eMAG Suite
1.0.0
System
Test suite extracted from eMAG.
pts/clomp-1.1.1
Static OMP Speedup
pts/mafft-1.6.2
Multiple Sequence Alignment - LSU RNA
pts/simdjson-1.1.1
Kostya
Throughput Test: Kostya
pts/simdjson-1.1.1
LargeRandom
Throughput Test: LargeRandom
pts/simdjson-1.1.1
PartialTweets
Throughput Test: PartialTweets
pts/simdjson-1.1.1
DistinctUserID
Throughput Test: DistinctUserID
pts/tscp-1.2.2
AI Chess Performance
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
pts/onednn-1.6.1
--matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
pts/onednn-1.6.1
--matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
pts/rav1e-1.4.0
-s 1 -l 20
Speed: 1
pts/rav1e-1.4.0
-s 5 -l 60
Speed: 5
pts/rav1e-1.4.0
-s 6 -l 60
Speed: 6
pts/rav1e-1.4.0
-s 10 -l 90
Speed: 10
pts/x264-2.6.1
H.264 Video Encoding
pts/coremark-1.0.1
CoreMark Size 666 - Iterations Per Second
pts/stockfish-1.2.0
Total Time
pts/asmfish-1.1.2
1024 Hash Memory, 26 Depth
pts/avifenc-1.0.0
-s 0
Encoder Speed: 0
pts/avifenc-1.0.0
-s 2
Encoder Speed: 2
pts/avifenc-1.0.0
-s 8
Encoder Speed: 8
pts/avifenc-1.0.0
-s 10
Encoder Speed: 10
pts/numpy-1.2.1
pts/build-eigen-1.1.0
Time To Compile
pts/encode-ape-1.4.0
WAV To APE
pts/encode-opus-1.1.1
WAV To Opus Encode
pts/espeak-1.6.1
Text-To-Speech Synthesis