BloomFilters.py revision 14264
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27# Authors: Daniel Carvalho
28
29from m5.params import *
30from m5.proxy import *
31from m5.SimObject import SimObject
32
33class BloomFilterBase(SimObject):
34    type = 'BloomFilterBase'
35    abstract = True
36    cxx_header = "base/filters/base.hh"
37    cxx_class = 'BloomFilter::Base'
38
39    size = Param.Int(4096, "Number of entries in the filter")
40
41    # By default assume that bloom filters are used for 64-byte cache lines
42    offset_bits = Param.Unsigned(6, "Number of bits in a cache line offset")
43
44    # Most of the filters are booleans, and thus saturate on 1
45    num_bits = Param.Int(1, "Number of bits in a filter entry")
46    threshold = Param.Int(1, "Value at which an entry is considered as set")
47
48class BloomFilterBlock(BloomFilterBase):
49    type = 'BloomFilterBlock'
50    cxx_class = 'BloomFilter::Block'
51    cxx_header = "base/filters/block_bloom_filter.hh"
52
53    masks_lsbs = VectorParam.Unsigned([Self.offset_bits,
54        2 * Self.offset_bits], "Position of the LSB of each mask")
55    masks_sizes = VectorParam.Unsigned([Self.offset_bits, Self.offset_bits],
56        "Size, in number of bits, of each mask")
57
58class BloomFilterMultiBitSel(BloomFilterBase):
59    type = 'BloomFilterMultiBitSel'
60    cxx_class = 'BloomFilter::MultiBitSel'
61    cxx_header = "base/filters/multi_bit_sel_bloom_filter.hh"
62
63    num_hashes = Param.Int(4, "Number of hashes")
64    threshold = Self.num_hashes
65    skip_bits = Param.Int(2, "Offset from block number")
66    is_parallel = Param.Bool(False, "Whether hashing is done in parallel")
67
68class BloomFilterBulk(BloomFilterMultiBitSel):
69    type = 'BloomFilterBulk'
70    cxx_class = 'BloomFilter::Bulk'
71    cxx_header = "base/filters/bulk_bloom_filter.hh"
72
73class BloomFilterH3(BloomFilterMultiBitSel):
74    type = 'BloomFilterH3'
75    cxx_class = 'BloomFilter::H3'
76    cxx_header = "base/filters/h3_bloom_filter.hh"
77
78class BloomFilterMulti(BloomFilterBase):
79    type = 'BloomFilterMulti'
80    cxx_class = 'BloomFilter::Multi'
81    cxx_header = "base/filters/multi_bloom_filter.hh"
82
83    # The base filter should not be used, since this filter is the combination
84    # of multiple sub-filters, so we use a dummy value
85    size = 1
86
87    # By default there are two sub-filters that hash sequential bitfields
88    filters = VectorParam.BloomFilterBase([
89        BloomFilterBlock(size = 4096, masks_lsbs = [6, 12]),
90        BloomFilterBlock(size = 1024, masks_lsbs = [18, 24])],
91        "Sub-filters to be combined")
92
93    # By default match this with the number of sub-filters
94    threshold = 2
95