one jpeg xeon

Tests for a future article. 2 x INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS) and ASPEED on Fedora Linux 39 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 2403015-NE-ONEJPEGXE05
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one jpeg xeon Suite 1.0.0 System Test suite extracted from one jpeg xeon. pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 50 Input: JPEG - Quality: 90 pts/onednn-3.4.0 --conv --batch=inputs/conv/shapes_auto --engine=cpu Harness: Convolution Batch Shapes Auto - Engine: CPU pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 80 Input: JPEG - Quality: 80 pts/onednn-3.4.0 --ip --batch=inputs/ip/shapes_1d --engine=cpu Harness: IP Shapes 1D - Engine: CPU pts/onednn-3.4.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --engine=cpu Harness: Recurrent Neural Network Inference - Engine: CPU pts/onednn-3.4.0 --rnn --batch=inputs/rnn/perf_rnn_training --engine=cpu Harness: Recurrent Neural Network Training - Engine: CPU pts/jpegxl-1.6.0 sample-4.png out.jxl -q 80 --num_reps 80 Input: PNG - Quality: 80 pts/jpegxl-decode-1.6.0 --num_reps=250 CPU Threads: All pts/jpegxl-1.6.0 sample-4.png out.jxl -q 90 --num_reps 50 Input: PNG - Quality: 90 pts/onednn-3.4.0 --deconv --batch=inputs/deconv/shapes_1d --engine=cpu Harness: Deconvolution Batch shapes_1d - Engine: CPU pts/onednn-3.4.0 --deconv --batch=inputs/deconv/shapes_3d --engine=cpu Harness: Deconvolution Batch shapes_3d - Engine: CPU pts/jpegxl-1.6.0 sample-4.png out.jxl -q 100 --num_reps 20 Input: PNG - Quality: 100 pts/jpegxl-decode-1.6.0 --num_threads=1 --num_reps=90 CPU Threads: 1 pts/onednn-3.4.0 --ip --batch=inputs/ip/shapes_3d --engine=cpu Harness: IP Shapes 3D - Engine: CPU pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 20 Input: JPEG - Quality: 100 pts/encode-wavpack-1.5.0 WAV To WavPack