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Commit 33586400 authored by Millian Poquet's avatar Millian Poquet
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script: update

parent 2f1d7f7f
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...@@ -35,6 +35,8 @@ data_z_joinable = data_z %>% transmute( ...@@ -35,6 +35,8 @@ data_z_joinable = data_z %>% transmute(
data_nz = inner_join(data_nz, data_z_joinable) %>% mutate( data_nz = inner_join(data_nz, data_z_joinable) %>% mutate(
mean_turnaround_time_minus_zero = mean_turnaround_time - zero_mean_turnaround_time, mean_turnaround_time_minus_zero = mean_turnaround_time - zero_mean_turnaround_time,
mean_waiting_time_minus_zero = mean_waiting_time - zero_mean_waiting_time mean_waiting_time_minus_zero = mean_waiting_time - zero_mean_waiting_time
) %>% mutate(
mean_turnaround_time_increase_ratio = mean_turnaround_time_minus_zero / zero_mean_turnaround_time
) )
# energy diff from powercap, depending on powercap ratio and predictor # energy diff from powercap, depending on powercap ratio and predictor
...@@ -209,8 +211,40 @@ data_nz %>% ...@@ -209,8 +211,40 @@ data_nz %>%
scale=0.9 scale=0.9
ggsave(sprintf("%s/sched-mtt-distribution.pdf", output_dir), width=8*scale, height=4*scale) ggsave(sprintf("%s/sched-mtt-distribution.pdf", output_dir), width=8*scale, height=4*scale)
# enable comparison of mean power values between predictors for all (workload, powercap) tuples
# show all columns...
options(dplyr.width = Inf) options(dplyr.width = Inf)
# compute overall power under-utilization compared to the powercap
data_nz %>%
mutate(power_underutilization_ratio = (powercap_dynamic_watts - mean_power)/powercap_dynamic_watts) %>%
group_by(predictor_name) %>%
summarize(
min_power_underutilization_ratio = min(power_underutilization_ratio),
average_power_underutilization_ratio = mean(power_underutilization_ratio)
)
# compute overall mean turnaround time increase
data_nz %>%
filter(start_dt_s != outlier_workload_start_dt_s) %>%
group_by(predictor_name) %>%
summarize(
average_mtt_increase = mean(mean_turnaround_time_minus_zero),
average_mtt_increase_ratio = mean(mean_turnaround_time_increase_ratio)
)
# compute overall powercap breaks
data_nz %>%
mutate(powercap_break = pmax(max_power_from_powercap, 0)) %>%
mutate(powercap_break_ratio = powercap_break / powercap_dynamic_watts) %>%
group_by(predictor_name) %>%
summarize(
mean_max_power_from_powercap = mean(max_power_from_powercap),
min_max_power_from_powercap = min(max_power_from_powercap),
max_max_power_from_powercap = max(max_power_from_powercap),
)
# enable comparison of mean power values between predictors for all (workload, powercap) tuples
data_p_mean = data %>% pivot_wider(names_from = predictor_name, values_from = mean_power) %>% data_p_mean = data %>% pivot_wider(names_from = predictor_name, values_from = mean_power) %>%
replace_na(list(max=0,upper_bound=0,zero=0,real_max=0,mean=0,real_mean=0)) replace_na(list(max=0,upper_bound=0,zero=0,real_max=0,mean=0,real_mean=0))
...@@ -259,8 +293,6 @@ against_mean = data_p_mean %>% ...@@ -259,8 +293,6 @@ against_mean = data_p_mean %>%
instances_where_some_max_power_is_NOT_below_mean = against_mean %>% filter(!all_max_below_mean) instances_where_some_max_power_is_NOT_below_mean = against_mean %>% filter(!all_max_below_mean)
print(sprintf("number of occurrences where mean consumes more power than max/real_max: %d/%d", nrow(instances_where_some_max_power_is_NOT_below_mean), nrow(against_mean))) print(sprintf("number of occurrences where mean consumes more power than max/real_max: %d/%d", nrow(instances_where_some_max_power_is_NOT_below_mean), nrow(against_mean)))
data_p_mtt = data_nz %>% filter(start_dt_s != outlier_workload_start_dt_s) %>% data_p_mtt = data_nz %>% filter(start_dt_s != outlier_workload_start_dt_s) %>%
pivot_wider(names_from = predictor_name, values_from = mean_turnaround_time_minus_zero) %>% pivot_wider(names_from = predictor_name, values_from = mean_turnaround_time_minus_zero) %>%
replace_na(list(max=0,upper_bound=0,zero=0,real_max=0,mean=0,real_mean=0)) replace_na(list(max=0,upper_bound=0,zero=0,real_max=0,mean=0,real_mean=0))
...@@ -339,6 +371,3 @@ data %>% ggplot() + ...@@ -339,6 +371,3 @@ data %>% ggplot() +
x="Power predictor", x="Power predictor",
y="Distribution of the unused energy consumed during the constrained period for each simulation (GJ).\nComputed as the integral of the dynamic power minus the dynamic powercap value, only keeping negative values." y="Distribution of the unused energy consumed during the constrained period for each simulation (GJ).\nComputed as the integral of the dynamic power minus the dynamic powercap value, only keeping negative values."
) )
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