1091 lines
34 KiB
Python
1091 lines
34 KiB
Python
# Copyright (c) 2022-2023 The University of Edinburgh
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# All rights reserved.
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#
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# The license below extends only to copyright in the software and shall
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# not be construed as granting a license to any other intellectual
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# property including but not limited to intellectual property relating
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# to a hardware implementation of the functionality of the software
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# licensed hereunder. You may use the software subject to the license
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# terms below provided that you ensure that this notice is replicated
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# unmodified and in its entirety in all distributions of the software,
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# modified or unmodified, in source code or in binary form.
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#
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# Copyright (c) 2012 Mark D. Hill and David A. Wood
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# Copyright (c) 2015 The University of Wisconsin
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met: redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer;
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# redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution;
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# neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from m5.objects.ClockedObject import ClockedObject
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from m5.objects.IndexingPolicies import *
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from m5.objects.ReplacementPolicies import *
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from m5.params import *
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from m5.proxy import *
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from m5.SimObject import *
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class BranchType(Enum):
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vals = [
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"NoBranch",
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"Return",
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"CallDirect",
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"CallIndirect", # 'Call',
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"DirectCond",
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"DirectUncond", # 'Direct',
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"IndirectCond",
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"IndirectUncond", #'Indirect',
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]
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class TargetProvider(Enum):
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vals = [
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"NoTarget",
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"BTB",
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"RAS",
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"Indirect",
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]
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class ReturnAddrStack(SimObject):
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type = "ReturnAddrStack"
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cxx_class = "gem5::branch_prediction::ReturnAddrStack"
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cxx_header = "cpu/pred/ras.hh"
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numThreads = Param.Unsigned(Parent.numThreads, "Number of threads")
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numEntries = Param.Unsigned(16, "Number of RAS entries")
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class BranchTargetBuffer(ClockedObject):
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type = "BranchTargetBuffer"
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cxx_class = "gem5::branch_prediction::BranchTargetBuffer"
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cxx_header = "cpu/pred/btb.hh"
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abstract = True
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numThreads = Param.Unsigned(Parent.numThreads, "Number of threads")
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class BTBIndexingPolicy(SimObject):
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type = "BTBIndexingPolicy"
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abstract = True
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cxx_class = "gem5::IndexingPolicyTemplate<gem5::BTBTagType>"
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cxx_header = "cpu/pred/btb_entry.hh"
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cxx_template_params = ["class Types"]
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# Get the associativity
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assoc = Param.Int(Parent.assoc, "associativity")
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class BTBSetAssociative(BTBIndexingPolicy):
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type = "BTBSetAssociative"
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cxx_class = "gem5::BTBSetAssociative"
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cxx_header = "cpu/pred/btb_entry.hh"
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# Get the number of entries in the BTB from the parent
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num_entries = Param.Unsigned(
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Parent.numEntries, "Number of entries in the BTB"
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)
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# Set shift for the index. Ignore lower 2 bits for a 4 byte instruction.
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set_shift = Param.Unsigned(2, "Number of bits to shift PC to get index")
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# Total number of bits in the tag.
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# This is above the index and offset bit
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tag_bits = Param.Unsigned(64, "number of bits in the tag")
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# Number of threads sharing the BTB
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numThreads = Param.Unsigned(Parent.numThreads, "Number of threads")
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class SimpleBTB(BranchTargetBuffer):
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type = "SimpleBTB"
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cxx_class = "gem5::branch_prediction::SimpleBTB"
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cxx_header = "cpu/pred/simple_btb.hh"
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numEntries = Param.Unsigned(4096, "Number of BTB entries")
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tagBits = Param.Unsigned(16, "Size of the BTB tags, in bits")
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instShiftAmt = Param.Unsigned(
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Parent.instShiftAmt, "Number of bits to shift instructions by"
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)
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associativity = Param.Unsigned(1, "BTB associativity")
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btbReplPolicy = Param.BaseReplacementPolicy(
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LRURP(), "BTB replacement policy"
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)
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btbIndexingPolicy = Param.BTBIndexingPolicy(
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BTBSetAssociative(
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assoc=Parent.associativity,
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num_entries=Parent.numEntries,
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set_shift=Parent.instShiftAmt,
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numThreads=1,
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),
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"BTB indexing policy",
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)
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class IndirectPredictor(SimObject):
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type = "IndirectPredictor"
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cxx_class = "gem5::branch_prediction::IndirectPredictor"
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cxx_header = "cpu/pred/indirect.hh"
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abstract = True
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numThreads = Param.Unsigned(Parent.numThreads, "Number of threads")
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class SimpleIndirectPredictor(IndirectPredictor):
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type = "SimpleIndirectPredictor"
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cxx_class = "gem5::branch_prediction::SimpleIndirectPredictor"
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cxx_header = "cpu/pred/simple_indirect.hh"
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indirectHashGHR = Param.Bool(True, "Hash branch predictor GHR")
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indirectHashTargets = Param.Bool(True, "Hash path history targets")
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indirectSets = Param.Unsigned(256, "Cache sets for indirect predictor")
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indirectWays = Param.Unsigned(2, "Ways for indirect predictor")
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indirectTagSize = Param.Unsigned(16, "Indirect target cache tag bits")
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indirectPathLength = Param.Unsigned(
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3, "Previous indirect targets to use for path history"
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)
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speculativePathLength = Param.Unsigned(
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256,
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"Additional buffer space to store speculative path history. "
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"If there are more speculative branches in flight the history cannot "
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"be recovered. Set this to an appropriate value respective the CPU"
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"pipeline depth or a high value e.g. 256 to make it 'unlimited'.",
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)
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indirectGHRBits = Param.Unsigned(13, "Indirect GHR number of bits")
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instShiftAmt = Param.Unsigned(2, "Number of bits to shift instructions by")
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class BranchPredictor(SimObject):
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type = "BranchPredictor"
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cxx_class = "gem5::branch_prediction::BPredUnit"
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cxx_header = "cpu/pred/bpred_unit.hh"
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abstract = True
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numThreads = Param.Unsigned(Parent.numThreads, "Number of threads")
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instShiftAmt = Param.Unsigned(2, "Number of bits to shift instructions by")
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requiresBTBHit = Param.Bool(
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False,
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"Requires the BTB to hit for returns and indirect branches. For an"
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"advanced front-end there is no other way than a BTB hit to know "
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"that the branch exists in the first place. Furthermore, the BPU "
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"needs to know the branch type to make the correct RAS operations. "
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"This info is only available from the BTB. "
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"Low-end CPUs predecoding might be used to identify branches. ",
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)
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btb = Param.BranchTargetBuffer(SimpleBTB(), "Branch target buffer (BTB)")
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ras = Param.ReturnAddrStack(
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ReturnAddrStack(), "Return address stack, set to NULL to disable RAS."
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)
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indirectBranchPred = Param.IndirectPredictor(
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SimpleIndirectPredictor(),
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"Indirect branch predictor, set to NULL to disable "
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"indirect predictions",
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)
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class LocalBP(BranchPredictor):
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type = "LocalBP"
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cxx_class = "gem5::branch_prediction::LocalBP"
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cxx_header = "cpu/pred/2bit_local.hh"
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localPredictorSize = Param.Unsigned(2048, "Size of local predictor")
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localCtrBits = Param.Unsigned(2, "Bits per counter")
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class TournamentBP(BranchPredictor):
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type = "TournamentBP"
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cxx_class = "gem5::branch_prediction::TournamentBP"
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cxx_header = "cpu/pred/tournament.hh"
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localPredictorSize = Param.Unsigned(2048, "Size of local predictor")
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localCtrBits = Param.Unsigned(2, "Bits per counter")
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localHistoryTableSize = Param.Unsigned(2048, "size of local history table")
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globalPredictorSize = Param.Unsigned(8192, "Size of global predictor")
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globalCtrBits = Param.Unsigned(2, "Bits per counter")
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choicePredictorSize = Param.Unsigned(8192, "Size of choice predictor")
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choiceCtrBits = Param.Unsigned(2, "Bits of choice counters")
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class BiModeBP(BranchPredictor):
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type = "BiModeBP"
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cxx_class = "gem5::branch_prediction::BiModeBP"
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cxx_header = "cpu/pred/bi_mode.hh"
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globalPredictorSize = Param.Unsigned(8192, "Size of global predictor")
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globalCtrBits = Param.Unsigned(2, "Bits per counter")
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choicePredictorSize = Param.Unsigned(8192, "Size of choice predictor")
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choiceCtrBits = Param.Unsigned(2, "Bits of choice counters")
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class TAGEBase(SimObject):
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type = "TAGEBase"
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cxx_class = "gem5::branch_prediction::TAGEBase"
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cxx_header = "cpu/pred/tage_base.hh"
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numThreads = Param.Unsigned(Parent.numThreads, "Number of threads")
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instShiftAmt = Param.Unsigned(
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Parent.instShiftAmt, "Number of bits to shift instructions by"
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)
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nHistoryTables = Param.Unsigned(7, "Number of history tables")
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minHist = Param.Unsigned(5, "Minimum history size of TAGE")
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maxHist = Param.Unsigned(130, "Maximum history size of TAGE")
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tagTableTagWidths = VectorParam.Unsigned(
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[0, 9, 9, 10, 10, 11, 11, 12], "Tag size in TAGE tag tables"
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)
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logTagTableSizes = VectorParam.Int(
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[13, 9, 9, 9, 9, 9, 9, 9], "Log2 of TAGE table sizes"
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)
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logRatioBiModalHystEntries = Param.Unsigned(
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2,
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"Log num of prediction entries for a shared hysteresis bit "
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"for the Bimodal",
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)
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tagTableCounterBits = Param.Unsigned(3, "Number of tag table counter bits")
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tagTableUBits = Param.Unsigned(2, "Number of tag table u bits")
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histBufferSize = Param.Unsigned(
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2097152,
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"A large number to track all branch histories(2MEntries default)",
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)
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pathHistBits = Param.Unsigned(16, "Path history size")
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logUResetPeriod = Param.Unsigned(
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18, "Log period in number of branches to reset TAGE useful counters"
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)
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numUseAltOnNa = Param.Unsigned(1, "Number of USE_ALT_ON_NA counters")
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initialTCounterValue = Param.Int(1 << 17, "Initial value of tCounter")
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useAltOnNaBits = Param.Unsigned(4, "Size of the USE_ALT_ON_NA counter(s)")
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maxNumAlloc = Param.Unsigned(
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1, "Max number of TAGE entries allocted on mispredict"
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)
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# List of enabled TAGE tables. If empty, all are enabled
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noSkip = VectorParam.Bool([], "Vector of enabled TAGE tables")
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speculativeHistUpdate = Param.Bool(
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True, "Use speculative update for histories"
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)
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# TAGE branch predictor as described in https://www.jilp.org/vol8/v8paper1.pdf
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# The default sizes below are for the 8C-TAGE configuration (63.5 Kbits)
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class TAGE(BranchPredictor):
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type = "TAGE"
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cxx_class = "gem5::branch_prediction::TAGE"
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cxx_header = "cpu/pred/tage.hh"
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tage = Param.TAGEBase(TAGEBase(), "Tage object")
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class LTAGE_TAGE(TAGEBase):
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nHistoryTables = 12
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minHist = 4
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maxHist = 640
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tagTableTagWidths = [0, 7, 7, 8, 8, 9, 10, 11, 12, 12, 13, 14, 15]
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logTagTableSizes = [14, 10, 10, 11, 11, 11, 11, 10, 10, 10, 10, 9, 9]
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logUResetPeriod = 19
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class LoopPredictor(SimObject):
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type = "LoopPredictor"
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cxx_class = "gem5::branch_prediction::LoopPredictor"
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cxx_header = "cpu/pred/loop_predictor.hh"
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logSizeLoopPred = Param.Unsigned(8, "Log size of the loop predictor")
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withLoopBits = Param.Unsigned(7, "Size of the WITHLOOP counter")
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loopTableAgeBits = Param.Unsigned(8, "Number of age bits per loop entry")
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loopTableConfidenceBits = Param.Unsigned(
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2, "Number of confidence bits per loop entry"
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)
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loopTableTagBits = Param.Unsigned(14, "Number of tag bits per loop entry")
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loopTableIterBits = Param.Unsigned(14, "Nuber of iteration bits per loop")
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logLoopTableAssoc = Param.Unsigned(2, "Log loop predictor associativity")
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# Parameters for enabling modifications to the loop predictor
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# They have been copied from TAGE-GSC-IMLI
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# (http://www.irisa.fr/alf/downloads/seznec/TAGE-GSC-IMLI.tar)
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#
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# All of them should be disabled to match the original LTAGE implementation
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# (http://hpca23.cse.tamu.edu/taco/camino/cbp2/cbp-src/realistic-seznec.h)
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# Add speculation
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useSpeculation = Param.Bool(False, "Use speculation")
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# Add hashing for calculating the loop table index
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useHashing = Param.Bool(False, "Use hashing")
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# Add a direction bit to the loop table entries
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useDirectionBit = Param.Bool(False, "Use direction info")
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# If true, use random to decide whether to allocate or not, and only try
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# with one entry
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restrictAllocation = Param.Bool(
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False, "Restrict the allocation conditions"
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)
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initialLoopIter = Param.Unsigned(1, "Initial iteration number")
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initialLoopAge = Param.Unsigned(255, "Initial age value")
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optionalAgeReset = Param.Bool(
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True, "Reset age bits optionally in some cases"
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)
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class TAGE_SC_L_TAGE(TAGEBase):
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type = "TAGE_SC_L_TAGE"
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cxx_class = "gem5::branch_prediction::TAGE_SC_L_TAGE"
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cxx_header = "cpu/pred/tage_sc_l.hh"
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abstract = True
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tagTableTagWidths = [0]
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numUseAltOnNa = 16
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pathHistBits = 27
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maxNumAlloc = 2
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logUResetPeriod = 10
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initialTCounterValue = 1 << 9
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useAltOnNaBits = 5
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# TODO No speculation implemented as of now
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speculativeHistUpdate = False
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# This size does not set the final sizes of the tables (it is just used
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# for some calculations)
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# Instead, the number of TAGE entries comes from shortTagsTageEntries and
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# longTagsTageEntries
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logTagTableSize = Param.Unsigned("Log size of each tag table")
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shortTagsTageFactor = Param.Unsigned(
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"Factor for calculating the total number of short tags TAGE entries"
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)
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longTagsTageFactor = Param.Unsigned(
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"Factor for calculating the total number of long tags TAGE entries"
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)
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shortTagsSize = Param.Unsigned(8, "Size of the short tags")
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longTagsSize = Param.Unsigned("Size of the long tags")
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firstLongTagTable = Param.Unsigned("First table with long tags")
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truncatePathHist = Param.Bool(
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True, "Truncate the path history to its configured size"
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)
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class TAGE_SC_L_TAGE_64KB(TAGE_SC_L_TAGE):
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type = "TAGE_SC_L_TAGE_64KB"
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cxx_class = "gem5::branch_prediction::TAGE_SC_L_TAGE_64KB"
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cxx_header = "cpu/pred/tage_sc_l_64KB.hh"
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nHistoryTables = 36
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minHist = 6
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maxHist = 3000
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tagTableUBits = 1
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logTagTableSizes = [13]
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# This is used to handle the 2-way associativity
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# (all odd entries are set to one, and if the corresponding even entry
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# is set to one, then there is a 2-way associativity for this pair)
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# Entry 0 is for the bimodal and it is ignored
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# Note: For this implementation, some odd entries are also set to 0 to save
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# some bits
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noSkip = [
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0,
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0,
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1,
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0,
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0,
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0,
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1,
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0,
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0,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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0,
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1,
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0,
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1,
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0,
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1,
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0,
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0,
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0,
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1,
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0,
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0,
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0,
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1,
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]
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logTagTableSize = 10
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shortTagsTageFactor = 10
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longTagsTageFactor = 20
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longTagsSize = 12
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firstLongTagTable = 13
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class TAGE_SC_L_TAGE_8KB(TAGE_SC_L_TAGE):
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type = "TAGE_SC_L_TAGE_8KB"
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cxx_class = "gem5::branch_prediction::TAGE_SC_L_TAGE_8KB"
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cxx_header = "cpu/pred/tage_sc_l_8KB.hh"
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nHistoryTables = 30
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minHist = 4
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maxHist = 1000
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logTagTableSize = 7
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shortTagsTageFactor = 9
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longTagsTageFactor = 17
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longTagsSize = 12
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logTagTableSizes = [12]
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firstLongTagTable = 11
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truncatePathHist = False
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noSkip = [
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0,
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0,
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1,
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0,
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1,
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0,
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1,
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1,
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1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
0,
|
|
1,
|
|
0,
|
|
1,
|
|
0,
|
|
1,
|
|
0,
|
|
1,
|
|
0,
|
|
1,
|
|
]
|
|
|
|
tagTableUBits = 2
|
|
|
|
|
|
# LTAGE branch predictor as described in
|
|
# https://www.irisa.fr/caps/people/seznec/L-TAGE.pdf
|
|
# It is basically a TAGE predictor plus a loop predictor
|
|
# The differnt TAGE sizes are updated according to the paper values (256 Kbits)
|
|
class LTAGE(TAGE):
|
|
type = "LTAGE"
|
|
cxx_class = "gem5::branch_prediction::LTAGE"
|
|
cxx_header = "cpu/pred/ltage.hh"
|
|
|
|
tage = LTAGE_TAGE()
|
|
|
|
loop_predictor = Param.LoopPredictor(LoopPredictor(), "Loop predictor")
|
|
|
|
|
|
class TAGE_SC_L_LoopPredictor(LoopPredictor):
|
|
type = "TAGE_SC_L_LoopPredictor"
|
|
cxx_class = "gem5::branch_prediction::TAGE_SC_L_LoopPredictor"
|
|
cxx_header = "cpu/pred/tage_sc_l.hh"
|
|
|
|
loopTableAgeBits = 4
|
|
loopTableConfidenceBits = 4
|
|
loopTableTagBits = 10
|
|
loopTableIterBits = 10
|
|
useSpeculation = False
|
|
useHashing = True
|
|
useDirectionBit = True
|
|
restrictAllocation = True
|
|
initialLoopIter = 0
|
|
initialLoopAge = 7
|
|
optionalAgeReset = False
|
|
|
|
|
|
class StatisticalCorrector(SimObject):
|
|
type = "StatisticalCorrector"
|
|
cxx_class = "gem5::branch_prediction::StatisticalCorrector"
|
|
cxx_header = "cpu/pred/statistical_corrector.hh"
|
|
abstract = True
|
|
|
|
# Statistical corrector parameters
|
|
|
|
numEntriesFirstLocalHistories = Param.Unsigned(
|
|
"Number of entries for first local histories"
|
|
)
|
|
|
|
bwnb = Param.Unsigned("Num global backward branch GEHL lengths")
|
|
bwm = VectorParam.Int("Global backward branch GEHL lengths")
|
|
logBwnb = Param.Unsigned("Log num of global backward branch GEHL entries")
|
|
bwWeightInitValue = Param.Int(
|
|
"Initial value of the weights of the global backward branch GEHL entries"
|
|
)
|
|
|
|
lnb = Param.Unsigned("Num first local history GEHL lenghts")
|
|
lm = VectorParam.Int("First local history GEHL lengths")
|
|
logLnb = Param.Unsigned("Log number of first local history GEHL entries")
|
|
lWeightInitValue = Param.Int(
|
|
"Initial value of the weights of the first local history GEHL entries"
|
|
)
|
|
|
|
inb = Param.Unsigned(1, "Num IMLI GEHL lenghts")
|
|
im = VectorParam.Int([8], "IMLI history GEHL lengths")
|
|
logInb = Param.Unsigned("Log number of IMLI GEHL entries")
|
|
iWeightInitValue = Param.Int(
|
|
"Initial value of the weights of the IMLI history GEHL entries"
|
|
)
|
|
|
|
logBias = Param.Unsigned("Log size of Bias tables")
|
|
|
|
logSizeUp = Param.Unsigned(
|
|
6, "Log size of update threshold counters tables"
|
|
)
|
|
|
|
chooserConfWidth = Param.Unsigned(
|
|
7, "Number of bits for the chooser counters"
|
|
)
|
|
|
|
updateThresholdWidth = Param.Unsigned(
|
|
12, "Number of bits for the update threshold counter"
|
|
)
|
|
|
|
pUpdateThresholdWidth = Param.Unsigned(
|
|
8, "Number of bits for the pUpdate threshold counters"
|
|
)
|
|
|
|
extraWeightsWidth = Param.Unsigned(
|
|
6, "Number of bits for the extra weights"
|
|
)
|
|
|
|
scCountersWidth = Param.Unsigned(6, "Statistical corrector counters width")
|
|
|
|
initialUpdateThresholdValue = Param.Int(
|
|
0, "Initial pUpdate threshold counter value"
|
|
)
|
|
|
|
|
|
# TAGE-SC-L branch predictor as desribed in
|
|
# https://www.jilp.org/cbp2016/paper/AndreSeznecLimited.pdf
|
|
# It is a modified LTAGE predictor plus a statistical corrector predictor
|
|
# The TAGE modifications include bank interleaving and partial associativity
|
|
# Two different sizes are proposed in the paper:
|
|
# 8KB => See TAGE_SC_L_8KB below
|
|
# 64KB => See TAGE_SC_L_64KB below
|
|
# The TAGE_SC_L_8KB and TAGE_SC_L_64KB classes differ not only on the values
|
|
# of some parameters, but also in some implementation details
|
|
# Given this, the TAGE_SC_L class is left abstract
|
|
# Note that as it is now, this branch predictor does not handle any type
|
|
# of speculation: All the structures/histories are updated at commit time
|
|
class TAGE_SC_L(LTAGE):
|
|
type = "TAGE_SC_L"
|
|
cxx_class = "gem5::branch_prediction::TAGE_SC_L"
|
|
cxx_header = "cpu/pred/tage_sc_l.hh"
|
|
abstract = True
|
|
|
|
statistical_corrector = Param.StatisticalCorrector("Statistical Corrector")
|
|
|
|
|
|
class TAGE_SC_L_64KB_LoopPredictor(TAGE_SC_L_LoopPredictor):
|
|
logSizeLoopPred = 5
|
|
|
|
|
|
class TAGE_SC_L_8KB_LoopPredictor(TAGE_SC_L_LoopPredictor):
|
|
logSizeLoopPred = 3
|
|
|
|
|
|
class TAGE_SC_L_64KB_StatisticalCorrector(StatisticalCorrector):
|
|
type = "TAGE_SC_L_64KB_StatisticalCorrector"
|
|
cxx_class = "gem5::branch_prediction::TAGE_SC_L_64KB_StatisticalCorrector"
|
|
cxx_header = "cpu/pred/tage_sc_l_64KB.hh"
|
|
|
|
pnb = Param.Unsigned(3, "Num variation global branch GEHL lengths")
|
|
pm = VectorParam.Int([25, 16, 9], "Variation global branch GEHL lengths")
|
|
logPnb = Param.Unsigned(
|
|
9, "Log number of variation global branch GEHL entries"
|
|
)
|
|
|
|
snb = Param.Unsigned(3, "Num second local history GEHL lenghts")
|
|
sm = VectorParam.Int([16, 11, 6], "Second local history GEHL lengths")
|
|
logSnb = Param.Unsigned(
|
|
9, "Log number of second local history GEHL entries"
|
|
)
|
|
|
|
tnb = Param.Unsigned(2, "Num third local history GEHL lenghts")
|
|
tm = VectorParam.Int([9, 4], "Third local history GEHL lengths")
|
|
logTnb = Param.Unsigned(
|
|
10, "Log number of third local history GEHL entries"
|
|
)
|
|
|
|
imnb = Param.Unsigned(2, "Num second IMLI GEHL lenghts")
|
|
imm = VectorParam.Int([10, 4], "Second IMLI history GEHL lengths")
|
|
logImnb = Param.Unsigned(9, "Log number of second IMLI GEHL entries")
|
|
|
|
numEntriesSecondLocalHistories = Param.Unsigned(
|
|
16, "Number of entries for second local histories"
|
|
)
|
|
numEntriesThirdLocalHistories = Param.Unsigned(
|
|
16, "Number of entries for second local histories"
|
|
)
|
|
|
|
numEntriesFirstLocalHistories = 256
|
|
|
|
logBias = 8
|
|
|
|
bwnb = 3
|
|
bwm = [40, 24, 10]
|
|
logBwnb = 10
|
|
bwWeightInitValue = 7
|
|
|
|
lnb = 3
|
|
lm = [11, 6, 3]
|
|
logLnb = 10
|
|
lWeightInitValue = 7
|
|
|
|
logInb = 8
|
|
iWeightInitValue = 7
|
|
|
|
|
|
class TAGE_SC_L_8KB_StatisticalCorrector(StatisticalCorrector):
|
|
type = "TAGE_SC_L_8KB_StatisticalCorrector"
|
|
cxx_class = "gem5::branch_prediction::TAGE_SC_L_8KB_StatisticalCorrector"
|
|
cxx_header = "cpu/pred/tage_sc_l_8KB.hh"
|
|
|
|
gnb = Param.Unsigned(2, "Num global branch GEHL lengths")
|
|
gm = VectorParam.Int([6, 3], "Global branch GEHL lengths")
|
|
logGnb = Param.Unsigned(7, "Log number of global branch GEHL entries")
|
|
|
|
numEntriesFirstLocalHistories = 64
|
|
|
|
logBias = 7
|
|
|
|
bwnb = 2
|
|
logBwnb = 7
|
|
bwm = [16, 8]
|
|
bwWeightInitValue = 7
|
|
|
|
lnb = 2
|
|
logLnb = 7
|
|
lm = [6, 3]
|
|
lWeightInitValue = 7
|
|
|
|
logInb = 7
|
|
iWeightInitValue = 7
|
|
|
|
|
|
# 64KB TAGE-SC-L branch predictor as described in
|
|
# http://www.jilp.org/cbp2016/paper/AndreSeznecLimited.pdf
|
|
class TAGE_SC_L_64KB(TAGE_SC_L):
|
|
type = "TAGE_SC_L_64KB"
|
|
cxx_class = "gem5::branch_prediction::TAGE_SC_L_64KB"
|
|
cxx_header = "cpu/pred/tage_sc_l_64KB.hh"
|
|
|
|
tage = TAGE_SC_L_TAGE_64KB()
|
|
loop_predictor = TAGE_SC_L_64KB_LoopPredictor()
|
|
statistical_corrector = TAGE_SC_L_64KB_StatisticalCorrector()
|
|
|
|
|
|
# 8KB TAGE-SC-L branch predictor as described in
|
|
# http://www.jilp.org/cbp2016/paper/AndreSeznecLimited.pdf
|
|
class TAGE_SC_L_8KB(TAGE_SC_L):
|
|
type = "TAGE_SC_L_8KB"
|
|
cxx_class = "gem5::branch_prediction::TAGE_SC_L_8KB"
|
|
cxx_header = "cpu/pred/tage_sc_l_8KB.hh"
|
|
|
|
tage = TAGE_SC_L_TAGE_8KB()
|
|
loop_predictor = TAGE_SC_L_8KB_LoopPredictor()
|
|
statistical_corrector = TAGE_SC_L_8KB_StatisticalCorrector()
|
|
|
|
|
|
class MultiperspectivePerceptron(BranchPredictor):
|
|
type = "MultiperspectivePerceptron"
|
|
cxx_class = "gem5::branch_prediction::MultiperspectivePerceptron"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron.hh"
|
|
abstract = True
|
|
|
|
num_filter_entries = Param.Int("Number of filter entries")
|
|
num_local_histories = Param.Int("Number of local history entries")
|
|
local_history_length = Param.Int(
|
|
11, "Length in bits of each history entry"
|
|
)
|
|
|
|
block_size = Param.Int(
|
|
21,
|
|
"number of ghist bits in a 'block'; this is the width of an initial "
|
|
"hash of ghist",
|
|
)
|
|
pcshift = Param.Int(-10, "Shift for hashing PC")
|
|
threshold = Param.Int(1, "Threshold for deciding low/high confidence")
|
|
bias0 = Param.Int(
|
|
-5, "Bias perceptron output this much on all-bits-zero local history"
|
|
)
|
|
bias1 = Param.Int(
|
|
5, "Bias perceptron output this much on all-bits-one local history"
|
|
)
|
|
biasmostly0 = Param.Int(
|
|
-1,
|
|
"Bias perceptron output this much on almost-all-bits-zero local "
|
|
"history",
|
|
)
|
|
biasmostly1 = Param.Int(
|
|
1,
|
|
"Bias perceptron output this much on almost-all-bits-one local "
|
|
"history",
|
|
)
|
|
nbest = Param.Int(
|
|
20,
|
|
"Use this many of the top performing tables on a low-confidence "
|
|
"branch",
|
|
)
|
|
tunebits = Param.Int(24, "Number of bits in misprediction counters")
|
|
hshift = Param.Int(
|
|
-6,
|
|
"How much to shift initial feauture hash before XORing with PC bits",
|
|
)
|
|
imli_mask1 = Param.UInt64(
|
|
"Which tables should have their indices hashed with the first IMLI "
|
|
"counter"
|
|
)
|
|
imli_mask4 = Param.UInt64(
|
|
"Which tables should have their indices hashed with the fourth IMLI "
|
|
"counter"
|
|
)
|
|
recencypos_mask = Param.UInt64(
|
|
"Which tables should have their indices hashed with the recency "
|
|
"position"
|
|
)
|
|
fudge = Param.Float(0.245, "Fudge factor to multiply by perceptron output")
|
|
n_sign_bits = Param.Int(2, "Number of sign bits per magnitude")
|
|
pcbit = Param.Int(2, "Bit from the PC to use for hashing global history")
|
|
decay = Param.Int(0, "Whether and how often to decay a random weight")
|
|
record_mask = Param.Int(
|
|
191, "Which histories are updated with filtered branch outcomes"
|
|
)
|
|
hash_taken = Param.Bool(
|
|
False, "Hash the taken/not taken value with a PC bit"
|
|
)
|
|
tuneonly = Param.Bool(
|
|
True, "If true, only count mispredictions of low-confidence branches"
|
|
)
|
|
extra_rounds = Param.Int(
|
|
1,
|
|
"Number of extra rounds of training a single weight on a "
|
|
"low-confidence prediction",
|
|
)
|
|
speed = Param.Int(9, "Adaptive theta learning speed")
|
|
initial_theta = Param.Int(10, "Initial theta")
|
|
budgetbits = Param.Int("Hardware budget in bits")
|
|
speculative_update = Param.Bool(
|
|
False, "Use speculative update for histories"
|
|
)
|
|
|
|
initial_ghist_length = Param.Int(1, "Initial GHist length value")
|
|
ignore_path_size = Param.Bool(False, "Ignore the path storage")
|
|
|
|
|
|
class MultiperspectivePerceptron8KB(MultiperspectivePerceptron):
|
|
type = "MultiperspectivePerceptron8KB"
|
|
cxx_class = "gem5::branch_prediction::MultiperspectivePerceptron8KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_8KB.hh"
|
|
|
|
budgetbits = 8192 * 8 + 2048
|
|
num_local_histories = 48
|
|
num_filter_entries = 0
|
|
imli_mask1 = 0x6
|
|
imli_mask4 = 0x4400
|
|
recencypos_mask = 0x100000090
|
|
|
|
|
|
class MultiperspectivePerceptron64KB(MultiperspectivePerceptron):
|
|
type = "MultiperspectivePerceptron64KB"
|
|
cxx_class = "gem5::branch_prediction::MultiperspectivePerceptron64KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_64KB.hh"
|
|
|
|
budgetbits = 65536 * 8 + 2048
|
|
num_local_histories = 510
|
|
num_filter_entries = 18025
|
|
imli_mask1 = 0xC1000
|
|
imli_mask4 = 0x80008000
|
|
recencypos_mask = 0x100000090
|
|
|
|
|
|
class MPP_TAGE(TAGEBase):
|
|
type = "MPP_TAGE"
|
|
cxx_class = "gem5::branch_prediction::MPP_TAGE"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage.hh"
|
|
|
|
nHistoryTables = 15
|
|
pathHistBits = 27
|
|
instShiftAmt = 0
|
|
histBufferSize = 16384
|
|
maxHist = 4096
|
|
tagTableTagWidths = [
|
|
0,
|
|
7,
|
|
9,
|
|
9,
|
|
9,
|
|
10,
|
|
11,
|
|
11,
|
|
12,
|
|
12,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
15,
|
|
15,
|
|
]
|
|
logTagTableSizes = [
|
|
14,
|
|
10,
|
|
11,
|
|
11,
|
|
11,
|
|
11,
|
|
11,
|
|
12,
|
|
12,
|
|
10,
|
|
11,
|
|
11,
|
|
9,
|
|
7,
|
|
7,
|
|
8,
|
|
]
|
|
tunedHistoryLengths = VectorParam.Unsigned(
|
|
[0, 5, 12, 15, 21, 31, 43, 64, 93, 137, 200, 292, 424, 612, 877, 1241],
|
|
"Tuned history lengths",
|
|
)
|
|
|
|
logUResetPeriod = 10
|
|
initialTCounterValue = 0
|
|
numUseAltOnNa = 512
|
|
speculativeHistUpdate = False
|
|
|
|
|
|
class MPP_LoopPredictor(LoopPredictor):
|
|
type = "MPP_LoopPredictor"
|
|
cxx_class = "gem5::branch_prediction::MPP_LoopPredictor"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage.hh"
|
|
|
|
useDirectionBit = True
|
|
useHashing = True
|
|
useSpeculation = False
|
|
loopTableConfidenceBits = 4
|
|
loopTableAgeBits = 4
|
|
initialLoopAge = 7
|
|
initialLoopIter = 0
|
|
loopTableIterBits = 12
|
|
optionalAgeReset = False
|
|
restrictAllocation = True
|
|
logSizeLoopPred = 6
|
|
loopTableTagBits = 10
|
|
|
|
|
|
class MPP_StatisticalCorrector(StatisticalCorrector):
|
|
type = "MPP_StatisticalCorrector"
|
|
cxx_class = "gem5::branch_prediction::MPP_StatisticalCorrector"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage.hh"
|
|
abstract = True
|
|
|
|
# Unused in this Statistical Corrector
|
|
bwnb = 0
|
|
bwm = []
|
|
logBwnb = 0
|
|
bwWeightInitValue = -1
|
|
|
|
# Unused in this Statistical Corrector
|
|
logInb = 0
|
|
iWeightInitValue = -1
|
|
|
|
extraWeightsWidth = 0
|
|
pUpdateThresholdWidth = 10
|
|
initialUpdateThresholdValue = 35
|
|
logSizeUp = 5
|
|
|
|
lnb = 3
|
|
lm = [11, 6, 3]
|
|
logLnb = 10
|
|
lWeightInitValue = -1
|
|
|
|
gnb = Param.Unsigned(4, "Num global branch GEHL lengths")
|
|
gm = VectorParam.Int([27, 22, 17, 14], "Global branch GEHL lengths")
|
|
logGnb = Param.Unsigned(10, "Log number of global branch GEHL entries")
|
|
|
|
pnb = Param.Unsigned(4, "Num variation global branch GEHL lengths")
|
|
pm = VectorParam.Int(
|
|
[16, 11, 6, 3], "Variation global branch GEHL lengths"
|
|
)
|
|
logPnb = Param.Unsigned(
|
|
9, "Log number of variation global branch GEHL entries"
|
|
)
|
|
|
|
|
|
class MultiperspectivePerceptronTAGE(MultiperspectivePerceptron):
|
|
type = "MultiperspectivePerceptronTAGE"
|
|
cxx_class = "gem5::branch_prediction::MultiperspectivePerceptronTAGE"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage.hh"
|
|
abstract = True
|
|
|
|
instShiftAmt = 4
|
|
|
|
imli_mask1 = 0x70
|
|
imli_mask4 = 0
|
|
num_filter_entries = 0
|
|
num_local_histories = 0
|
|
recencypos_mask = 0 # Unused
|
|
threshold = -1
|
|
initial_ghist_length = 0
|
|
ignore_path_size = True
|
|
n_sign_bits = 1
|
|
|
|
tage = Param.TAGEBase("Tage object")
|
|
loop_predictor = Param.LoopPredictor("Loop predictor")
|
|
statistical_corrector = Param.StatisticalCorrector("Statistical Corrector")
|
|
|
|
|
|
class MPP_StatisticalCorrector_64KB(MPP_StatisticalCorrector):
|
|
type = "MPP_StatisticalCorrector_64KB"
|
|
cxx_class = "gem5::branch_prediction::MPP_StatisticalCorrector_64KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage_64KB.hh"
|
|
|
|
logBias = 8
|
|
|
|
snb = Param.Unsigned(4, "Num second local history GEHL lenghts")
|
|
sm = VectorParam.Int([16, 11, 6, 3], "Second local history GEHL lengths")
|
|
logSnb = Param.Unsigned(
|
|
9, "Log number of second local history GEHL entries"
|
|
)
|
|
|
|
tnb = Param.Unsigned(3, "Num third local history GEHL lenghts")
|
|
tm = VectorParam.Int([22, 17, 14], "Third local history GEHL lengths")
|
|
logTnb = Param.Unsigned(
|
|
9, "Log number of third local history GEHL entries"
|
|
)
|
|
|
|
numEntriesSecondLocalHistories = Param.Unsigned(
|
|
16, "Number of entries for second local histories"
|
|
)
|
|
numEntriesThirdLocalHistories = Param.Unsigned(
|
|
16, "Number of entries for second local histories"
|
|
)
|
|
|
|
numEntriesFirstLocalHistories = 256
|
|
|
|
|
|
class MultiperspectivePerceptronTAGE64KB(MultiperspectivePerceptronTAGE):
|
|
type = "MultiperspectivePerceptronTAGE64KB"
|
|
cxx_class = "gem5::branch_prediction::MultiperspectivePerceptronTAGE64KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage_64KB.hh"
|
|
|
|
budgetbits = 65536 * 8 + 2048
|
|
|
|
tage = MPP_TAGE()
|
|
loop_predictor = MPP_LoopPredictor()
|
|
statistical_corrector = MPP_StatisticalCorrector_64KB()
|
|
|
|
|
|
class MPP_TAGE_8KB(MPP_TAGE):
|
|
type = "MPP_TAGE_8KB"
|
|
cxx_class = "gem5::branch_prediction::MPP_TAGE_8KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage_8KB.hh"
|
|
|
|
nHistoryTables = 10
|
|
tagTableTagWidths = [0, 7, 7, 7, 8, 9, 10, 10, 11, 13, 13]
|
|
logTagTableSizes = [12, 8, 8, 9, 9, 8, 8, 8, 7, 6, 7]
|
|
tunedHistoryLengths = [0, 4, 8, 13, 23, 36, 56, 93, 145, 226, 359]
|
|
|
|
|
|
class MPP_LoopPredictor_8KB(MPP_LoopPredictor):
|
|
type = "MPP_LoopPredictor_8KB"
|
|
cxx_class = "gem5::branch_prediction::MPP_LoopPredictor_8KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage_8KB.hh"
|
|
|
|
loopTableIterBits = 10
|
|
logSizeLoopPred = 4
|
|
|
|
|
|
class MPP_StatisticalCorrector_8KB(MPP_StatisticalCorrector):
|
|
type = "MPP_StatisticalCorrector_8KB"
|
|
cxx_class = "gem5::branch_prediction::MPP_StatisticalCorrector_8KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage_8KB.hh"
|
|
|
|
logBias = 7
|
|
|
|
lnb = 2
|
|
lm = [8, 3]
|
|
logLnb = 9
|
|
|
|
logGnb = 9
|
|
|
|
logPnb = 7
|
|
|
|
numEntriesFirstLocalHistories = 64
|
|
|
|
|
|
class MultiperspectivePerceptronTAGE8KB(MultiperspectivePerceptronTAGE):
|
|
type = "MultiperspectivePerceptronTAGE8KB"
|
|
cxx_class = "gem5::branch_prediction::MultiperspectivePerceptronTAGE8KB"
|
|
cxx_header = "cpu/pred/multiperspective_perceptron_tage_8KB.hh"
|
|
|
|
budgetbits = 8192 * 8 + 2048
|
|
|
|
tage = MPP_TAGE_8KB()
|
|
loop_predictor = MPP_LoopPredictor_8KB()
|
|
statistical_corrector = MPP_StatisticalCorrector_8KB()
|