""" DDCMPolicy Module:
This module contains the Dynamic Duty Cycle Modulation (DDCM) based policy
aimed at mitigating workload imbalance in parallel applications that use
barrier synchronizations. It reduces duty cycle of cpus not on the critical
path of execution thereby reducing energy with little or no adverse impact
on performance.
This implementation specifically targets Intel architecture.
Please check your architecture specification for supported power control
mechanisms and other information.
Additional information:
1. Bhalachandra, Sridutt, Allan Porterfield, and Jan F. Prins. "Using
dynamic duty cycle modulation to improve energy efficiency in high
performance computing." In Parallel and Distributed Processing Symposium
Workshop (IPDPSW), 2015 IEEE International, pp. 911-918. IEEE, 2015.
2. Porterfield, Allan, Rob Fowler, Sridutt Bhalachandra, Barry Rountree,
Diptorup Deb, and Rob Lewis. "Application runtime variability and power
optimization for exascale computers." In Proceedings of the 5th
International Workshop on Runtime and Operating Systems for Supercomputers,
p. 3. ACM, 2015.
"""
from __future__ import division
import math
import coolr
import coolr.dutycycle
class DDCMPolicy:
""" Contains cpu-specific DDCM based power policy """
def __init__(self, maxlevel=16, minlevel=1):
self.maxdclevel = maxlevel
self.mindclevel = minlevel
# Relaxation factor
self.relaxation = 1
self.ddcmpolicyset = 0
self.ddcmpolicyreset = 0
self.dc = coolr.dutycycle.DutyCycle()
def print_stats(self, resetflag=False):
ddcmstats = dict()
ddcmstats['DDCMPolicySets'] = self.ddcmpolicyset
ddcmstats['DDCMPolicyResets'] = self.ddcmpolicyreset
if resetflag:
self.ddcmpolicyset = 0
self.ddcmpolicyreset = 0
return ddcmstats
def execute(self, cpu, currentdclevel, computetime, totalphasetime):
# Compute work done by cpu during current phase
work = computetime / totalphasetime
# Compute effective work based on current duty cycle(dc) level
effectivework = work * self.maxdclevel / currentdclevel
# Compute effective slow down in current phase
effectiveslowdown = work * self.mindclevel / currentdclevel
# Decrease or keep constant dc level in the next phase if the effective
# work done is equal or less than 1.0
if effectivework <= 1.0:
self.ddcmpolicyset += 1
# Compute by how many levels dc needs to decrease
dcreduction = math.floor(effectivework / 0.0625) - 15
# Compute new dc level for next phase
if -14 < dcreduction < 0:
# Note that dcreduction is a negative value
newdclevel = currentdclevel + dcreduction + self.relaxation
elif dcreduction < -13:
# Empirical observation shows reducing dc below 18.75% leads to
# excessive slowdown
newdclevel = currentdclevel - 13
else:
# If reduction required is 0
newdclevel = currentdclevel
# If there was a slowdown in the last phase, then increase the duty
# cycle level corresponding to the slowdown
else:
self.ddcmpolicyreset += 1
# Compute by how many levels dc needs to increase
dcincrease = math.floor(effectiveslowdown / 0.0625)
newdclevel = currentdclevel + dcincrease
# Check if new dc level computed is within permissible range, else
# reset
if newdclevel < self.mindclevel or newdclevel > self.maxdclevel:
newdclevel = self.maxdclevel
# Set the duty cycle of cpu to the new value computed
self.dc.set(cpu, newdclevel)
return newdclevel