cascade lake refresh
2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and ASPEED on Ubuntu 20.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2212189-NE-CASCADELA27&grw.
Stress-NG
Test: MMAP
Stress-NG
Test: NUMA
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: Forking
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: x86_64 RdRand
Stress-NG
Test: Memory Copying
Stress-NG
Test: Socket Activity
Stress-NG
Test: Context Switching
Stress-NG
Test: Glibc C String Functions
Stress-NG
Test: Glibc Qsort Data Sorting
Stress-NG
Test: System V Message Passing
FLAC Audio Encoding
WAV To FLAC
BRL-CAD
VGR Performance Metric
Stargate Digital Audio Workstation
Sample Rate: 44100 - Buffer Size: 512
Stargate Digital Audio Workstation
Sample Rate: 96000 - Buffer Size: 512
Stargate Digital Audio Workstation
Sample Rate: 192000 - Buffer Size: 512
Stargate Digital Audio Workstation
Sample Rate: 44100 - Buffer Size: 1024
Stargate Digital Audio Workstation
Sample Rate: 480000 - Buffer Size: 512
Stargate Digital Audio Workstation
Sample Rate: 96000 - Buffer Size: 1024
Stargate Digital Audio Workstation
Sample Rate: 192000 - Buffer Size: 1024
Stargate Digital Audio Workstation
Sample Rate: 480000 - Buffer Size: 1024
ASTC Encoder
Preset: Fast
ASTC Encoder
Preset: Medium
ASTC Encoder
Preset: Thorough
ASTC Encoder
Preset: Exhaustive
JPEG XL Decoding libjxl
CPU Threads: 1
JPEG XL Decoding libjxl
CPU Threads: All
JPEG XL libjxl
Input: PNG - Quality: 80
JPEG XL libjxl
Input: PNG - Quality: 90
JPEG XL libjxl
Input: JPEG - Quality: 80
JPEG XL libjxl
Input: JPEG - Quality: 90
JPEG XL libjxl
Input: PNG - Quality: 100
JPEG XL libjxl
Input: JPEG - Quality: 100
WebP2 Image Encode
Encode Settings: Default
WebP2 Image Encode
Encode Settings: Quality 75, Compression Effort 7
WebP2 Image Encode
Encode Settings: Quality 95, Compression Effort 7
WebP2 Image Encode
Encode Settings: Quality 100, Compression Effort 5
WebP2 Image Encode
Encode Settings: Quality 100, Lossless Compression
WebP Image Encode
Encode Settings: Default
WebP Image Encode
Encode Settings: Quality 100
WebP Image Encode
Encode Settings: Quality 100, Lossless
WebP Image Encode
Encode Settings: Quality 100, Highest Compression
WebP Image Encode
Encode Settings: Quality 100, Lossless, Highest Compression
Xmrig
Variant: Monero - Hash Count: 1M
Xmrig
Variant: Wownero - Hash Count: 1M
miniBUDE
Implementation: OpenMP - Input Deck: BM1
miniBUDE
Implementation: OpenMP - Input Deck: BM1
miniBUDE
Implementation: OpenMP - Input Deck: BM2
miniBUDE
Implementation: OpenMP - Input Deck: BM2
nekRS
Input: TurboPipe Periodic
OpenRadioss
Model: Bumper Beam
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
TensorFlow
Device: CPU - Batch Size: 16 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 32 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 64 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 16 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 32 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 64 - Model: AlexNet
TensorFlow
Device: CPU - Batch Size: 16 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 16 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 32 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 32 - Model: ResNet-50
TensorFlow
Device: CPU - Batch Size: 64 - Model: GoogLeNet
TensorFlow
Device: CPU - Batch Size: 64 - Model: ResNet-50
Numenta Anomaly Benchmark
Detector: KNN CAD
Numenta Anomaly Benchmark
Detector: Relative Entropy
Numenta Anomaly Benchmark
Detector: Windowed Gaussian
Numenta Anomaly Benchmark
Detector: Earthgecko Skyline
Numenta Anomaly Benchmark
Detector: Bayesian Changepoint
Numenta Anomaly Benchmark
Detector: Contextual Anomaly Detector OSE
Mobile Neural Network
Model: nasnet
Mobile Neural Network
Model: mobilenetV3
Mobile Neural Network
Model: squeezenetv1.1
Mobile Neural Network
Model: resnet-v2-50
Mobile Neural Network
Model: SqueezeNetV1.0
Mobile Neural Network
Model: MobileNetV2_224
Mobile Neural Network
Model: mobilenet-v1-1.0
Mobile Neural Network
Model: inception-v3
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Y-Cruncher
Pi Digits To Calculate: 1B
Y-Cruncher
Pi Digits To Calculate: 500M
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
spaCy
Model: en_core_web_lg
spaCy
Model: en_core_web_trf
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
LAMMPS Molecular Dynamics Simulator
Model: 20k Atoms
LAMMPS Molecular Dynamics Simulator
Model: Rhodopsin Protein
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
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: 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: 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: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
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
Aircrack-ng
7-Zip Compression
Test: Compression Rating
7-Zip Compression
Test: Decompression Rating
Timed PHP Compilation
Time To Compile
Cpuminer-Opt
Algorithm: Magi
Cpuminer-Opt
Algorithm: x25x
Cpuminer-Opt
Algorithm: scrypt
Cpuminer-Opt
Algorithm: Deepcoin
Cpuminer-Opt
Algorithm: Ringcoin
Cpuminer-Opt
Algorithm: Blake-2 S
Cpuminer-Opt
Algorithm: Garlicoin
Cpuminer-Opt
Algorithm: Skeincoin
Cpuminer-Opt
Algorithm: Myriad-Groestl
Cpuminer-Opt
Algorithm: LBC, LBRY Credits
Cpuminer-Opt
Algorithm: Quad SHA-256, Pyrite
Cpuminer-Opt
Algorithm: Triple SHA-256, Onecoin
Timed Linux Kernel Compilation
Build: defconfig
Timed Linux Kernel Compilation
Build: allmodconfig
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 0 Two-Pass - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p
AOM AV1
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p
GraphicsMagick
Operation: Swirl
GraphicsMagick
Operation: Rotate
GraphicsMagick
Operation: Sharpen
GraphicsMagick
Operation: Enhanced
GraphicsMagick
Operation: Resizing
GraphicsMagick
Operation: Noise-Gaussian
GraphicsMagick
Operation: HWB Color Space
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
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
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
OpenVKL
Benchmark: vklBenchmark ISPC
OpenVKL
Benchmark: vklBenchmark Scalar
Natron
Input: Spaceship
OSPRay Studio
Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer
OSPRay Studio
Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer
OSPRay Studio
Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer
OSPRay Studio
Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer
OSPRay Studio
Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer
OSPRay Studio
Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer
OSPRay Studio
Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer
OSPRay Studio
Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer
OSPRay Studio
Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer
OSPRay Studio
Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer
OSPRay Studio
Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer
OSPRay Studio
Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer
OSPRay Studio
Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer
OSPRay Studio
Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer
OSPRay Studio
Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer
OSPRay Studio
Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer
OSPRay Studio
Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer
OSPRay Studio
Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer
Timed CPython Compilation
Build Configuration: Default
Timed CPython Compilation
Build Configuration: Released Build, PGO + LTO Optimized
Timed Erlang/OTP Compilation
Time To Compile
Timed Node.js Compilation
Time To Compile
srsRAN
Test: OFDM_Test
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM
srsRAN
Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM
srsRAN
Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM
srsRAN
Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM
Apache Spark
Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 100 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Group By Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Repartition Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time
Apache Spark
Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time
ClickHouse
100M Rows Web Analytics Dataset, First Run / Cold Cache
ClickHouse
100M Rows Web Analytics Dataset, Second Run
ClickHouse
100M Rows Web Analytics Dataset, Third Run
CockroachDB
Workload: MoVR - Concurrency: 128
CockroachDB
Workload: MoVR - Concurrency: 256
CockroachDB
Workload: MoVR - Concurrency: 512
CockroachDB
Workload: MoVR - Concurrency: 1024
CockroachDB
Workload: KV, 10% Reads - Concurrency: 128
CockroachDB
Workload: KV, 10% Reads - Concurrency: 256
CockroachDB
Workload: KV, 10% Reads - Concurrency: 512
CockroachDB
Workload: KV, 50% Reads - Concurrency: 128
CockroachDB
Workload: KV, 50% Reads - Concurrency: 256
CockroachDB
Workload: KV, 50% Reads - Concurrency: 512
CockroachDB
Workload: KV, 60% Reads - Concurrency: 128
CockroachDB
Workload: KV, 60% Reads - Concurrency: 256
CockroachDB
Workload: KV, 60% Reads - Concurrency: 512
CockroachDB
Workload: KV, 95% Reads - Concurrency: 128
CockroachDB
Workload: KV, 95% Reads - Concurrency: 256
CockroachDB
Workload: KV, 95% Reads - Concurrency: 512
CockroachDB
Workload: KV, 10% Reads - Concurrency: 1024
CockroachDB
Workload: KV, 50% Reads - Concurrency: 1024
CockroachDB
Workload: KV, 60% Reads - Concurrency: 1024
CockroachDB
Workload: KV, 95% Reads - Concurrency: 1024
Dragonflydb
Clients: 50 - Set To Get Ratio: 1:1
Dragonflydb
Clients: 50 - Set To Get Ratio: 1:5
Dragonflydb
Clients: 50 - Set To Get Ratio: 5:1
Dragonflydb
Clients: 200 - Set To Get Ratio: 1:1
Dragonflydb
Clients: 200 - Set To Get Ratio: 1:5
Dragonflydb
Clients: 200 - Set To Get Ratio: 5:1
memtier_benchmark
Protocol: Redis - Clients: 50 - Set To Get Ratio: 10:1
Redis
Test: GET - Parallel Connections: 50
Redis
Test: SET - Parallel Connections: 50
Redis
Test: GET - Parallel Connections: 500
Redis
Test: SET - Parallel Connections: 500
Redis
Test: GET - Parallel Connections: 1000
Redis
Test: SET - Parallel Connections: 1000
Facebook RocksDB
Test: Random Read
Facebook RocksDB
Test: Update Random
Facebook RocksDB
Test: Read While Writing
Facebook RocksDB
Test: Read Random Write Random
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency
EnCodec
Target Bandwidth: 24 kbps
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Only
EnCodec
Target Bandwidth: 6 kbps
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency
EnCodec
Target Bandwidth: 3 kbps
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Only
EnCodec
Target Bandwidth: 1.5 kbps
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Write
Node.js V8 Web Tooling Benchmark
Unpacking The Linux Kernel
linux-5.19.tar.xz
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency
Phoronix Test Suite v10.8.5