Benchmarks for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2308099-NE-XEONPLATI49
Xeon Platinum 8380 AVX-512 Workloads
Benchmarks for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 via the Phoronix Test Suite.
,,"0xd000390","0xd0003a5"
Processor,,2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads),2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads)
Motherboard,,Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS),Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS)
Chipset,,Intel Ice Lake IEH,Intel Ice Lake IEH
Memory,,512GB,512GB
Disk,,7682GB INTEL SSDPF2KX076TZ,7682GB INTEL SSDPF2KX076TZ
Graphics,,ASPEED,ASPEED
Monitor,,VE228,VE228
Network,,2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP,2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS,,Ubuntu 22.10,Ubuntu 22.10
Kernel,,6.5.0-060500rc4daily20230804-generic (x86_64),6.5.0-rc5-phx-tues (x86_64)
Desktop,,GNOME Shell 43.0,GNOME Shell 43.0
Display Server,,X Server 1.21.1.3,X Server 1.21.1.3
Vulkan,,1.3.224,1.3.224
Compiler,,GCC 12.2.0,GCC 12.2.0
File-System,,ext4,ext4
Screen Resolution,,1920x1080,1920x1080
,,"0xd000390","0xd0003a5"
"TensorFlow - Device: CPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB,85.97,84.84
"TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,83.89,86.93
"libxsmm - M N K: 128 (GFLOPS/s)",HIB,1941.1,1978.9
"ONNX Runtime - Model: fcn-resnet101-11 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,110.323,117.545
"ONNX Runtime - Model: fcn-resnet101-11 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,9.08067,8.59908
"OpenVKL - Benchmark: vklBenchmark ISPC (Items / Sec)",HIB,912,856
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,832.452,816.508
"ONNX Runtime - Model: yolov4 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,85.7987,89.8299
"ONNX Runtime - Model: yolov4 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,11.6605,11.1539
"ONNX Runtime - Model: GPT-2 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,5.54783,5.28095
"ONNX Runtime - Model: GPT-2 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,180.163,190.570
"ONNX Runtime - Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,25.5687,25.8601
"ONNX Runtime - Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,39.1157,38.7185
"ONNX Runtime - Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,1.43407,1.51029
"ONNX Runtime - Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,696.725,664.537
"ONNX Runtime - Model: super-resolution-10 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,6.31428,6.71968
"ONNX Runtime - Model: super-resolution-10 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,158.355,158.014
"QMCPACK - Input: FeCO6_b3lyp_gms (Execution Time - sec)",LIB,268.56,263.23
"ONNX Runtime - Model: bertsquad-12 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,59.8415,61.5091
"ONNX Runtime - Model: bertsquad-12 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,16.7110,16.3098
"libxsmm - M N K: 256 (GFLOPS/s)",HIB,594.6,600.2
"TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,317.27,323.79
"OSPRay - Benchmark: particle_volume/pathtracer/real_time (Items/sec)",HIB,150.281,136.853
"Timed MrBayes Analysis - Primate Phylogeny Analysis (sec)",LIB,166.528,165.419
"ONNX Runtime - Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard (Inference Time Cost (ms))",LIB,4.52403,4.62085
"ONNX Runtime - Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard (Inferences/sec)",HIB,221.020,216.481
"QMCPACK - Input: FeCO6_b3lyp_gms (Execution Time - sec)",LIB,147.51,178.19
"Palabos - Grid Size: 100 (Mega Site Updates/sec)",HIB,312.195,312.530
"Cpuminer-Opt - Algorithm: Garlicoin (kH/s)",HIB,29203,22086.25
"OSPRay - Benchmark: particle_volume/scivis/real_time (Items/sec)",HIB,24.9506,16.3849
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,405.9662,421.7544
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,98.3394,94.2573
"Palabos - Grid Size: 400 (Mega Site Updates/sec)",HIB,388.476,393.844
"QMCPACK - Input: Li2_STO_ae (Execution Time - sec)",LIB,124.23,123.26
"Palabos - Grid Size: 500 (Mega Site Updates/sec)",HIB,413.207,417.483
"NCNN - Target: CPU - Model: FastestDet (ms)",LIB,10.20,9.71
"NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,46.92,45.23
"NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,45.54,38.85
"NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,15.34,16.10
"NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,23.71,24.10
"NCNN - Target: CPU - Model: resnet50 (ms)",LIB,17.15,18.51
"NCNN - Target: CPU - Model: alexnet (ms)",LIB,5.39,5.46
"NCNN - Target: CPU - Model: resnet18 (ms)",LIB,8.97,9.42
"NCNN - Target: CPU - Model: vgg16 (ms)",LIB,23.86,25.71
"NCNN - Target: CPU - Model: googlenet (ms)",LIB,15.36,16.58
"NCNN - Target: CPU - Model: blazeface (ms)",LIB,4.35,4.54
"NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,11.62,11.71
"NCNN - Target: CPU - Model: mnasnet (ms)",LIB,7.57,7.41
"NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,9.89,9.76
"NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,8.88,8.76
"NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,8.03,7.96
"NCNN - Target: CPU - Model: mobilenet (ms)",LIB,15.46,15.66
"VVenC - Video Input: Bosphorus 4K - Video Preset: Fast (FPS)",HIB,5.722,5.705
"OSPRay - Benchmark: particle_volume/ao/real_time (Items/sec)",HIB,24.7473,16.4611
"TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,309.63,321.72
"Cpuminer-Opt - Algorithm: Myriad-Groestl (kH/s)",HIB,43127,43450
"Laghos - Test: Sedov Blast Wave, ube_922_hex.mesh (Major Kernels Rate)",HIB,385.89,386.08
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,524.381,521.742
"simdjson - Throughput Test: PartialTweets (GB/s)",HIB,4.62,4.77
"simdjson - Throughput Test: DistinctUserID (GB/s)",HIB,5.52,5.71
"simdjson - Throughput Test: TopTweet (GB/s)",HIB,5.60,5.75
"TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,760.15,839.41
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,93.5399,100.1856
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,426.9495,398.7153
"OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,1490.76,1496.19
"OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,13.29,13.25
"OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,1517.69,1519.84
"OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,13.03,13.04
"OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,827.51,823.09
"OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,24.04,24.18
"OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,209.31,209.02
"OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,95.42,95.54
"OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,79.47,78.18
"OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,251.00,255.09
"OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,9.77,9.63
"OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,2039.63,2070.72
"VVenC - Video Input: Bosphorus 4K - Video Preset: Faster (FPS)",HIB,10.364,10.415
"OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,33.89,33.63
"OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,2344.97,2362.84
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,1.16,1.16
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,67604.00,67754.34
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,37.9943,45.9597
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1051.7868,869.0393
"OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,8.50,8.48
"OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,9396.52,9419.77
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,1.33,1.33
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,59274.06,59377.96
"OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,4.51,4.49
"OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,4419.17,4442.98
"OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,17.80,17.87
"OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,1121.56,1117.09
"SVT-HEVC - Tuning: 1 - Input: Bosphorus 4K (FPS)",HIB,10.46,10.45
"simdjson - Throughput Test: Kostya (GB/s)",HIB,2.61,2.87
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,474.6917,478.8510
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,84.0181,83.2041
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,17.3504,19.3090
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2301.1554,2068.3047
"OSPRay - Benchmark: gravity_spheres_volume/dim_512/scivis/real_time (Items/sec)",HIB,20.5780,18.4626
"OSPRay - Benchmark: gravity_spheres_volume/dim_512/ao/real_time (Items/sec)",HIB,21.0761,18.8862
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,129.8095,136.1780
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,307.8729,293.1241
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,551.1195,553.2682
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,72.0674,71.8868
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,551.8180,555.0876
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,72.1788,71.5436
"simdjson - Throughput Test: LargeRandom (GB/s)",HIB,0.85,0.96
"VP9 libvpx Encoding - Speed: Speed 5 - Input: Bosphorus 4K (FPS)",HIB,12.63,12.33
"miniBUDE - Implementation: OpenMP - Input Deck: BM2 (Billion Interactions/s)",HIB,101.076,104.048
"miniBUDE - Implementation: OpenMP - Input Deck: BM2 (GFInst/s)",HIB,2526.887,2601.212
"SPECFEM3D - Model: Water-layered Halfspace (sec)",LIB,31.146951238,31.381798528
"oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2.59271,2.57437
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,173.7945,172.5579
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,230.0525,231.4277
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,43.3033,42.9405
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,922.9302,930.7506
"Laghos - Test: Triple Point Problem (Major Kernels Rate)",HIB,256.27,256.87
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,62.0099,64.4439
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,644.4430,620.2548
"QMCPACK - Input: simple-H2O (Execution Time - sec)",LIB,39.555,41.246
"TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,723.27,781.25
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,94.6126,96.9429
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,422.1167,412.0531
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,39.7308,40.4508
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1005.5843,987.5452
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,39.6954,39.4131
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1006.3407,1013.8215
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,5.2210,5.6210
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,7633.2598,7092.5030
"libxsmm - M N K: 64 (GFLOPS/s)",HIB,1098.8,1177.1
"SPECFEM3D - Model: Layered Halfspace (sec)",LIB,29.497987898,29.373606551
"Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,30.74,30.90
"libxsmm - M N K: 32 (GFLOPS/s)",HIB,604.7,609.2
"Cpuminer-Opt - Algorithm: LBC, LBRY Credits (kH/s)",HIB,421660,423130
"Cpuminer-Opt - Algorithm: scrypt (kH/s)",HIB,2319.31,2321.74
"Cpuminer-Opt - Algorithm: Skeincoin (kH/s)",HIB,613333,617130
"Cpuminer-Opt - Algorithm: Blake-2 S (kH/s)",HIB,4462327,4466653
"Cpuminer-Opt - Algorithm: Magi (kH/s)",HIB,2309.47,2308.66
"Cpuminer-Opt - Algorithm: Deepcoin (kH/s)",HIB,64677,64897
"Cpuminer-Opt - Algorithm: Triple SHA-256, Onecoin (kH/s)",HIB,1332237,1333117
"Cpuminer-Opt - Algorithm: x25x (kH/s)",HIB,2659.17,2659.55
"Cpuminer-Opt - Algorithm: Quad SHA-256, Pyrite (kH/s)",HIB,921730,926277
"GROMACS - Implementation: MPI CPU - Input: water_GMX50_bare (Ns/Day)",HIB,9.234,9.094
"Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,23.83,23.72
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3.90360,3.91322
"SPECFEM3D - Model: Homogeneous Halfspace (sec)",LIB,18.022236470,17.751628972
"dav1d - Video Input: Chimera 1080p (FPS)",HIB,515.81,514.58
"SPECFEM3D - Model: Tomographic Model (sec)",LIB,14.574192022,14.145999546
"SPECFEM3D - Model: Mount St. Helens (sec)",LIB,13.148692362,12.948759795
"Intel Open Image Denoise - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only (Images / Sec)",HIB,1.46,1.46
"dav1d - Video Input: Summer Nature 4K (FPS)",HIB,281.36,280.84
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,67.170,66.460
"Remhos - Test: Sample Remap Example (sec)",LIB,12.245,12.401
"CloverLeaf - Lagrangian-Eulerian Hydrodynamics (sec)",LIB,12.04,11.98
"Xcompact3d Incompact3d - Input: input.i3d 193 Cells Per Direction (sec)",LIB,11.0240278,11.0041968
"miniBUDE - Implementation: OpenMP - Input Deck: BM1 (Billion Interactions/s)",HIB,94.136,94.897
"miniBUDE - Implementation: OpenMP - Input Deck: BM1 (GFInst/s)",HIB,2353.389,2372.418
"Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,88.1941,83.4621
"Intel Open Image Denoise - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,3.03,3.03
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,104.6844,101.0959
"SVT-HEVC - Tuning: 7 - Input: Bosphorus 4K (FPS)",HIB,138.75,138.49
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2.06936,2.06967
"SVT-HEVC - Tuning: 10 - Input: Bosphorus 4K (FPS)",HIB,184.38,182.74
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,180.967,177.721
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,175.102,177.203
"HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256 (GFLOP/s)",HIB,46.3516,46.9833
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3.62526,3.62266
"HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256 (GFLOP/s)",HIB,93.7474,94.8614
"HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256 (GFLOP/s)",HIB,101.977,102.920
"HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256 (GFLOP/s)",HIB,224.417,226.783
"HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,154.731,159.100
"HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,144.942,153.790
"HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,93.0906,93.9207
"HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,195.199,198.869