Tests for a future article. Intel Core i7-1165G7 testing with a Dell 0GG9PT (3.15.0 BIOS) and Intel Xe TGL GT2 15GB on Ubuntu 23.10 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 2312185-SYST-FHOS22301
fhos
Tests for a future article. Intel Core i7-1165G7 testing with a Dell 0GG9PT (3.15.0 BIOS) and Intel Xe TGL GT2 15GB on Ubuntu 23.10 via the Phoronix Test Suite.
s:
Processor: Intel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads), Motherboard: Dell 0GG9PT (3.15.0 BIOS), Chipset: Intel Tiger Lake-LP, Memory: 16GB, Disk: Kioxia KBG40ZNS256G NVMe 256GB, Graphics: Intel Xe TGL GT2 15GB (1300MHz), Audio: Realtek ALC289, Network: Intel Wi-Fi 6 AX201
OS: Ubuntu 23.10, Kernel: 6.5.0-10-generic (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3, OpenCL: OpenCL 3.0, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
b:
Processor: Intel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads), Motherboard: Dell 0GG9PT (3.15.0 BIOS), Chipset: Intel Tiger Lake-LP, Memory: 16GB, Disk: Kioxia KBG40ZNS256G NVMe 256GB, Graphics: Intel Xe TGL GT2 15GB (1300MHz), Audio: Realtek ALC289, Network: Intel Wi-Fi 6 AX201
OS: Ubuntu 23.10, Kernel: 6.5.0-10-generic (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3, OpenCL: OpenCL 3.0, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
c:
Processor: Intel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads), Motherboard: Dell 0GG9PT (3.15.0 BIOS), Chipset: Intel Tiger Lake-LP, Memory: 16GB, Disk: Kioxia KBG40ZNS256G NVMe 256GB, Graphics: Intel Xe TGL GT2 15GB (1300MHz), Audio: Realtek ALC289, Network: Intel Wi-Fi 6 AX201
OS: Ubuntu 23.10, Kernel: 6.5.0-10-generic (x86_64), Desktop: GNOME Shell 45.0, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3, OpenCL: OpenCL 3.0, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
OpenSSL
Algorithm: ChaCha20
OpenSSL
Algorithm: AES-128-GCM
OpenSSL
Algorithm: AES-256-GCM
OpenSSL
Algorithm: ChaCha20-Poly1305
OpenSSL
Algorithm: SHA256
byte/s > Higher Is Better
s . 2903712830 |===============================================================
b . 2893925250 |===============================================================
OpenSSL
Algorithm: SHA512
byte/s > Higher Is Better
s . 1091931650 |===============================================================
b . 1094541280 |===============================================================
SVT-AV1 1.8
Encoder Mode: Preset 4 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
s . 1.427 |====================================================================
b . 1.411 |===================================================================
SVT-AV1 1.8
Encoder Mode: Preset 8 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
s . 10.98 |===================================================================
b . 11.08 |====================================================================
SVT-AV1 1.8
Encoder Mode: Preset 12 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
s . 42.85 |====================================================================
b . 42.50 |===================================================================
SVT-AV1 1.8
Encoder Mode: Preset 13 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
s . 45.92 |====================================================================
b . 45.91 |====================================================================
SVT-AV1 1.8
Encoder Mode: Preset 4 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
s . 5.897 |=================================================================
b . 6.210 |====================================================================
SVT-AV1 1.8
Encoder Mode: Preset 8 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
s . 41.91 |==================================================================
b . 43.50 |====================================================================
SVT-AV1 1.8
Encoder Mode: Preset 12 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
s . 216.67 |==================================================================
b . 220.54 |===================================================================
SVT-AV1 1.8
Encoder Mode: Preset 13 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
s . 286.55 |==================================================================
b . 290.87 |===================================================================
Xmrig 6.21
Variant: KawPow - Hash Count: 1M
H/s > Higher Is Better
s . 1967.3 |===================================================================
b . 1976.1 |===================================================================
c . 1980.6 |===================================================================
Xmrig 6.21
Variant: Monero - Hash Count: 1M
H/s > Higher Is Better
s . 1960.5 |==================================================================
b . 1989.0 |===================================================================
c . 1996.1 |===================================================================
Xmrig 6.21
Variant: Wownero - Hash Count: 1M
H/s > Higher Is Better
s . 2642.2 |==================================================================
b . 2684.0 |===================================================================
c . 2670.1 |===================================================================
Xmrig 6.21
Variant: GhostRider - Hash Count: 1M
H/s > Higher Is Better
s . 417.4 |================================================================
b . 442.7 |====================================================================
c . 434.2 |===================================================================
Xmrig 6.21
Variant: CryptoNight-Heavy - Hash Count: 1M
H/s > Higher Is Better
s . 1966.1 |==================================================================
b . 1986.6 |===================================================================
c . 1977.2 |===================================================================
Xmrig 6.21
Variant: CryptoNight-Femto UPX2 - Hash Count: 1M
H/s > Higher Is Better
s . 1961.9 |===================================================================
b . 1970.0 |===================================================================
c . 1967.6 |===================================================================
Neural Magic DeepSparse 1.6
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 3.7744 |===================================================================
b . 3.4805 |==============================================================
Neural Magic DeepSparse 1.6
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 3.2513 |===================================================================
b . 3.2516 |===================================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 120.39 |==========================================================
b . 139.49 |===================================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 119.40 |===================================================================
b . 100.90 |=========================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 51.75 |================================================================
b . 54.87 |====================================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 49.44 |==============================================================
b . 53.81 |====================================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 296.33 |===================================================================
b . 292.85 |==================================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 285.27 |===================================================================
b . 272.24 |================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 21.24 |===================================================================
b . 21.69 |====================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 19.07 |====================================================================
b . 19.08 |====================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 4.5305 |===============================================================
b . 4.7828 |===================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 4.0358 |===================================================================
b . 4.0391 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 51.26 |=============================================================
b . 56.80 |====================================================================
Neural Magic DeepSparse 1.6
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 50.67 |====================================================================
b . 50.03 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 22.86 |====================================================================
b . 21.11 |===============================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 19.26 |================================================================
b . 20.49 |====================================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 27.79 |===========================================================
b . 31.88 |====================================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 31.69 |====================================================================
b . 27.24 |==========================================================
Neural Magic DeepSparse 1.6
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 6.3326 |===================================================================
b . 6.3331 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 6.4333 |==================================================================
b . 6.5345 |===================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 52.44 |============================================================
b . 58.99 |====================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 54.73 |====================================================================
b . 44.48 |=======================================================
Neural Magic DeepSparse 1.6
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
s . 3.2662 |===========================================================
b . 3.6826 |===================================================================
Neural Magic DeepSparse 1.6
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
s . 3.2556 |==================================================================
b . 3.2899 |===================================================================
Java SciMark 2.2
Computational Test: Composite
Mflops > Higher Is Better
s . 2757.18 |=========================================================
b . 3083.83 |================================================================
c . 3169.70 |==================================================================
Java SciMark 2.2
Computational Test: Monte Carlo
Mflops > Higher Is Better
s . 1149.92 |==================================================================
b . 1137.14 |=================================================================
c . 1150.54 |==================================================================
Java SciMark 2.2
Computational Test: Fast Fourier Transform
Mflops > Higher Is Better
s . 520.78 |===================================================================
b . 518.57 |===================================================================
c . 521.89 |===================================================================
Java SciMark 2.2
Computational Test: Sparse Matrix Multiply
Mflops > Higher Is Better
s . 1780.87 |=============================================================
b . 1935.73 |==================================================================
c . 1893.67 |=================================================================
Java SciMark 2.2
Computational Test: Dense LU Matrix Factorization
Mflops > Higher Is Better
s . 8623.01 |=====================================================
b . 10120.18 |==============================================================
c . 10571.52 |=================================================================
Java SciMark 2.2
Computational Test: Jacobi Successive Over-Relaxation
Mflops > Higher Is Better
s . 1711.34 |==================================================================
b . 1707.52 |==================================================================
c . 1710.86 |==================================================================
WebP2 Image Encode 20220823
Encode Settings: Default
MP/s > Higher Is Better
s . 3.52 |====================================================================
b . 3.55 |=====================================================================
c . 3.54 |=====================================================================
WebP2 Image Encode 20220823
Encode Settings: Quality 75, Compression Effort 7
MP/s > Higher Is Better
s . 0.04 |=====================================================================
b . 0.04 |=====================================================================
c . 0.04 |=====================================================================
WebP2 Image Encode 20220823
Encode Settings: Quality 95, Compression Effort 7
MP/s > Higher Is Better
s . 0.02 |=====================================================================
b . 0.02 |=====================================================================
c . 0.02 |=====================================================================
WebP2 Image Encode 20220823
Encode Settings: Quality 100, Compression Effort 5
MP/s > Higher Is Better
s . 1.33 |============================================================
b . 1.51 |====================================================================
c . 1.53 |=====================================================================
WebP2 Image Encode 20220823
Encode Settings: Quality 100, Lossless Compression
MP/s > Higher Is Better
LeelaChessZero 0.30
Backend: BLAS
Nodes Per Second > Higher Is Better
s . 67 |=============================================================
b . 66 |============================================================
c . 78 |=======================================================================
LeelaChessZero 0.30
Backend: Eigen
Nodes Per Second > Higher Is Better
s . 52 |==================================================================
b . 52 |==================================================================
c . 56 |=======================================================================
ScyllaDB 5.2.9
Test: Writes
Op/s > Higher Is Better
s . 40254 |====================================================================
b . 40498 |====================================================================
OpenSSL
Algorithm: RSA4096
sign/s > Higher Is Better
s . 2415.5 |===================================================================
b . 2166.4 |============================================================
OpenSSL
Algorithm: RSA4096
verify/s > Higher Is Better
s . 65538.3 |==================================================================
b . 56122.7 |=========================================================
Neural Magic DeepSparse 1.6
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 529.86 |==============================================================
b . 574.38 |===================================================================
Neural Magic DeepSparse 1.6
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 307.56 |===================================================================
b . 307.52 |===================================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 16.58 |====================================================================
b . 14.31 |===========================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 8.3642 |=========================================================
b . 9.8981 |===================================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 38.62 |====================================================================
b . 36.42 |================================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 20.22 |====================================================================
b . 18.57 |==============================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 6.7235 |==================================================================
b . 6.8051 |===================================================================
Neural Magic DeepSparse 1.6
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 3.4944 |================================================================
b . 3.6618 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 94.11 |====================================================================
b . 92.17 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 52.41 |====================================================================
b . 52.40 |====================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 438.99 |===================================================================
b . 418.12 |================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 247.77 |===================================================================
b . 247.56 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 38.99 |====================================================================
b . 35.18 |=============================================================
Neural Magic DeepSparse 1.6
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 19.72 |===================================================================
b . 19.98 |====================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 87.48 |===============================================================
b . 94.71 |====================================================================
Neural Magic DeepSparse 1.6
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 51.90 |====================================================================
b . 48.79 |================================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 71.93 |====================================================================
b . 62.70 |===========================================================
Neural Magic DeepSparse 1.6
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 31.54 |==========================================================
b . 36.70 |====================================================================
Neural Magic DeepSparse 1.6
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 315.79 |===================================================================
b . 315.76 |===================================================================
Neural Magic DeepSparse 1.6
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 155.41 |===================================================================
b . 153.01 |==================================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 38.10 |====================================================================
b . 33.87 |============================================================
Neural Magic DeepSparse 1.6
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 18.26 |=======================================================
b . 22.47 |====================================================================
Neural Magic DeepSparse 1.6
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
s . 612.27 |===================================================================
b . 542.15 |===========================================================
Neural Magic DeepSparse 1.6
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
s . 307.14 |===================================================================
b . 303.94 |==================================================================