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Add new script for plotting results and rename bench
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‎bench/ndarray/roofline-plot.py‎

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#######################################################################
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# Copyright (c) 2019-present, Blosc Development Team <blosc@blosc.org>
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# All rights reserved.
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#
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# This source code is licensed under a BSD-style license (found in the
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# LICENSE file in the root directory of this source tree)
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#######################################################################
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# This plots the roofline results from the 7800X3D CPU when using
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# the flops-vs-arithmetic-intensity.py script. The default is potting
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# the disk results. To plot the in-memory results, set mem_mode=True
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# below.
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import matplotlib.pyplot as plt
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import ast
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mem_mode = False
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legend = "on-disk"
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result = """
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{'blosc2': {'low': {'GFLOPS': 6.327852773687432,
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'Intensity': 11.0,
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'Time': 78.22558736801147},
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'matmul0': {'GFLOPS': 139.6079603241232,
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'Intensity': 2000,
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'Time': 0.12893247604370117},
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'matmul1': {'GFLOPS': 285.5730560584079,
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'Intensity': 10000,
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'Time': 7.878894567489624},
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'matmul2': {'GFLOPS': 275.25046270149494,
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'Intensity': 20000,
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'Time': 65.39498543739319},
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'medium': {'GFLOPS': 41.090081101862665,
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'Intensity': 73.5,
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'Time': 80.49387860298157},
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'very low': {'GFLOPS': 1.8312096896017473,
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'Intensity': 0.5,
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'Time': 12.286959886550903}},
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'blosc2-nocomp': {'low': {'GFLOPS': 2.411916710121926,
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'Intensity': 11.0,
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'Time': 205.23096752166748},
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'matmul0': {'GFLOPS': 1.3107291477971774,
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'Intensity': 2000,
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'Time': 13.732814311981201},
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'matmul1': {'GFLOPS': 250.65634985918948,
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'Intensity': 10000,
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'Time': 8.976433277130127},
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'matmul2': {'GFLOPS': 259.70444823051554,
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'Intensity': 20000,
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'Time': 69.30955600738525},
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'medium': {'GFLOPS': 16.14344559666435,
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'Intensity': 73.5,
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'Time': 204.88191199302673},
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'very low': {'GFLOPS': 0.17230079619691702,
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'Intensity': 0.5,
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'Time': 130.58558344841003}},
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'numpy/numexpr': {'low': {'GFLOPS': 3.507544548886978,
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'Intensity': 11.0,
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'Time': 141.1243658065796},
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'matmul0': {'GFLOPS': 0.9969869122268231,
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'Intensity': 2000,
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'Time': 18.054399490356445},
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'matmul1': {'GFLOPS': 285.9938969965044,
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'Intensity': 10000,
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'Time': 7.867300748825073},
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'matmul2': {'GFLOPS': 331.3412081775571,
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'Intensity': 20000,
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'Time': 54.32466459274292},
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'medium': {'GFLOPS': 20.830858799336102,
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'Intensity': 73.5,
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'Time': 158.77885937690735},
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'very low': {'GFLOPS': 0.2501104809770888,
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'Intensity': 0.5,
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'Time': 89.96024441719055}}}
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"""
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result_mem = """
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{'blosc2': {'low': {'GFLOPS': 4.866522333848727,
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'Intensity': 11.0,
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'Time': 2.5428836345672607},
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'matmul0': {'GFLOPS': 85.0395948600573,
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'Intensity': 1000,
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'Time': 0.026458263397216797},
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'matmul1': {'GFLOPS': 268.91185402574024,
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'Intensity': 5000,
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'Time': 1.045881748199463},
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'matmul2': {'GFLOPS': 299.895418397909,
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'Intensity': 10000,
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'Time': 7.502615451812744},
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'medium': {'GFLOPS': 35.53410004647491,
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'Intensity': 73.5,
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'Time': 2.3269901275634766},
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'very low': {'GFLOPS': 0.9519585532368179,
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'Intensity': 0.5,
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'Time': 0.5908870697021484}},
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'blosc2-nocomp': {'low': {'GFLOPS': 4.6922734647067745,
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'Intensity': 11.0,
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'Time': 2.6373143196105957},
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'matmul0': {'GFLOPS': 58.503040710180954,
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'Intensity': 1000,
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'Time': 0.03845953941345215},
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'matmul1': {'GFLOPS': 273.2579472297275,
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'Intensity': 5000,
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'Time': 1.0292472839355469},
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'matmul2': {'GFLOPS': 301.0879153190238,
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'Intensity': 10000,
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'Time': 7.472900390625},
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'medium': {'GFLOPS': 32.61464583666784,
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'Intensity': 73.5,
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'Time': 2.535287380218506},
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'very low': {'GFLOPS': 0.6691234986315745,
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'Intensity': 0.5,
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'Time': 0.8406519889831543}},
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'numpy/numexpr': {'low': {'GFLOPS': 9.115128287351304,
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'Intensity': 11.0,
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'Time': 1.357633113861084},
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'matmul0': {'GFLOPS': 266.15855825365935,
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'Intensity': 1000,
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'Time': 0.008453607559204102},
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'matmul1': {'GFLOPS': 362.16460995052523,
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'Intensity': 5000,
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'Time': 0.7765805721282959},
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'matmul2': {'GFLOPS': 371.33744743751464,
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'Intensity': 10000,
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'Time': 6.059178829193115},
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'medium': {'GFLOPS': 46.40747408437248,
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'Intensity': 73.5,
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'Time': 1.781771183013916},
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'very low': {'GFLOPS': 2.681374682770323,
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'Intensity': 0.5,
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'Time': 0.20978045463562012}}}
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"""
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if mem_mode:
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result = result_mem
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legend = "in-memory"
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# Parse the result string as a dictionary
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results = ast.literal_eval(result)
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# Create roofline plot
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fig, ax = plt.subplots(figsize=(10, 6))
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# Define colors and markers for each backend
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styles = {
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'numpy/numexpr': {'color': 'blue', 'marker': 'o', 'label': 'NumPy/NumExpr'},
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'blosc2': {'color': 'red', 'marker': 's', 'label': 'Blosc2 (compressed)'},
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'blosc2-nocomp': {'color': 'green', 'marker': '^', 'label': 'Blosc2 (uncompressed)'}
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}
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# Plot each backend's results
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for backend, backend_results in results.items():
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intensities = []
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gflops = []
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labels = []
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for workload, metrics in backend_results.items():
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intensities.append(metrics['Intensity'])
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gflops.append(metrics['GFLOPS'])
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labels.append(workload)
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style = styles[backend]
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ax.loglog(intensities, gflops,
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marker=style['marker'],
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color=style['color'],
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label=style['label'],
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markersize=8,
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linestyle='',
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alpha=0.7)
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# Build a single annotation per unique x (Intensity)
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intensity_map = {}
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for backend_results in results.values():
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for workload, metrics in backend_results.items():
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intensity = metrics['Intensity']
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gflop = metrics['GFLOPS']
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# Apply same mapping used above, but only for annotation text
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if intensity not in intensity_map:
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intensity_map[intensity] = {'label': workload, 'gflops': []}
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intensity_map[intensity]['gflops'].append(gflop)
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# Ensure labels fit inside the plot by extending the lower y-limit if necessary
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all_min_gflops = [min(info['gflops']) for info in intensity_map.values()]
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cur_ymin, cur_ymax = ax.get_ylim()
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new_ymin = min(cur_ymin, min(all_min_gflops) * 0.5)
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ax.set_ylim(new_ymin, cur_ymax)
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# Annotate once per intensity, centered under the cluster of points
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for intensity, info in sorted(intensity_map.items()):
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# place label below the lowest point for this x, but keep a margin above x-axis
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raw_ypos = min(info['gflops']) * 0.6
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ymin, ymax = ax.get_ylim()
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# ensure at least 1.5x above current ymin to avoid overlapping the axis
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safe_ypos = max(raw_ypos, ymin * 1.5 if ymin > 0 else raw_ypos)
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ax.annotate(info['label'],
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(intensity, safe_ypos),
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ha='center',
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va='top',
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fontsize=10,
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alpha=0.9)
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ax.set_xlabel('Arithmetic Intensity (FLOPs/element)', fontsize=12)
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ax.set_ylabel('Performance (GFLOPS/sec)', fontsize=12)
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ax.set_title(f'Roofline Analysis: AMD 7800X3D ({legend})', fontsize=14, fontweight='bold')
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ax.legend(loc='best')
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ax.grid(False)
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plt.tight_layout()
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plt.savefig(f'roofline_plot-7800X3D-{legend}.png', dpi=300, bbox_inches='tight')
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plt.show()

‎bench/ndarray/flops-vs-arithmetic-intensity.py‎ renamed to ‎bench/roofline-analysis.py‎

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