# Copyright (c) 2021 The Regents of the University of California
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from typing import List, Union
from math import log
from gem5.utils.override import overrides
from gem5.components.processors.abstract_generator import (
AbstractGenerator,
partition_range,
)
from gem5.components.processors.linear_generator_core import (
LinearGeneratorCore,
)
from gem5.components.processors.random_generator_core import (
RandomGeneratorCore,
)
class HybridGenerator(AbstractGenerator):
def __init__(
self,
num_cores: int = 2,
duration: str = "1ms",
rate: str = "1GiB/s",
block_size: int = 8,
min_addr: int = 0,
max_addr: int = 131072,
rd_perc: int = 100,
data_limit: int = 0,
) -> None:
if num_cores < 2:
raise ValueError("num_cores should be >= 2!")
super().__init__(
cores=self._create_cores(
num_cores=num_cores,
duration=duration,
rate=rate,
block_size=block_size,
min_addr=min_addr,
max_addr=max_addr,
rd_perc=rd_perc,
data_limit=data_limit,
)
)
"""The hybrid generator
This class defines an external interface to create a list of linear and
random generator cores that could replace the processing cores in a board.
:param num_cores: The number of linear generator cores to create.
:param duration: The number of ticks for the generator to generate
traffic.
:param rate: The rate at which the synthetic data is read/written.
:param block_size: The number of bytes to be read/written with each
request.
:param min_addr: The lower bound of the address range the generator
will read/write from/to.
:param max_addr: The upper bound of the address range the generator
will read/write from/to.
:param rd_perc: The percentage of read requests among all the generated
requests. The write percentage would be equal to
``100 - rd_perc``.
:param data_limit: The amount of data in bytes to read/write by the
generator before stopping generation.
"""
def _create_cores(
self,
num_cores: int,
duration: str,
rate: str,
block_size: int,
min_addr: int,
max_addr: int,
rd_perc: int,
data_limit: int,
) -> List[Union[LinearGeneratorCore, RandomGeneratorCore]]:
"""
The helper function to create the cores for the generator, it will use
the same inputs as the constructor function.
"""
def get_num_linear_cores(num_cores: int):
"""
Returns the largest power of two that is smaller than num_cores
"""
if num_cores & (num_cores - 1) == 0:
return num_cores // 2
else:
return 2 ** int(log(num_cores, 2))
"""
Initializing variables
"""
core_list = []
# TODO (1): Assign the number of linear and random cores
"""
partition_range(): Partitions the range of min_addr to max_addr into
num_linear_cores sections
Each section is represented as a tuple <min, max>, where min
is the smallest address for that section and max is the largest
address for that section
"""
# TODO (2): Initialize the address_ranges variable
"""
The first n cores (where n is the largest power of 2 that is
less than num_cores) will be Linear Generator Cores
Each Linear Generator Core gets a section of memory, and all
Linear Generator Cores cover the range of min_addr to max_addr
"""
# (3): Instantiate all linear cores and add them to core_list
"""
The remaining cores will be Random Generator Cores
Each Random Generator Core covers the range of min_addr to max_addr
"""
# TODO (4): Instantiate all random cores and add them to core_list
"""
Return our list of cores
"""
return core_list
@overrides(AbstractGenerator)
def start_traffic(self) -> None:
for core in self.cores:
core.start_traffic()