Percentile Cleanup, Processor Speed Hack, and Experimental Config (#257)
* chore: Hackily hardcode CPU speed * chore: dump execution profile in spec generation * chore: also generate dat * fix: awk off by one error * chore: write dummy row * chore: Remove ekf, up concur, up iter * chore: awk statistical significance * fix: wrap awk string in quotes * fix: add awk trailing delimiter * chore: disable ekf in deadline description * chore: Increase iters * chore: generate deadline key * chore: make percentile global * chore: update mix for new multiples * chore: deadline csv key * docs: Better explain how to run run experiments * refactor: Inital percentile table cleanup * refactor: additional percentile table cleanup * refactor: payload percentiles table * refactor: workload_mix percentile table * feat: final percentiles row stuff * chore: revert processor hackmaster
Sean McBride
4 years ago
committed by
GitHub
parent
d8f01eac21
commit
7640cd5fb6
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# shellcheck shell=bash
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source "type_checks.sh" || exit 1
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# These utility functions are used to generate percentile tables that summarize distributions of data.
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# Each row represents an independent variable, such as a scheduling policy
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# The data for each column is provided as a single column of sorted data
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# If the data is too course, a percentile might be statistically insignificant. If this is the case,
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# The script writes an * to the appropriate cell
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#
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# Example:
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#
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# percentiles_table_header "./table.csv"
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# for $variant in (fifo_nopreemption fifo_preemption edf_nopreemption edf_preemption); do
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# percentiles_table_row "./${variant}.csv" "./table.csv" "$variant"
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# done
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#
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# See Also:
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# - csv_to_dat - Can transform a table into a *.dat file suitable for gnuplot
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#
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# References
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# - The AWK Programming Language - https://ia802309.us.archive.org/25/items/pdfy-MgN0H1joIoDVoIC7/The_AWK_Programming_Language.pdf
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# - GAWK: Effective AWK Programming - https://www.gnu.org/software/gawk/manual/gawk.pdf
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percentiles_table_header() {
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local table_file="${1:?table_file not set}"
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# Can optionally override "app" in header
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local label_header="${2:-app}"
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echo "${label_header},cnt,min,mean,p50,p90,p99,max" > "$table_file"
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}
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# columnar_data_file is assumed to be a file containing a single column or sorted data
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percentiles_table_row() {
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local -r columnar_data_file="${1:?columnar_data_file not set}"
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check_file columnar_data_file
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local -r table_file="${2:?table_file not set}"
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check_file table_file
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local -r row_label="${3:?row_label not set}"
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local -r format_string="${4:-%1.4f}"
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# Count the number of results
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local -i sample_size
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sample_size=$(wc -l < "$columnar_data_file")
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if ((sample_size == 0)); then
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# We might not have actually run every variant depending on iterations and workload mix
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# Insert a degenerate row if this is the case
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echo "$row_label,0,*,*,*,*,*,*" >> "$table_file"
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else
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awk '
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BEGIN {
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sample_size='"$sample_size"'
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row_label="'"$row_label"'"
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format_string="'"$format_string"'"
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invalid_number_symbol="*"
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sum = 0
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p50_idx = int(sample_size * 0.5)
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p90_idx = int(sample_size * 0.9)
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p99_idx = int(sample_size * 0.99)
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p100_idx = sample_size
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}
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# Empty pattern matches all rows
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{ sum += $0 }
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NR==1 { min = sample_size > 0 ? sprintf(format_string, $0) : invalid_number_symbol }
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NR==p50_idx { p50 = sample_size >= 3 ? sprintf(format_string, $0) : invalid_number_symbol }
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NR==p90_idx { p90 = sample_size >= 10 ? sprintf(format_string, $0) : invalid_number_symbol }
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NR==p99_idx { p99 = sample_size >= 100 ? sprintf(format_string, $0) : invalid_number_symbol }
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NR==p100_idx { p100 = sample_size > 0 ? sprintf(format_string, $0) : invalid_number_symbol }
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END {
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mean = sample_size > 0 ? sprintf(format_string, sum / NR) : invalid_number_symbol
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printf "%s,%d,%s,%s,%s,%s,%s,%s\n", row_label, sample_size, min, mean, p50, p90, p99, p100
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}
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' < "$columnar_data_file" >> "$table_file"
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fi
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}
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