138ab14c06003fca508": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=32 --model=googlenet", "description": "Device: CPU - Batch Size: 32 - Model: GoogLeNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 20.120000000000000994759830064140260219573974609375, "test_run_times": [ 178.30000000000001136868377216160297393798828125 ] }, "b": { "value": 20.519999999999999573674358543939888477325439453125, "test_run_times": [ 174.909999999999996589394868351519107818603515625 ] } } }, "553578ea95c8cd321f02803aae717264cb615497": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=32 --model=resnet50", "description": "Device: CPU - Batch Size: 32 - Model: ResNet-50", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 6.410000000000000142108547152020037174224853515625, "test_run_times": [ 553.9199999999999590727384202182292938232421875 ] }, "b": { "value": 6.4199999999999999289457264239899814128875732421875, "test_run_times": [ 552.3899999999999863575794734060764312744140625 ] } } }, "756b849822aa6cfd1f7ea7544eee95ad83059012": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=64 --model=googlenet", "description": "Device: CPU - Batch Size: 64 - Model: GoogLeNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 19.730000000000000426325641456060111522674560546875, "test_run_times": [ 360.240000000000009094947017729282379150390625 ] }, "b": { "value": 19.800000000000000710542735760100185871124267578125, "test_run_times": [ 359.20999999999997953636921010911464691162109375 ] } } }, "c2b4c32d69dc5fddfa4829c640df8922cbe2fa37": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=64 --model=resnet50", "description": "Device: CPU - Batch Size: 64 - Model: ResNet-50", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 6.410000000000000142108547152020037174224853515625, "test_run_times": [ 1102.73000000000001818989403545856475830078125 ] }, "b": { "value": 6.32000000000000028421709430404007434844970703125, "test_run_times": [ 1119.160000000000081854523159563541412353515625 ] } } }, "f45c09499237f1715f224cec3778b8e2b561e26b": { "identifier": "pts\/tensorflow-2.2.0", "title": "TensorFlow", "app_version": "2.16.1", "arguments": "--device cpu --batch_size=256 --model=googlenet", "description": "Device: CPU - Batch Size: 256 - Model: GoogLeNet", "scale": "images\/sec", "proportion": "HIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 19.480000000000000426325641456060111522674560546875, "test_run_times": [ 1449.30999999999994543031789362430572509765625 ] }, "b": { "value": 19.530000000000001136868377216160297393798828125, "test_run_times": [ 1445.990000000000009094947017729282379150390625 ] } } }, "e40d6565e0c575145dc453b94f3ca3ca2807fdeb": { "identifier": "pts\/build-mesa-1.1.0", "title": "Timed Mesa Compilation", "app_version": "24.0", "description": "Time To Compile", "scale": "Seconds", "proportion": "LIB", "display_format": "BAR_GRAPH", "results": { "a": { "value": 58.72800000000000153477230924181640148162841796875, "test_run_times": [ 58.72999999999999687361196265555918216705322265625 ] }, "b": { "value": 62.04899999999999948840923025272786617279052734375, "test_run_times": [ 62.0499999999999971578290569595992565155029296875 ] } } } } }