macminim4pro-python

Apple M4 Pro testing with a Apple Mac mini and Apple M4 Pro on macOS 15.2 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 2501132-NE-MACMINIM416
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Date
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  Test
  Duration
MAC mini m4 PRO python benchmark
January 13
  1 Hour, 48 Minutes
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MAC mini m4 PRO python benchmark

Processor: Apple M4 Pro (14 Cores), Motherboard: Apple Mac mini, Memory: 24GB, Disk: 461GB, Graphics: Apple M4 Pro, Monitor: LC49G95T

OS: macOS 15.2, Kernel: 24.2.0 (arm64), OpenCL: OpenCL 1.2 (Nov 9 2024 22:11:50), Compiler: GCC 16.0.0 + Clang 16.0.0 + Xcode 16.1, File-System: APFS, Screen Resolution: 2560x1440

Environment Notes: XPC_FLAGS=0x0
Python Notes: Python 3.12.7

macminim4pro-pythonOpenBenchmarking.orgPhoronix Test SuiteApple M4 Pro (14 Cores)Apple Mac mini24GB461GBApple M4 ProLC49G95TmacOS 15.224.2.0 (arm64)OpenCL 1.2 (Nov 9 2024 22:11:50)GCC 16.0.0 + Clang 16.0.0 + Xcode 16.1APFS2560x1440ProcessorMotherboardMemoryDiskGraphicsMonitorOSKernelOpenCLCompilerFile-SystemScreen ResolutionMacminim4pro-python BenchmarksSystem Logs- XPC_FLAGS=0x0- Python 3.12.7

macminim4pro-pythonnumenta-nab: KNN CADnumenta-nab: Relative Entropypyperformance: asyncio_tcp_sslpyperformance: xml_etreepyperformance: async_tree_ionumpy: pyperformance: python_startupnumenta-nab: Earthgecko Skylinepyperformance: asyncio_websocketspyhpc: CPU - Numpy - 4194304 - Isoneutral Mixingpyperformance: gc_collectpyperformance: pickle_pure_pythonpyhpc: CPU - Numba - 4194304 - Isoneutral Mixingpyhpc: CPU - Numba - 1048576 - Isoneutral Mixingnumenta-nab: Bayesian Changepointpyperformance: crypto_pyaespyperformance: floatpyperformance: raytracepyperformance: django_templatepyperformance: pathlibpyperformance: gopyhpc: CPU - Numpy - 4194304 - Equation of Statepyhpc: CPU - Numba - 65536 - Equation of Statepyperformance: regex_compilepyperformance: chaospyperformance: json_loadspyhpc: CPU - Numpy - 65536 - Isoneutral Mixingpyperformance: nbodynumenta-nab: Contextual Anomaly Detector OSEpyhpc: CPU - Numpy - 1048576 - Equation of Statepyhpc: CPU - Numba - 4194304 - Equation of Statepyhpc: CPU - Numba - 65536 - Isoneutral Mixingpyhpc: CPU - Numpy - 16384 - Equation of Statepyhpc: CPU - Numpy - 1048576 - Isoneutral Mixingpyhpc: CPU - Numpy - 262144 - Isoneutral Mixingpybench: Total For Average Test Timespyhpc: CPU - Numba - 262144 - Equation of Statepyhpc: CPU - Numpy - 65536 - Equation of Statepyhpc: CPU - Numba - 262144 - Isoneutral Mixingpyhpc: CPU - Numpy - 16384 - Isoneutral Mixingnumenta-nab: Windowed Gaussianpyhpc: CPU - Numpy - 262144 - Equation of Statepyhpc: CPU - Numba - 1048576 - Equation of Statepyhpc: CPU - Numba - 16384 - Isoneutral Mixingpyhpc: CPU - PyTorch - 1048576 - Isoneutral MixingMAC mini m4 PRO python benchmark240.89956.93869927.9512887.327.5059.1953311.1865601460.4900.11317.92640.438.915715.612.972.50.7050.00257.130.111.40.01254.520.6930.1430.1180.0070.0020.2730.0524270.0070.0080.0320.0045.5480.0400.0290.001OpenBenchmarking.org

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: KNN CADMAC mini m4 PRO python benchmark50100150200250SE +/- 2.26, N = 7240.90

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropyMAC mini m4 PRO python benchmark1326395265SE +/- 0.89, N = 1556.94

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: asyncio_tcp_sslMAC mini m4 PRO python benchmark150300450600750SE +/- 1.53, N = 3699

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: xml_etreeMAC mini m4 PRO python benchmark714212835SE +/- 0.03, N = 327.9

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: async_tree_ioMAC mini m4 PRO python benchmark110220330440550SE +/- 0.67, N = 3512

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy BenchmarkMAC mini m4 PRO python benchmark2004006008001000SE +/- 8.05, N = 3887.32

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: python_startupMAC mini m4 PRO python benchmark246810SE +/- 0.01, N = 37.50

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineMAC mini m4 PRO python benchmark1326395265SE +/- 0.27, N = 359.20

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: asyncio_websocketsMAC mini m4 PRO python benchmark70140210280350SE +/- 2.67, N = 3331

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.26690.53380.80071.06761.3345SE +/- 0.004, N = 31.186

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: gc_collectMAC mini m4 PRO python benchmark120240360480600SE +/- 0.67, N = 3560

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: pickle_pure_pythonMAC mini m4 PRO python benchmark306090120150SE +/- 0.33, N = 3146

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.11030.22060.33090.44120.5515SE +/- 0.001, N = 30.490

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.02540.05080.07620.10160.127SE +/- 0.000, N = 30.113

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointMAC mini m4 PRO python benchmark48121620SE +/- 0.20, N = 517.93

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: crypto_pyaesMAC mini m4 PRO python benchmark918273645SE +/- 0.03, N = 340.4

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: floatMAC mini m4 PRO python benchmark918273645SE +/- 0.03, N = 338.9

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: raytraceMAC mini m4 PRO python benchmark306090120150SE +/- 0.00, N = 3157

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: django_templateMAC mini m4 PRO python benchmark48121620SE +/- 0.00, N = 315.6

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: pathlibMAC mini m4 PRO python benchmark3691215SE +/- 0.09, N = 312.9

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: goMAC mini m4 PRO python benchmark1632486480SE +/- 0.03, N = 372.5

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.15860.31720.47580.63440.793SE +/- 0.001, N = 30.705

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.00050.0010.00150.0020.0025SE +/- 0.000, N = 30.002

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: regex_compileMAC mini m4 PRO python benchmark1326395265SE +/- 0.00, N = 357.1

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: chaosMAC mini m4 PRO python benchmark714212835SE +/- 0.03, N = 330.1

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: json_loadsMAC mini m4 PRO python benchmark3691215SE +/- 0.03, N = 311.4

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.00270.00540.00810.01080.0135SE +/- 0.000, N = 30.012

PyPerformance

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.11Benchmark: nbodyMAC mini m4 PRO python benchmark1224364860SE +/- 0.06, N = 354.5

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Contextual Anomaly Detector OSEMAC mini m4 PRO python benchmark510152025SE +/- 0.21, N = 320.69

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.03220.06440.09660.12880.161SE +/- 0.000, N = 30.143

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.02660.05320.07980.10640.133SE +/- 0.000, N = 30.118

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.00160.00320.00480.00640.008SE +/- 0.000, N = 30.007

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.00050.0010.00150.0020.0025SE +/- 0.000, N = 30.002

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.06140.12280.18420.24560.307SE +/- 0.004, N = 30.273

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.01170.02340.03510.04680.0585SE +/- 0.000, N = 30.052

PyBench

This test profile reports the total time of the different average timed test results from PyBench. PyBench reports average test times for different functions such as BuiltinFunctionCalls and NestedForLoops, with this total result providing a rough estimate as to Python's average performance on a given system. This test profile runs PyBench each time for 20 rounds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test TimesMAC mini m4 PRO python benchmark90180270360450SE +/- 1.86, N = 3427

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.00160.00320.00480.00640.008SE +/- 0.000, N = 30.007

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.00180.00360.00540.00720.009SE +/- 0.000, N = 30.008

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.00720.01440.02160.02880.036SE +/- 0.000, N = 30.032

Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.00090.00180.00270.00360.0045SE +/- 0.000, N = 30.004

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianMAC mini m4 PRO python benchmark1.24832.49663.74494.99326.2415SE +/- 0.031, N = 35.548

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.0090.0180.0270.0360.045SE +/- 0.000, N = 30.040

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Equation of StateMAC mini m4 PRO python benchmark0.00650.0130.01950.0260.0325SE +/- 0.000, N = 30.029

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Isoneutral MixingMAC mini m4 PRO python benchmark0.00020.00040.00060.00080.001SE +/- 0.000, N = 30.001

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Equation of State

MAC mini m4 PRO python benchmark: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Isoneutral Mixing

MAC mini m4 PRO python benchmark: The test run did not produce a result.

MAC mini m4 PRO python benchmark

Processor: Apple M4 Pro (14 Cores), Motherboard: Apple Mac mini, Memory: 24GB, Disk: 461GB, Graphics: Apple M4 Pro, Monitor: LC49G95T

OS: macOS 15.2, Kernel: 24.2.0 (arm64), OpenCL: OpenCL 1.2 (Nov 9 2024 22:11:50), Compiler: GCC 16.0.0 + Clang 16.0.0 + Xcode 16.1, File-System: APFS, Screen Resolution: 2560x1440

Environment Notes: XPC_FLAGS=0x0
Python Notes: Python 3.12.7

Testing initiated at 13 January 2025 12:43 by user chionyenkwu.