AMD EPYC 7F32 8-Core testing with a ASRockRack EPYCD8 (P2.40 BIOS) and ASPEED on Debian 11 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 2302171-NE-7F32FEB4497
7f32 feb
AMD EPYC 7F32 8-Core testing with a ASRockRack EPYCD8 (P2.40 BIOS) and ASPEED on Debian 11 via the Phoronix Test Suite.
,,"a","b"
Processor,,AMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads),AMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads)
Motherboard,,ASRockRack EPYCD8 (P2.40 BIOS),ASRockRack EPYCD8 (P2.40 BIOS)
Chipset,,AMD Starship/Matisse,AMD Starship/Matisse
Memory,,28GB,28GB
Disk,,Samsung SSD 970 EVO Plus 250GB,Samsung SSD 970 EVO Plus 250GB
Graphics,,ASPEED,ASPEED
Network,,2 x Intel I350,2 x Intel I350
OS,,Debian 11,Debian 11
Kernel,,5.10.0-10-amd64 (x86_64),5.10.0-10-amd64 (x86_64)
Desktop,,GNOME Shell 3.38.6,GNOME Shell 3.38.6
Display Server,,X Server,X Server
Compiler,,GCC 10.2.1 20210110,GCC 10.2.1 20210110
File-System,,ext4,ext4
Screen Resolution,,1024x768,1024x768
,,"a","b"
"OpenEMS - Test: pyEMS Coupler (MCells/s)",HIB,20.99,20.97
"OpenEMS - Test: openEMS MSL_NotchFilter (MCells/s)",HIB,43.28,44.47
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,6.3968,6.3828
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,618.826,619.4588
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,5.421,5.3209
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,184.4582,187.927
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,59.3773,59.5734
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,67.2973,67.0404
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (items/sec)",HIB,33.533,35.6398
"Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (ms/batch)",LIB,29.8094,28.0476
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,22.4232,22.5889
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,177.9141,176.5938
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,12.0852,12.9139
"Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,82.7342,77.4236
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,36.4926,36.2166
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,109.4521,110.3102
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,26.1528,26.3667
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,38.221,37.9114
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,72.2822,71.5431
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,55.2697,55.8468
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,52.314,50.7451
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,19.1051,19.6955
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,50.3749,50.1637
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,79.2948,79.6162
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,36.5696,36.0677
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,27.3353,27.716
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,8.0865,8.1627
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,492.1324,487.1978
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,6.8129,6.821
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,146.7613,146.5857
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,25.5502,25.2728
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,156.3355,157.8873
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,18.5281,18.4728
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,53.9623,54.1228
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,6.4027,6.3771
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,618.215,618.8398
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,5.3727,5.3372
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,186.1177,187.355
"GROMACS - Implementation: MPI CPU - Input: water_GMX50_bare (Ns/Day)",HIB,1.244,1.283
"AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,0.19,0.19
"AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,5.64,5.65
"AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K (FPS)",HIB,45.97,46.6
"AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,9.56,9.53
"AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K (FPS)",HIB,37.74,38.13
"AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K (FPS)",HIB,45.36,48.38
"AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K (FPS)",HIB,44.6,46.21
"AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,0.54,0.54
"AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,12.3,12.35
"AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p (FPS)",HIB,107.42,100.54
"AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,28.8,28.22
"AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p (FPS)",HIB,101.4,95.22
"AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p (FPS)",HIB,114.06,115.24
"AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p (FPS)",HIB,125.49,126.5
"VP9 libvpx Encoding - Speed: Speed 0 - Input: Bosphorus 4K (FPS)",HIB,5.42,5.44
"VP9 libvpx Encoding - Speed: Speed 5 - Input: Bosphorus 4K (FPS)",HIB,9.8,9.61
"VP9 libvpx Encoding - Speed: Speed 0 - Input: Bosphorus 1080p (FPS)",HIB,11.26,11.01
"VP9 libvpx Encoding - Speed: Speed 5 - Input: Bosphorus 1080p (FPS)",HIB,23.93,23.25
"dav1d - Video Input: Chimera 1080p (FPS)",HIB,390.36,390.37
"dav1d - Video Input: Summer Nature 4K (FPS)",HIB,170.85,178.99
"dav1d - Video Input: Summer Nature 1080p (FPS)",HIB,612.98,616.71
"dav1d - Video Input: Chimera 1080p 10-bit (FPS)",HIB,366.43,369.21
"VVenC - Video Input: Bosphorus 4K - Video Preset: Fast (FPS)",HIB,2.72,2.71
"VVenC - Video Input: Bosphorus 4K - Video Preset: Faster (FPS)",HIB,6.274,6.085
"VVenC - Video Input: Bosphorus 1080p - Video Preset: Fast (FPS)",HIB,7.229,7.257
"VVenC - Video Input: Bosphorus 1080p - Video Preset: Faster (FPS)",HIB,17.597,17.604
"Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,9.5705,9.8828
"Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,9.3491,9.3296
"Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,10.5382,10.8146
"Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,9.7218,9.76
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,10.8786,10.7861
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,9.3827,9.3503
"Apache Spark - Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time (sec)",LIB,4.03,4.04
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark (sec)",LIB,188.33713603,187.447750082
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.486413801,11.52
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Group By Test Time (sec)",LIB,4.64,4.59
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Repartition Test Time (sec)",LIB,2.00,2.02
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Inner Join Test Time (sec)",LIB,2.13,2.05
"Apache Spark - Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time (sec)",LIB,1.93,1.80
"Apache Spark - Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time (sec)",LIB,4.15,4.13
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark (sec)",LIB,189.277707808,188.998279266
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.386792489,11.691897916
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Group By Test Time (sec)",LIB,5.05,5.02
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Repartition Test Time (sec)",LIB,1.96,2.23
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Inner Join Test Time (sec)",LIB,2.30,2.49
"Apache Spark - Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time (sec)",LIB,2.07,1.91
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time (sec)",LIB,4.32,4.29
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark (sec)",LIB,186.606238989,185.925845518
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.43,11.543094442
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Group By Test Time (sec)",LIB,5.59,5.31
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Repartition Test Time (sec)",LIB,2.25,2.22
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time (sec)",LIB,2.77,2.43
"Apache Spark - Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time (sec)",LIB,2.17651277,2.38
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time (sec)",LIB,4.74,4.78
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark (sec)",LIB,186.57794947,186.163020544
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe (sec)",LIB,11.51,11.399265237
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Group By Test Time (sec)",LIB,5.79,5.77
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Repartition Test Time (sec)",LIB,2.753324538,2.69
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time (sec)",LIB,3.18,3.01
"Apache Spark - Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time (sec)",LIB,2.39,2.72
"ClickHouse - (Queries/min, Geo Mean)",HIB,,
"RocksDB - Test: Random Fill (Op/s)",HIB,530099,528550
"RocksDB - Test: Random Read (Op/s)",HIB,35559141,36110424
"RocksDB - Test: Update Random (Op/s)",HIB,346409,347469
"RocksDB - Test: Sequential Fill (Op/s)",HIB,589310,595266
"RocksDB - Test: Random Fill Sync (Op/s)",HIB,2852,2857
"RocksDB - Test: Read While Writing (Op/s)",HIB,1490922,1401161
"RocksDB - Test: Read Random Write Random (Op/s)",HIB,1146165,1152478
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Only (TPS)",HIB,61593,64239
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency (ms)",LIB,0.016,0.016
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Write (TPS)",HIB,708,703
"PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency (ms)",LIB,1.413,1.423
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Only (TPS)",HIB,620983,618290
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.081,0.081
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Only (TPS)",HIB,627441,645705
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.159,0.155
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Only (TPS)",HIB,606075,617509
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency (ms)",LIB,0.412,0.405
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Write (TPS)",HIB,678,675
"PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,73.717,74.049
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Only (TPS)",HIB,522138,571774
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency (ms)",LIB,0.958,0.874
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Only (TPS)",HIB,499392,479473
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,1.602,1.668
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Only (TPS)",HIB,54842,55389
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency (ms)",LIB,0.018,0.018
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Write (TPS)",HIB,658,654
"PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,152.082,152.856
"PostgreSQL - Scaling Factor: 1 - Clients: 1000 - Mode: Read Only (TPS)",HIB,466800,450620
"PostgreSQL - Scaling Factor: 1 - Clients: 1000 - Mode: Read Only - Average Latency (ms)",LIB,2.142,2.219
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Write (TPS)",HIB,598,614
"PostgreSQL - Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency (ms)",LIB,417.722,407.38
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Write (TPS)",HIB,509,488
"PostgreSQL - Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency (ms)",LIB,982.95,1025.324
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Write (TPS)",HIB,326,329
"PostgreSQL - Scaling Factor: 1 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,2457.447,2428.662
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Write (TPS)",HIB,425,424
"PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency (ms)",LIB,2.355,2.36
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only (TPS)",HIB,545224,531797
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.092,0.094
"PostgreSQL - Scaling Factor: 1 - Clients: 1000 - Mode: Read Write (TPS)",HIB,270,258
"PostgreSQL - Scaling Factor: 1 - Clients: 1000 - Mode: Read Write - Average Latency (ms)",LIB,3707.466,3876.571
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only (TPS)",HIB,561050,563522
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.178,0.177
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Only (TPS)",HIB,550537,543855
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency (ms)",LIB,0.454,0.46
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write (TPS)",HIB,4856,4516
"PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,10.298,11.073
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Only (TPS)",HIB,491975,470230
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency (ms)",LIB,1.016,1.063
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only (TPS)",HIB,446689,486710
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,1.791,1.644
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write (TPS)",HIB,6893,6055
"PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,14.507,16.515
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only (TPS)",HIB,410670,508680
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency (ms)",LIB,2.435,1.966
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Write (TPS)",HIB,8126,7069
"PostgreSQL - Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency (ms)",LIB,30.765,35.367
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Write (TPS)",HIB,8425,6839
"PostgreSQL - Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency (ms)",LIB,59.345,73.114
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write (TPS)",HIB,8166,7173
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,97.971,111.522
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write (TPS)",HIB,7792,6889
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency (ms)",LIB,128.345,145.158