3900xt-november Suite
1.0.0
System
Test suite extracted from 3900xt-november .
pts/libplacebo-1.1.0
Test: deband_heavy
pts/libplacebo-1.1.0
Test: polar_nocompute
pts/libplacebo-1.1.0
Test: hdr_peakdetect
pts/libplacebo-1.1.0
Test: hdr_lut
pts/libplacebo-1.1.0
Test: av1_grain_lap
pts/minibude-1.0.0
--deck ../data/bm1 --iterations 500
Implementation: OpenMP - Input Deck: BM1
pts/minibude-1.0.0
--deck ../data/bm2 --iterations 10
Implementation: OpenMP - Input Deck: BM2
pts/smhasher-1.1.0
--test=Speed wyhash
Hash: wyhash
pts/smhasher-1.1.0
--test=Speed sha3-256
Hash: SHA3-256
pts/smhasher-1.1.0
--test=Speed Spooky32
Hash: Spooky32
pts/smhasher-1.1.0
--test=Speed fasthash32
Hash: fasthash32
pts/smhasher-1.1.0
--test=Speed FarmHash128
Hash: FarmHash128
pts/smhasher-1.1.0
--test=Speed t1ha2_atonce
Hash: t1ha2_atonce
pts/smhasher-1.1.0
--test=Speed FarmHash32
Hash: FarmHash32 x86_64 AVX
pts/smhasher-1.1.0
--test=Speed t1ha0_aes_avx2
Hash: t1ha0_aes_avx2 x86_64
pts/smhasher-1.1.0
--test=Speed MeowHash
Hash: MeowHash x86_64 AES-NI
pts/nekrs-1.0.0
turbPipePeriodic turbPipe.par
Input: TurboPipe Periodic
pts/openfoam-1.2.0
incompressible/simpleFoam/drivaerFastback/ -m S
Input: drivaerFastback, Small Mesh Size - Mesh Time
pts/openfoam-1.2.0
incompressible/simpleFoam/drivaerFastback/ -m S
Input: drivaerFastback, Small Mesh Size - Execution Time
pts/openradioss-1.0.0
Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad
Model: Bumper Beam
pts/openradioss-1.0.0
Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad
Model: Cell Phone Drop Test
pts/openradioss-1.0.0
BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad
Model: Bird Strike on Windshield
pts/openradioss-1.0.0
RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad
Model: Rubber O-Ring Seal Installation
pts/openradioss-1.0.0
fsi_drop_container_0000.rad fsi_drop_container_0001.rad
Model: INIVOL and Fluid Structure Interaction Drop Container
pts/xmrig-1.1.0
--bench=1M
Variant: Monero - Hash Count: 1M
pts/xmrig-1.1.0
-a rx/wow --bench=1M
Variant: Wownero - Hash Count: 1M
pts/jpegxl-1.5.0
sample-4.png out.jxl -q 80 --num_reps 50
Input: PNG - Quality: 80
pts/jpegxl-1.5.0
sample-4.png out.jxl -q 90 --num_reps 40
Input: PNG - Quality: 90
pts/jpegxl-1.5.0
--lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 50
Input: JPEG - Quality: 80
pts/jpegxl-1.5.0
--lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40
Input: JPEG - Quality: 90
pts/jpegxl-1.5.0
sample-4.png out.jxl -q 100 --num_reps 10
Input: PNG - Quality: 100
pts/jpegxl-1.5.0
--lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 10
Input: JPEG - Quality: 100
pts/jpegxl-decode-1.5.0
--num_threads=1 --num_reps=100
CPU Threads: 1
pts/jpegxl-decode-1.5.0
--num_reps=200
CPU Threads: All
pts/quadray-1.0.0
-d 1 -x 3840 -y 2160
Scene: 1 - Resolution: 4K
pts/quadray-1.0.0
-d 2 -x 3840 -y 2160
Scene: 2 - Resolution: 4K
pts/quadray-1.0.0
-d 3 -x 3840 -y 2160
Scene: 3 - Resolution: 4K
pts/quadray-1.0.0
-d 5 -x 3840 -y 2160
Scene: 5 - Resolution: 4K
pts/quadray-1.0.0
-d 1 -x 1920 -y 1080
Scene: 1 - Resolution: 1080p
pts/quadray-1.0.0
-d 2 -x 1920 -y 1080
Scene: 2 - Resolution: 1080p
pts/quadray-1.0.0
-d 3 -x 1920 -y 1080
Scene: 3 - Resolution: 1080p
pts/quadray-1.0.0
-d 5 -x 1920 -y 1080
Scene: 5 - Resolution: 1080p
pts/aom-av1-3.5.0
--cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m
Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=4 Bosphorus_3840x2160.y4m
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=6 --rt Bosphorus_3840x2160.y4m
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=6 Bosphorus_3840x2160.y4m
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=8 --rt Bosphorus_3840x2160.y4m
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=9 --rt Bosphorus_3840x2160.y4m
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=10 --rt Bosphorus_3840x2160.y4m
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K
pts/aom-av1-3.5.0
--cpu-used=0 --limit=20 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p
pts/aom-av1-3.5.0
--cpu-used=4 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p
pts/aom-av1-3.5.0
--cpu-used=6 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p
pts/aom-av1-3.5.0
--cpu-used=6 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p
pts/aom-av1-3.5.0
--cpu-used=8 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p
pts/aom-av1-3.5.0
--cpu-used=9 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p
pts/aom-av1-3.5.0
--cpu-used=10 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m
Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p
pts/avifenc-1.3.0
-s 0
Encoder Speed: 0
pts/avifenc-1.3.0
-s 2
Encoder Speed: 2
pts/avifenc-1.3.0
-s 6
Encoder Speed: 6
pts/avifenc-1.3.0
-s 6 -l
Encoder Speed: 6, Lossless
pts/avifenc-1.3.0
-s 10 -l
Encoder Speed: 10, Lossless
pts/y-cruncher-1.2.0
1b
Pi Digits To Calculate: 1B
pts/y-cruncher-1.2.0
500m
Pi Digits To Calculate: 500M
pts/onednn-2.7.0
--ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
pts/onednn-2.7.0
--matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
pts/encode-flac-1.8.1
WAV To FLAC
pts/ffmpeg-3.0.0
--encoder=libx264 live
Encoder: libx264 - Scenario: Live
pts/ffmpeg-3.0.0
--encoder=libx265 live
Encoder: libx265 - Scenario: Live
pts/ffmpeg-3.0.0
--encoder=libx264 upload
Encoder: libx264 - Scenario: Upload
pts/ffmpeg-3.0.0
--encoder=libx265 upload
Encoder: libx265 - Scenario: Upload
pts/ffmpeg-3.0.0
--encoder=libx264 platform
Encoder: libx264 - Scenario: Platform
pts/ffmpeg-3.0.0
--encoder=libx265 platform
Encoder: libx265 - Scenario: Platform
pts/ffmpeg-3.0.0
--encoder=libx264 vod
Encoder: libx264 - Scenario: Video On Demand
pts/ffmpeg-3.0.0
--encoder=libx265 vod
Encoder: libx265 - Scenario: Video On Demand
pts/cpuminer-opt-1.6.0
-a m7m
Algorithm: Magi
pts/cpuminer-opt-1.6.0
-a x25x
Algorithm: x25x
pts/cpuminer-opt-1.6.0
-a scrypt
Algorithm: scrypt
pts/cpuminer-opt-1.6.0
-a deep
Algorithm: Deepcoin
pts/cpuminer-opt-1.6.0
-a minotaur
Algorithm: Ringcoin
pts/cpuminer-opt-1.6.0
-a blake2s
Algorithm: Blake-2 S
pts/cpuminer-opt-1.6.0
-a allium
Algorithm: Garlicoin
pts/cpuminer-opt-1.6.0
-a skein
Algorithm: Skeincoin
pts/cpuminer-opt-1.6.0
-a myr-gr
Algorithm: Myriad-Groestl
pts/cpuminer-opt-1.6.0
-a lbry
Algorithm: LBC, LBRY Credits
pts/cpuminer-opt-1.6.0
-a sha256q
Algorithm: Quad SHA-256, Pyrite
pts/cpuminer-opt-1.6.0
-a sha256t
Algorithm: Triple SHA-256, Onecoin
pts/tensorflow-2.0.0
--device cpu --batch_size=16 --model=alexnet
Device: CPU - Batch Size: 16 - Model: AlexNet
pts/tensorflow-2.0.0
--device cpu --batch_size=32 --model=alexnet
Device: CPU - Batch Size: 32 - Model: AlexNet
pts/tensorflow-2.0.0
--device cpu --batch_size=64 --model=alexnet
Device: CPU - Batch Size: 64 - Model: AlexNet
pts/tensorflow-2.0.0
--device cpu --batch_size=256 --model=alexnet
Device: CPU - Batch Size: 256 - Model: AlexNet
pts/tensorflow-2.0.0
--device cpu --batch_size=512 --model=alexnet
Device: CPU - Batch Size: 512 - Model: AlexNet
pts/tensorflow-2.0.0
--device cpu --batch_size=16 --model=googlenet
Device: CPU - Batch Size: 16 - Model: GoogLeNet
pts/tensorflow-2.0.0
--device cpu --batch_size=16 --model=resnet50
Device: CPU - Batch Size: 16 - Model: ResNet-50
pts/tensorflow-2.0.0
--device cpu --batch_size=32 --model=googlenet
Device: CPU - Batch Size: 32 - Model: GoogLeNet
pts/tensorflow-2.0.0
--device cpu --batch_size=32 --model=resnet50
Device: CPU - Batch Size: 32 - Model: ResNet-50
pts/tensorflow-2.0.0
--device cpu --batch_size=64 --model=googlenet
Device: CPU - Batch Size: 64 - Model: GoogLeNet
pts/tensorflow-2.0.0
--device cpu --batch_size=64 --model=resnet50
Device: CPU - Batch Size: 64 - Model: ResNet-50
pts/tensorflow-2.0.0
--device cpu --batch_size=256 --model=googlenet
Device: CPU - Batch Size: 256 - Model: GoogLeNet
pts/tensorflow-2.0.0
--device cpu --batch_size=512 --model=googlenet
Device: CPU - Batch Size: 512 - Model: GoogLeNet
pts/deepsparse-1.0.1
zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
pts/deepsparse-1.0.1
zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario sync
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
pts/deepsparse-1.0.1
zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async
Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync
Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream
pts/deepsparse-1.0.1
zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
pts/deepsparse-1.0.1
zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
pts/deepsparse-1.0.1
zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario async
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario sync
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
pts/deepsparse-1.0.1
zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
pts/deepsparse-1.0.1
zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
pts/stress-ng-1.6.0
--mmap -1
Test: MMAP
pts/stress-ng-1.6.0
--numa -1
Test: NUMA
pts/stress-ng-1.6.0
--futex -1
Test: Futex
pts/stress-ng-1.6.0
--memfd -1
Test: MEMFD
pts/stress-ng-1.6.0
--mutex -1
Test: Mutex
pts/stress-ng-1.6.0
--atomic -1
Test: Atomic
pts/stress-ng-1.6.0
--crypt -1
Test: Crypto
pts/stress-ng-1.6.0
--malloc -1
Test: Malloc
pts/stress-ng-1.6.0
--fork -1
Test: Forking
pts/stress-ng-1.6.0
--io-uring -1
Test: IO_uring
pts/stress-ng-1.6.0
--sendfile -1
Test: SENDFILE
pts/stress-ng-1.6.0
--cache -1
Test: CPU Cache
pts/stress-ng-1.6.0
--cpu -1 --cpu-method all
Test: CPU Stress
pts/stress-ng-1.6.0
--sem -1
Test: Semaphores
pts/stress-ng-1.6.0
--matrix -1
Test: Matrix Math
pts/stress-ng-1.6.0
--vecmath -1
Test: Vector Math
pts/stress-ng-1.6.0
--memcpy -1
Test: Memory Copying
pts/stress-ng-1.6.0
--sock -1
Test: Socket Activity
pts/stress-ng-1.6.0
--switch -1
Test: Context Switching
pts/stress-ng-1.6.0
--str -1
Test: Glibc C String Functions
pts/stress-ng-1.6.0
--qsort -1
Test: Glibc Qsort Data Sorting
pts/stress-ng-1.6.0
--msg -1
Test: System V Message Passing
pts/spacy-1.0.0
Model: en_core_web_lg
pts/spacy-1.0.0
Model: en_core_web_trf
pts/nginx-3.0.0
-c 100
Connections: 100
pts/nginx-3.0.0
-c 200
Connections: 200
pts/nginx-3.0.0
-c 500
Connections: 500
pts/nginx-3.0.0
-c 1000
Connections: 1000
pts/encodec-1.0.1
-b 3
Target Bandwidth: 3 kbps
pts/encodec-1.0.1
-b 6
Target Bandwidth: 6 kbps
pts/encodec-1.0.1
-b 24
Target Bandwidth: 24 kbps
pts/encodec-1.0.1
-b 1.5
Target Bandwidth: 1.5 kbps