Xeon Max Linux Kernels
2 x Intel Xeon Max 9480 testing with a Supermicro X13DEM v1.10 (1.3 BIOS) and ASPEED on Ubuntu 23.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2310054-NE-XEONMAXLI13.
Crypto++
Test: Keyed Algorithms
Crypto++
Test: Unkeyed Algorithms
High Performance Conjugate Gradient
X Y Z: 104 104 104 - RT: 60
High Performance Conjugate Gradient
X Y Z: 144 144 144 - RT: 60
NAS Parallel Benchmarks
Test / Class: BT.C
NAS Parallel Benchmarks
Test / Class: CG.C
NAS Parallel Benchmarks
Test / Class: EP.C
NAS Parallel Benchmarks
Test / Class: EP.D
NAS Parallel Benchmarks
Test / Class: FT.C
NAS Parallel Benchmarks
Test / Class: IS.D
NAS Parallel Benchmarks
Test / Class: LU.C
NAS Parallel Benchmarks
Test / Class: MG.C
NAS Parallel Benchmarks
Test / Class: SP.B
NAS Parallel Benchmarks
Test / Class: SP.C
Rodinia
Test: OpenMP LavaMD
Rodinia
Test: OpenMP HotSpot3D
Rodinia
Test: OpenMP Leukocyte
Rodinia
Test: OpenMP CFD Solver
Rodinia
Test: OpenMP Streamcluster
OpenFOAM
Input: drivaerFastback, Small Mesh Size - Mesh Time
OpenFOAM
Input: drivaerFastback, Small Mesh Size - Execution Time
OpenFOAM
Input: drivaerFastback, Medium Mesh Size - Mesh Time
OpenFOAM
Input: drivaerFastback, Medium Mesh Size - Execution Time
OpenRadioss
Model: Bumper Beam
OpenRadioss
Model: Chrysler Neon 1M
OpenRadioss
Model: Cell Phone Drop Test
OpenRadioss
Model: Bird Strike on Windshield
OpenRadioss
Model: Rubber O-Ring Seal Installation
OpenRadioss
Model: INIVOL and Fluid Structure Interaction Drop Container
nekRS
Input: Kershaw
nekRS
Input: TurboPipe Periodic
AOM AV1
Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K
AOM AV1
Encoder Mode: Speed 11 Realtime - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 4 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 8 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 12 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 13 - Input: Bosphorus 4K
SVT-AV1
Encoder Mode: Preset 4 - Input: Bosphorus 1080p
SVT-AV1
Encoder Mode: Preset 8 - Input: Bosphorus 1080p
SVT-AV1
Encoder Mode: Preset 12 - Input: Bosphorus 1080p
SVT-AV1
Encoder Mode: Preset 13 - Input: Bosphorus 1080p
VVenC
Video Input: Bosphorus 4K - Video Preset: Fast
VVenC
Video Input: Bosphorus 4K - Video Preset: Faster
VVenC
Video Input: Bosphorus 1080p - Video Preset: Fast
VVenC
Video Input: Bosphorus 1080p - Video Preset: Faster
7-Zip Compression
Test: Compression Rating
7-Zip Compression
Test: Decompression Rating
libavif avifenc
Encoder Speed: 0
libavif avifenc
Encoder Speed: 2
libavif avifenc
Encoder Speed: 6
libavif avifenc
Encoder Speed: 6, Lossless
libavif avifenc
Encoder Speed: 10, Lossless
Timed Linux Kernel Compilation
Build: defconfig
Timed Linux Kernel Compilation
Build: allmodconfig
Timed LLVM Compilation
Build System: Ninja
Timed LLVM Compilation
Build System: Unix Makefiles
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1000 - Clients: 800 - Mode: Read Only
PostgreSQL
Scaling Factor: 1000 - Clients: 800 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1000 - Clients: 1000 - Mode: Read Only
PostgreSQL
Scaling Factor: 1000 - Clients: 1000 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1000 - Clients: 800 - Mode: Read Write
PostgreSQL
Scaling Factor: 1000 - Clients: 800 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1000 - Clients: 1000 - Mode: Read Write
PostgreSQL
Scaling Factor: 1000 - Clients: 1000 - Mode: Read Write - Average Latency
TensorFlow
Device: CPU - Batch Size: 16 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 32 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 64 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 256 - Model: ResNet-50
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10
Stress-NG
Test: Hash
Stress-NG
Test: MMAP
Stress-NG
Test: NUMA
Stress-NG
Test: Pipe
Stress-NG
Test: Poll
Stress-NG
Test: Zlib
Stress-NG
Test: Futex
Stress-NG
Test: MEMFD
Stress-NG
Test: Mutex
Stress-NG
Test: Atomic
Stress-NG
Test: Crypto
Stress-NG
Test: Malloc
Stress-NG
Test: Cloning
Stress-NG
Test: Forking
Stress-NG
Test: Pthread
Stress-NG
Test: AVL Tree
Stress-NG
Test: IO_uring
Stress-NG
Test: SENDFILE
Stress-NG
Test: CPU Cache
Stress-NG
Test: CPU Stress
Stress-NG
Test: Semaphores
Stress-NG
Test: Matrix Math
Stress-NG
Test: Vector Math
Stress-NG
Test: AVX-512 VNNI
Stress-NG
Test: Function Call
Stress-NG
Test: x86_64 RdRand
Stress-NG
Test: Floating Point
Stress-NG
Test: Matrix 3D Math
Stress-NG
Test: Memory Copying
Stress-NG
Test: Vector Shuffle
Stress-NG
Test: Mixed Scheduler
Stress-NG
Test: Socket Activity
Stress-NG
Test: Wide Vector Math
Stress-NG
Test: Context Switching
Stress-NG
Test: Fused Multiply-Add
Stress-NG
Test: Vector Floating Point
Stress-NG
Test: Glibc C String Functions
Stress-NG
Test: Glibc Qsort Data Sorting
Stress-NG
Test: System V Message Passing
NCNN
Target: CPU - Model: mobilenet
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: CPU - Model: shufflenet-v2
NCNN
Target: CPU - Model: mnasnet
NCNN
Target: CPU - Model: efficientnet-b0
NCNN
Target: CPU - Model: blazeface
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: vgg16
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: alexnet
NCNN
Target: CPU - Model: resnet50
NCNN
Target: CPU - Model: yolov4-tiny
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: vision_transformer
NCNN
Target: CPU - Model: FastestDet
Blender
Blend File: BMW27 - Compute: CPU-Only
Blender
Blend File: Classroom - Compute: CPU-Only
Blender
Blend File: Fishy Cat - Compute: CPU-Only
Blender
Blend File: Barbershop - Compute: CPU-Only
Blender
Blend File: Pabellon Barcelona - Compute: CPU-Only
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection Retail FP16-INT8 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
OpenVINO
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
OpenVINO
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
Apache Hadoop
Operation: Open - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 100 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: Open - Threads: 500 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Delete - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 100 - Files: 1000000
Apache Hadoop
Operation: Open - Threads: 1000 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 500 - Files: 1000000
Apache Hadoop
Operation: Rename - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 100 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: Create - Threads: 500 - Files: 100000
Phoronix Test Suite v10.8.5