77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""Example configuration file
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This file illustrates how to define options and simulation parameter ranges.
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It also defines the traversal order of the simulation space. As the file
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extension suggests, configuration is pure python code, allowing complex
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setups. Use at your own risk.
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To use this example, run
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python3 study.py config_example
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Otherwise copy it and modify as needed. The default traversal order defined
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in the nested loop of nextShape() is good for most cases, but customizable
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if needed.
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"""
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import logging
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import itertools
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import numpy as np
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from DAS.shape import Shape
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dumpXML = 1
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visualization = 1
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logLevel = logging.INFO
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# number of parallel workers. -1: all cores; 1: sequential
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# for more details, see joblib.Parallel
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numJobs = 3
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# distribute rows/columns evenly between validators (True)
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# or generate it using local randomness (False)
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evenLineDistribution = False
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# Number of simulation runs with the same parameters for statistical relevance
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runs = range(10)
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# Number of validators
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numberNodes = range(256, 513, 128)
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# Percentage of block not released by producer
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failureRates = range(10, 91, 40)
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# Block size in one dimension in segments. Block is blockSizes * blockSizes segments.
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blockSizes = range(32,65,16)
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# Per-topic mesh neighborhood size
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netDegrees = range(6, 9, 2)
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# number of rows and columns a validator is interested in
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chis = range(1, 5, 2)
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# ratio of class1 nodes (see below for parameters per class)
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class1ratios = np.arange(0, 1, .2)
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# Number of validators per beacon node
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validatorsPerNode1 = [1]
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validatorsPerNode2 = [2, 4, 8, 16, 32]
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# Set uplink bandwidth. In segments (~560 bytes) per timestep (50ms?)
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# 1 Mbps ~= 1e6 / 20 / 8 / 560 ~= 11
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bwUplinksProd = [2200]
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bwUplinks1 = [110]
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bwUplinks2 = [2200]
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# Set to True if you want your run to be deterministic, False if not
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deterministic = False
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# If your run is deterministic you can decide the random seed. This is ignore otherwise.
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randomSeed = "DAS"
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def nextShape():
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for run, fr, class1ratio, chi, vpn1, vpn2, blockSize, nn, netDegree, bwUplinkProd, bwUplink1, bwUplink2 in itertools.product(
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runs, failureRates, class1ratios, chis, validatorsPerNode1, validatorsPerNode2, blockSizes, numberNodes, netDegrees, bwUplinksProd, bwUplinks1, bwUplinks2):
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# Network Degree has to be an even number
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if netDegree % 2 == 0:
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shape = Shape(blockSize, nn, fr, class1ratio, chi, vpn1, vpn2, netDegree, bwUplinkProd, bwUplink1, bwUplink2, run)
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yield shape
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