xe sep
Intel Core i9-10980XE testing with a ASRock X299 Steel Legend (P1.30 BIOS) and llvmpipe on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2309209-PTS-XESEP76303&gru.
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
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
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
AOM AV1
Encoder Mode: Speed 11 Realtime - Input: Bosphorus 1080p
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
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - 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: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Apache Cassandra
Test: Writes
Apache Hadoop
Operation: Open - Threads: 20 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 100 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 20 - Files: 1000000
Apache Hadoop
Operation: Open - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: Open - Threads: 500 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 20 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Delete - Threads: 20 - 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: 20 - Files: 10000000
Apache Hadoop
Operation: Open - Threads: 50 - Files: 10000000
Apache Hadoop
Operation: Open - Threads: 500 - Files: 1000000
Apache Hadoop
Operation: Rename - Threads: 20 - Files: 100000
Apache Hadoop
Operation: Rename - Threads: 50 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 100 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 20 - Files: 1000000
Apache Hadoop
Operation: Create - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: Create - Threads: 500 - Files: 100000
Apache Hadoop
Operation: Delete - Threads: 100 - Files: 100000
Apache Hadoop
Operation: Delete - Threads: 20 - Files: 1000000
Apache Hadoop
Operation: Delete - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: Delete - Threads: 500 - Files: 100000
Apache Hadoop
Operation: Open - Threads: 100 - Files: 10000000
Apache Hadoop
Operation: Open - Threads: 1000 - Files: 1000000
Apache Hadoop
Operation: Open - Threads: 500 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 100 - Files: 100000
Apache Hadoop
Operation: Rename - Threads: 20 - Files: 1000000
Apache Hadoop
Operation: Rename - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: Rename - Threads: 500 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 100 - Files: 1000000
Apache Hadoop
Operation: Create - Threads: 1000 - Files: 100000
Apache Hadoop
Operation: Create - Threads: 20 - Files: 10000000
Apache Hadoop
Operation: Create - Threads: 50 - Files: 10000000
Apache Hadoop
Operation: Create - Threads: 500 - Files: 1000000
Apache Hadoop
Operation: Delete - Threads: 100 - Files: 1000000
Apache Hadoop
Operation: Delete - Threads: 1000 - Files: 100000
Apache Hadoop
Operation: Delete - Threads: 20 - Files: 10000000
Apache Hadoop
Operation: Delete - Threads: 50 - Files: 10000000
Apache Hadoop
Operation: Delete - Threads: 500 - Files: 1000000
Apache Hadoop
Operation: Open - Threads: 1000 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 100 - Files: 1000000
Apache Hadoop
Operation: Rename - Threads: 1000 - Files: 100000
Apache Hadoop
Operation: Rename - Threads: 20 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 50 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 500 - Files: 1000000
Apache Hadoop
Operation: Create - Threads: 100 - Files: 10000000
Apache Hadoop
Operation: Create - Threads: 1000 - Files: 1000000
Apache Hadoop
Operation: Create - Threads: 500 - Files: 10000000
Apache Hadoop
Operation: Delete - Threads: 100 - Files: 10000000
Apache Hadoop
Operation: Delete - Threads: 1000 - Files: 1000000
Apache Hadoop
Operation: Delete - Threads: 500 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 100 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 1000 - Files: 1000000
Apache Hadoop
Operation: Rename - Threads: 500 - Files: 10000000
Apache Hadoop
Operation: Create - Threads: 1000 - Files: 10000000
Apache Hadoop
Operation: Delete - Threads: 1000 - Files: 10000000
Apache Hadoop
Operation: Rename - Threads: 1000 - Files: 10000000
Apache Hadoop
Operation: File Status - Threads: 20 - Files: 100000
Apache Hadoop
Operation: File Status - Threads: 50 - Files: 100000
Apache Hadoop
Operation: File Status - Threads: 100 - Files: 100000
Apache Hadoop
Operation: File Status - Threads: 20 - Files: 1000000
Apache Hadoop
Operation: File Status - Threads: 50 - Files: 1000000
Apache Hadoop
Operation: File Status - Threads: 500 - Files: 100000
Apache Hadoop
Operation: File Status - Threads: 100 - Files: 1000000
Apache Hadoop
Operation: File Status - Threads: 1000 - Files: 100000
Apache Hadoop
Operation: File Status - Threads: 20 - Files: 10000000
Apache Hadoop
Operation: File Status - Threads: 50 - Files: 10000000
Apache Hadoop
Operation: File Status - Threads: 500 - Files: 1000000
Apache Hadoop
Operation: File Status - Threads: 100 - Files: 10000000
Apache Hadoop
Operation: File Status - Threads: 1000 - Files: 1000000
Apache Hadoop
Operation: File Status - Threads: 500 - Files: 10000000
Apache Hadoop
Operation: File Status - Threads: 1000 - Files: 10000000
Dragonflydb
Clients Per Thread: 10 - Set To Get Ratio: 1:5
Dragonflydb
Clients Per Thread: 20 - Set To Get Ratio: 1:5
Dragonflydb
Clients Per Thread: 50 - Set To Get Ratio: 1:5
Dragonflydb
Clients Per Thread: 10 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 20 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 50 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 10 - Set To Get Ratio: 1:100
Dragonflydb
Clients Per Thread: 20 - Set To Get Ratio: 1:100
Dragonflydb
Clients Per Thread: 50 - Set To Get Ratio: 1:100
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: 500 - Set To Get Ratio: 1:5
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10
Redis 7.0.12 + memtier_benchmark
Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 400
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: 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: 500 - 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: 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: 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: 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: 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: 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: 400
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 1000 - Mode: Read Only
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Write
PostgreSQL
Scaling Factor: 1 - Clients: 1000 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Only
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Write
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Write
BRL-CAD
VGR Performance Metric
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 400
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100
Apache IoTDB
Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 400
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: 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: 500 - 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: 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: 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: 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: 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: 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: 400
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 1000 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 800 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 1 - Clients: 1000 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency
PostgreSQL
Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency
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
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - 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: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
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
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 GCC Compilation
Time To Compile
Phoronix Test Suite v10.8.5