initial commit for public eval
This commit is contained in:
Sebastian Rust
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.git
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local.mk
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analysis/.venv
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out/
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derived/
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figures/out.bak/
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derived/*
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out/
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local.mk
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analysis/.venv/
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+27
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FROM rocker/tidyverse:4.4.1
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RUN apt-get update && apt-get install -y --no-install-recommends \
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make \
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curl \
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ca-certificates \
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texlive-latex-base \
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texlive-latex-recommended \
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texlive-fonts-recommended \
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&& rm -rf /var/lib/apt/lists/*
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ENV RENV_CONFIG_REPOS_OVERRIDE=https://packagemanager.posit.co/cran/__linux__/jammy/latest \
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RENV_CONFIG_AUTOLOADER_ENABLED=FALSE
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COPY figures/renv.lock /tmp/figures/renv.lock
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RUN R -e 'install.packages("renv"); setwd("/tmp/figures"); renv::restore(prompt = FALSE)' && rm -rf /tmp/figures
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ADD https://astral.sh/uv/install.sh /uv-installer.sh
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RUN sh /uv-installer.sh && rm /uv-installer.sh
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ENV UV_PROJECT_ENVIRONMENT=/opt/venv \
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PATH=/opt/venv/bin:/root/.local/bin:$PATH
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COPY analysis/pyproject.toml analysis/uv.lock /tmp/analysis/
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RUN cd /tmp/analysis && uv sync --frozen --no-install-project && rm -rf /tmp/analysis
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WORKDIR /work/eval
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# Configuration precedence (highest to lowest):
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# 1. command line: `make RAW_DATA_ROOT=/mnt/data ...`
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# 2. environment: `RAW_DATA_ROOT=/mnt/data make ...`
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# 3. local.mk file (Make syntax, use ?= so env still wins)
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# 4. Makefile default (../raw_data)
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-include local.mk
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export RAW_DATA_ROOT
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.PHONY: all figures tikz values derive sanity clean
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all: figures values
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figures:
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$(MAKE) -C figures figures
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tikz:
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$(MAKE) -C figures tikz
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values:
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$(MAKE) -C figures values
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derive:
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$(MAKE) -C figures derive
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sanity:
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$(MAKE) -C figures sanity
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clean:
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$(MAKE) -C figures clean
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# Evaluation pipeline
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Aggregates raw per-run measurements into figures (PDF + tikz) and a pgfkeys
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`values.tex` for the paper.
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## Run
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With Docker (reproducible toolchain):
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docker compose up --build
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Outputs land in `out/`:
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- `out/values.tex` -- all `\val{...}` keys for the paper
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- `out/<experiment>/*.pdf` -- per-experiment figures
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- `out/<experiment>/*.tex` -- tikz versions (via `make tikz`)
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Without Docker, requires R (with renv), Python 3.12+, uv, LaTeX (for
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tikzDevice's metric probe). Then:
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make all
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## Targets
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- `make all` (= `figures values`)
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- `make figures` -- PDFs for all experiments
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- `make tikz` -- tikz `.tex` for all experiments
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- `make values` -- regenerate `out/values.tex` only
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- `make derive` -- aggregate raw data into `derived/<experiment>/*.csv`
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- `make sanity` -- shape + NaN + solution-coverage checks on derived CSVs
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## Configuration
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`RAW_DATA_ROOT` controls where the aggregator reads raw aggregates from.
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Precedence (highest first):
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1. command line: `make RAW_DATA_ROOT=/mnt/data ...`
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2. environment: `RAW_DATA_ROOT=/mnt/data make ...`
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3. `local.mk` (copy from `local.mk.example`)
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4. Makefile default: `../raw_data`
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For Docker, the same variable picks the host path that gets bind-mounted as
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`/raw_data` inside the container:
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RAW_DATA_ROOT=/mnt/data docker compose up
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## Layout
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analysis/ Python: aggregation, sanity, gen_values, plugins
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values/ one plugin per metric family (cpu, rtt, idt, ...)
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figures/ R: ggplot scripts, common.R, renv.lock
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out/ generated (gitignored)
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derived/ intermediate CSVs (gitignored)
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## Plugins
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`analysis/values/*.py` each expose `compute(derived) -> (keys, sources)`.
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Keys are pgfkeys paths (e.g. `datacenter-fq/sender-cpu/cake/mean-pct`);
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`gen_values.py` merges them, wraps numbers in `\qty{}{}` / `\num{}` by
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suffix, and writes one `values.tex`. Add a new metric by dropping a new
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plugin into `analysis/values/`.
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.venv/
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__pycache__/
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#!/usr/bin/env python3
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"""Aggregate raw per-run data into tidy unit-of-observation CSVs.
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Outputs four CSVs into --out:
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runs.csv one row per run: setup, solution, run, throughput_bps, cpu_sender, cpu_receiver
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rtts.csv one row per RTT sample: setup, solution, rtt_us
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idts.csv one row per per-flow inter-departure-time: setup, solution, run, stream_id, idt_us
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firstflow_bins.csv one row per 50 us bin (first stream of run 1): setup, solution, t_ms, packets
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"""
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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import pandas as pd
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BIN_US = 50
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MIN_PACKETS_PER_STREAM = 100
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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p.add_argument("--raw", required=True, type=Path, help="raw aggregates directory")
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p.add_argument("--setup-prefix", required=True, help="directory prefix before _<solution>")
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p.add_argument("--solutions", required=True, nargs="+",
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help="solution names. Default lookup: <setup-prefix>_<solution>/. "
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"Use 'label:dirname' to point a solution at a non-conforming directory "
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"(e.g. cake:datacenter_cake_no-tso).")
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p.add_argument("--out", required=True, type=Path, help="derived CSV output directory")
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return p.parse_args()
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def solution_spec(spec: str, setup_prefix: str) -> tuple[str, str]:
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if ":" in spec:
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label, dirname = spec.split(":", 1)
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return label, dirname
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return spec, f"{setup_prefix}_{spec}"
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def load_metrics(sol_dir: Path) -> pd.DataFrame:
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df = pd.read_csv(sol_dir / "metrics.csv")
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return df[["run_num", "throughput_bps", "cpu_sender", "cpu_receiver"]].rename(columns={"run_num": "run"})
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def load_rtts(sol_dir: Path) -> pd.DataFrame:
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with open(sol_dir / "rtt.json") as f:
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rtts = json.load(f)
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return pd.DataFrame({"rtt_us": pd.Series(rtts, dtype="float64")})
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def load_packets(sol_dir: Path) -> pd.DataFrame:
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df = pd.read_csv(sol_dir / "packets.csv")
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df = df[["run_num", "stream_id", "p4_timestamp_ns"]].rename(columns={"run_num": "run"})
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df = df.astype({"run": "int64", "stream_id": "int64", "p4_timestamp_ns": "int64"})
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stream_counts = df.groupby("stream_id").size()
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keep = stream_counts[stream_counts >= MIN_PACKETS_PER_STREAM].index
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df = df[df["stream_id"].isin(keep)]
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return df.sort_values("p4_timestamp_ns", kind="mergesort").reset_index(drop=True)
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def per_flow_idts(packets: pd.DataFrame) -> pd.DataFrame:
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rows = []
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for (run, stream_id), df_flow in packets.groupby(["run", "stream_id"], sort=True):
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t = df_flow["p4_timestamp_ns"].to_numpy(dtype=np.float64)
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if len(t) < 2:
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continue
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idts = np.diff(t) / 1e3
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rows.append(pd.DataFrame({"run": run, "stream_id": stream_id, "idt_us": idts}))
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return pd.concat(rows, ignore_index=True)
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def firstflow_bins(packets: pd.DataFrame, bin_us: int) -> pd.DataFrame:
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first_run = int(packets["run"].min())
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df_run = packets[packets["run"] == first_run]
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first_stream = int(df_run["stream_id"].min())
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df_flow = df_run[df_run["stream_id"] == first_stream].sort_values("p4_timestamp_ns", kind="mergesort")
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assert len(df_flow) >= MIN_PACKETS_PER_STREAM, f"first flow (run={first_run}, stream={first_stream}) too small"
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t_s = df_flow["p4_timestamp_ns"].to_numpy(dtype=np.float64) / 1e9
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bin_width_s = bin_us / 1e6
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t0 = float(t_s.min())
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idx = np.floor((t_s - t0) / bin_width_s).astype(np.int64)
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n_bins = int(idx.max() + 1)
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counts = np.bincount(idx, minlength=n_bins)
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t_ms = (np.arange(n_bins) * bin_width_s) * 1000.0
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return pd.DataFrame({"t_ms": t_ms, "packets": counts})
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def main() -> None:
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args = parse_args()
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args.out.mkdir(parents=True, exist_ok=True)
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runs_frames = []
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rtts_frames = []
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idts_frames = []
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bins_frames = []
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for spec in args.solutions:
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label, dirname = solution_spec(spec, args.setup_prefix)
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sol_dir = args.raw / dirname
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if not sol_dir.is_dir():
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raise SystemExit(f"missing solution directory: {sol_dir}")
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print(f" {label} (from {dirname})")
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runs = load_metrics(sol_dir)
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runs.insert(0, "solution", label)
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runs.insert(0, "setup", args.setup_prefix)
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runs_frames.append(runs)
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rtts = load_rtts(sol_dir)
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rtts.insert(0, "solution", label)
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rtts.insert(0, "setup", args.setup_prefix)
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rtts_frames.append(rtts)
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if not (sol_dir / "packets.csv").exists():
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print(f" no packets.csv, skipping idts + firstflow_bins")
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continue
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packets = load_packets(sol_dir)
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if packets.empty:
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print(f" no packet data, skipping idts + firstflow_bins")
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continue
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idts = per_flow_idts(packets)
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idts.insert(0, "solution", label)
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idts.insert(0, "setup", args.setup_prefix)
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idts_frames.append(idts)
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bins = firstflow_bins(packets, BIN_US)
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bins.insert(0, "solution", label)
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bins.insert(0, "setup", args.setup_prefix)
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bins_frames.append(bins)
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pd.concat(runs_frames, ignore_index=True).to_csv(args.out / "runs.csv", index=False)
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print(f"wrote {args.out / 'runs.csv'}")
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pd.concat(rtts_frames, ignore_index=True).to_csv(args.out / "rtts.csv", index=False)
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print(f"wrote {args.out / 'rtts.csv'}")
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if idts_frames:
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pd.concat(idts_frames, ignore_index=True).to_csv(args.out / "idts.csv", index=False)
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print(f"wrote {args.out / 'idts.csv'}")
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if bins_frames:
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pd.concat(bins_frames, ignore_index=True).to_csv(args.out / "firstflow_bins.csv", index=False)
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print(f"wrote {args.out / 'firstflow_bins.csv'}")
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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"""Check raw_data for duplicate per-run metrics rows."""
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from __future__ import annotations
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import csv
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import hashlib
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from pathlib import Path
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import argparse
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RAW = Path(__file__).parent.parent / "raw_data"
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def metrics_payload(p: Path) -> tuple[str, ...]:
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with p.open() as f:
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rows = list(csv.reader(f))
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return tuple(rows[1][2:])
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def packet_signature(p: Path) -> tuple[int, int, tuple[int, ...], int, str]:
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size = p.stat().st_size
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streams: dict[int, int] = {}
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timestamps: list[int] = []
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with p.open() as f:
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next(f)
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for line in f:
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parts = line.rstrip().split(",")
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streams[int(parts[2])] = streams.get(int(parts[2]), 0) + 1
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timestamps.append(int(parts[3]))
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timestamps.sort()
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span = timestamps[-1] - timestamps[0] if timestamps else 0
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ts_hash = hashlib.sha1(",".join(map(str, timestamps)).encode()).hexdigest()[:12]
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return size, len(timestamps), tuple(sorted(streams)), span, ts_hash
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def check_solution(sol_dir: Path) -> list[str]:
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payloads: dict[tuple[str, ...], list[int]] = {}
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for run_dir in sol_dir.glob("tmp_run_*"):
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n = int(run_dir.name.removeprefix("tmp_run_"))
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try:
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payloads.setdefault(metrics_payload(run_dir / "metrics_row.csv"), []).append(n)
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except FileNotFoundError:
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continue
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report = []
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for payload, runs in payloads.items():
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if len(runs) < 2:
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continue
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runs.sort()
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base = runs[0]
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for other in runs[1:]:
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base_pp = sol_dir / f"tmp_run_{base}" / "parsed_packets.csv"
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other_pp = sol_dir / f"tmp_run_{other}" / "parsed_packets.csv"
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if not base_pp.exists() or not other_pp.exists():
|
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report.append(
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f" run {base:>2} = run {other:<2} metrics: same "
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f"parsed_packets: MISSING -> duplicate confirmed via metrics only"
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)
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continue
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sig_base, sig_other = packet_signature(base_pp), packet_signature(other_pp)
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ts_match = (sig_base[4] == sig_other[4])
|
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if ts_match:
|
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verdict = "DEEPER BUG (timestamps identical despite different stream IDs)"
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elif sig_base[1] == sig_other[1] and sig_base[3] == sig_other[3]:
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||||
verdict = "BUG + suspicious (rows + span match but timestamps differ)"
|
||||
else:
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||||
verdict = "BUG (independent captures, metrics row stamped from another run)"
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||||
report.append(
|
||||
f" run {base:>2} = run {other:<2} metrics: same -> {verdict}\n"
|
||||
f" run {base:>2}: size={sig_base[0]:>10} rows={sig_base[1]:>7} "
|
||||
f"streams={list(sig_base[2])} span_ns={sig_base[3]} ts_hash={sig_base[4]}\n"
|
||||
f" run {other:>2}: size={sig_other[0]:>10} rows={sig_other[1]:>7} "
|
||||
f"streams={list(sig_other[2])} span_ns={sig_other[3]} ts_hash={sig_other[4]}"
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||||
)
|
||||
return report
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--data", type=Path, default=RAW, help="raw_data root directory")
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||||
args = parser.parse_args()
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||||
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||||
total_bugs = 0
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||||
for exp_dir in sorted(args.data.iterdir()):
|
||||
agg = exp_dir / "aggregates"
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||||
if not agg.is_dir():
|
||||
continue
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||||
for sol_dir in sorted(agg.iterdir()):
|
||||
if not sol_dir.is_dir():
|
||||
continue
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||||
lines = check_solution(sol_dir)
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||||
if not lines:
|
||||
continue
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||||
print(f"\n== {exp_dir.name}/{sol_dir.name} ==")
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||||
for line in lines:
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||||
print(line)
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||||
if "BUG" in line:
|
||||
total_bugs += 1
|
||||
print(f"\nconfirmed BUG count: {total_bugs}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,99 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Generate values.tex from plugins under analysis/values/.
|
||||
|
||||
Each plugin exposes compute(derived) -> (keys, sources). Keys are merged into
|
||||
one \\pgfkeys block; sources are recorded in the header for provenance.
|
||||
"""
|
||||
import argparse
|
||||
import datetime as dt
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
from types import ModuleType
|
||||
|
||||
def load_plugins(plugin_dir: Path, skip: set[str]) -> list[tuple[str, ModuleType]]:
|
||||
plugins: list[tuple[str, ModuleType]] = []
|
||||
for p in sorted(plugin_dir.glob("*.py")):
|
||||
if p.name.startswith("_") or p.stem in skip:
|
||||
continue
|
||||
spec = importlib.util.spec_from_file_location(f"values.{p.stem}", p)
|
||||
if spec is None or spec.loader is None:
|
||||
raise SystemExit(f"could not load plugin {p}")
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
if hasattr(mod, "compute"):
|
||||
plugins.append((p.stem, mod))
|
||||
return plugins
|
||||
|
||||
|
||||
def file_mtime_iso(p: Path) -> str:
|
||||
return dt.datetime.fromtimestamp(p.stat().st_mtime).isoformat(timespec="seconds")
|
||||
|
||||
|
||||
# Map key-name suffix to siunitx unit.
|
||||
UNIT_SUFFIXES = {
|
||||
"-us": r"\micro\second",
|
||||
"-ms": r"\milli\second",
|
||||
"-gbps": r"\giga\bit\per\second",
|
||||
"-pct": r"\percent",
|
||||
"-kb": r"\kilo\byte",
|
||||
}
|
||||
|
||||
|
||||
NUM_SUFFIXES = ("-p", "-alpha", "-n", "-uncorrected")
|
||||
NUM_PREFIXES = ("n-",)
|
||||
|
||||
|
||||
def value_to_latex(key: str, value: str) -> str:
|
||||
leaf = key.rsplit("/", 1)[-1]
|
||||
for suffix, unit in UNIT_SUFFIXES.items():
|
||||
if leaf.endswith(suffix):
|
||||
return f"\\qty{{{value}}}{{{unit}}}"
|
||||
if any(leaf.startswith(p) for p in NUM_PREFIXES) or any(leaf.endswith(s) for s in NUM_SUFFIXES):
|
||||
return f"\\num{{{value}}}"
|
||||
return value
|
||||
|
||||
|
||||
def format_pgfkeys(all_keys: dict[str, str], provenance: list[str]) -> str:
|
||||
lines: list[str] = []
|
||||
lines.append("% Auto-generated by analysis/gen_values.py. Do not edit by hand.")
|
||||
lines.append(f"% Generated: {dt.datetime.now().isoformat(timespec='seconds')}")
|
||||
lines.extend(provenance)
|
||||
for k, v in sorted(all_keys.items()):
|
||||
lines.append(f"\\pgfkeyssetvalue{{/{k}}}{{{value_to_latex(k, v)}}}")
|
||||
return "\n".join(lines) + "\n"
|
||||
|
||||
|
||||
def main() -> None:
|
||||
p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
p.add_argument("--derived", required=True, type=Path, help="derived/ root with per-experiment subdirs")
|
||||
p.add_argument("--out", required=True, type=Path, help="output values.tex path")
|
||||
p.add_argument("--plugins", type=Path, default=Path(__file__).parent / "values",
|
||||
help="plugin directory (default: analysis/values)")
|
||||
p.add_argument("--skip", nargs="*", default=[],
|
||||
help="plugin names to skip (stem only, e.g. --skip cpu rtt)")
|
||||
args = p.parse_args()
|
||||
|
||||
plugins = load_plugins(args.plugins, set(args.skip))
|
||||
if not plugins:
|
||||
raise SystemExit(f"no plugins found in {args.plugins} (after --skip)")
|
||||
|
||||
all_keys: dict[str, str] = {}
|
||||
provenance: list[str] = ["% Plugins:"]
|
||||
for name, mod in plugins:
|
||||
keys, sources = mod.compute(args.derived)
|
||||
dup = set(keys) & set(all_keys)
|
||||
if dup:
|
||||
raise SystemExit(f"plugin {name} duplicates keys: {sorted(dup)}")
|
||||
all_keys.update(keys)
|
||||
provenance.append(f"% {name}: {len(keys)} keys")
|
||||
for src in sources:
|
||||
provenance.append(f"% {src} (mtime {file_mtime_iso(src)})")
|
||||
print(f" {name}: {len(keys)} keys from {len(sources)} sources")
|
||||
|
||||
args.out.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.out.write_text(format_pgfkeys(all_keys, provenance))
|
||||
print(f"wrote {args.out} ({len(all_keys)} keys total)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,10 @@
|
||||
[project]
|
||||
name = "eval-analysis"
|
||||
version = "0.1.0"
|
||||
description = "Aggregate raw per-run data to tidy unit-of-observation CSVs"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"pandas>=2.2",
|
||||
"numpy>=2.0",
|
||||
"scipy>=1.13",
|
||||
]
|
||||
@@ -0,0 +1,58 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Sanity check derived CSVs: shapes, no NaN, expected solution coverage."""
|
||||
import argparse
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
REQUIRED = {
|
||||
"runs.csv": {"setup", "solution", "run", "throughput_bps", "cpu_sender", "cpu_receiver"},
|
||||
"rtts.csv": {"setup", "solution", "rtt_us"},
|
||||
}
|
||||
|
||||
OPTIONAL = {
|
||||
"idts.csv": {"setup", "solution", "run", "stream_id", "idt_us"},
|
||||
"firstflow_bins.csv": {"setup", "solution", "t_ms", "packets"},
|
||||
}
|
||||
|
||||
|
||||
def check(derived: Path, expected_solutions: set[str]) -> list[str]:
|
||||
errors = []
|
||||
for name, cols in {**REQUIRED, **OPTIONAL}.items():
|
||||
path = derived / name
|
||||
if not path.exists():
|
||||
if name in REQUIRED:
|
||||
errors.append(f"{name}: missing")
|
||||
continue
|
||||
df = pd.read_csv(path)
|
||||
missing_cols = cols - set(df.columns)
|
||||
if missing_cols:
|
||||
errors.append(f"{name}: missing columns {missing_cols}")
|
||||
if df.isna().any().any():
|
||||
nan_cols = df.columns[df.isna().any()].tolist()
|
||||
errors.append(f"{name}: NaN in columns {nan_cols}")
|
||||
if len(df) == 0:
|
||||
errors.append(f"{name}: empty")
|
||||
sols = set(df["solution"].unique())
|
||||
if sols != expected_solutions:
|
||||
errors.append(f"{name}: solutions {sols} != expected {expected_solutions}")
|
||||
return errors
|
||||
|
||||
|
||||
def main() -> None:
|
||||
p = argparse.ArgumentParser()
|
||||
p.add_argument("--derived", required=True, type=Path)
|
||||
p.add_argument("--solutions", required=True, nargs="+")
|
||||
args = p.parse_args()
|
||||
|
||||
errors = check(args.derived, set(args.solutions))
|
||||
if errors:
|
||||
for e in errors:
|
||||
print(f" FAIL: {e}")
|
||||
sys.exit(1)
|
||||
print("ok")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Generated
+232
@@ -0,0 +1,232 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.12"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version < '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version < '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.4.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/50/8e/b8041bc719f056afd864478029d52214789341ac6583437b0ee5031e9530/numpy-2.4.5.tar.gz", hash = "sha256:ca670567a5683b7c1670ec03e0ddd5862e10934e92a70751d68d7b7b74ca7f9f", size = 20735669, upload-time = "2026-05-15T20:25:19.492Z" }
|
||||
wheels = [
|
||||
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/47/cc/ddaf3af9c46966fef5be879256f213d85a0c56c75d07a3b7defec7cf6b4c/numpy-2.4.5-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:4f5bc96d35d94e4ceab8b38a92241b4611e95dc44e63b9f1fa2a331858ee3507", size = 5204601, upload-time = "2026-05-15T20:22:56.257Z" },
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{ url = "https://files.pythonhosted.org/packages/07/ea/627fadd11959b3c7759008f34c92a35af8ff942dd8284a66ced648bbe516/numpy-2.4.5-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:4bb33e900ee81730ad77a258965134aa8ceac805124f7e5229347beda4b8d0aa", size = 6551360, upload-time = "2026-05-15T20:22:58.334Z" },
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||||
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{ url = "https://files.pythonhosted.org/packages/d1/0b/b905ae82d9419dc38123523862db64978ca2954b69609c3ae8fdaca1084c/numpy-2.4.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685681e956fc8dcb75adc6ff26694e1dfd738b24bd8d4696c51ca0110157f912", size = 16645703, upload-time = "2026-05-15T20:23:04.358Z" },
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{ url = "https://files.pythonhosted.org/packages/d3/a7/9041af38d527ab80a06a93570a77e29425b41507ad41f6acf5da78cfb4a4/numpy-2.4.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b42d9496f79e3a728192f05a42d86e36163217b7cdecb3813d0028a0aa6b72d7", size = 18368768, upload-time = "2026-05-15T20:23:09.44Z" },
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||||
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||||
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{ url = "https://files.pythonhosted.org/packages/28/f9/a90d2220ffcdc0798f5d55bb5d5463cd6254ec9ef43f384dae80217d7a2f/numpy-2.4.5-cp313-cp313-win_amd64.whl", hash = "sha256:fb4a6c9c537d6ccec9cc4aeae4261bd3cc79b070c67ddc0646f5b1c07fddde42", size = 12318553, upload-time = "2026-05-15T20:23:41.436Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/b8/c9/96f531fb3234545315152d34efdf3de7daee81254448447eb619e8d16967/numpy-2.4.5-cp313-cp313-win_arm64.whl", hash = "sha256:6d7df2da2e7ea0624a43aa368104b3a3ce14aae98ad4bb2c9a93fecef76f1c97", size = 10222200, upload-time = "2026-05-15T20:23:43.681Z" },
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{ url = "https://files.pythonhosted.org/packages/e1/f4/a291caab5a3c520babf93ff77c54fd5fdb1ebbc3296cee2eb2146ce773b1/numpy-2.4.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:2a235607a18df941760a695927051af4b1cd5d3ee85840d0e2af816785771feb", size = 14821438, upload-time = "2026-05-15T20:23:45.911Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/85/26/13dbb1159b864370568e7309063fd72667984df89db74e9caeb175d067c7/numpy-2.4.5-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:58dcf64969d870f36bc7fbd557d2617e997db7dc06261b6e3327148ea460d0a4", size = 5326663, upload-time = "2026-05-15T20:23:48.18Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/7c/99/d233408072a0e019e2288e27edd23f7d572ccd4a73d1539baa3270ede85d/numpy-2.4.5-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:235f54b0156274d8fa3155db3ed6d2f401c7e8f3367c90db0a12f02a58fde6ed", size = 6646874, upload-time = "2026-05-15T20:23:49.856Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/c5/00/eeb6f193dfe767725e952e0464f3e51f44145c5dd261cd7389aa36ac0713/numpy-2.4.5-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3b5bb65437a3555c648e706475db01c645559ca80dc8b03e4f202ea757e0d6", size = 15728147, upload-time = "2026-05-15T20:23:51.655Z" },
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{ url = "https://files.pythonhosted.org/packages/e5/c9/b8ed039f1fde1b13a8807c893e7e2f9432a379f4d6401edecf0028da5b2c/numpy-2.4.5-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7f09a7e5f017d7098c66522097c96257411c9620c0926212200d66bc8cee3976", size = 16681770, upload-time = "2026-05-15T20:23:53.933Z" },
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{ url = "https://files.pythonhosted.org/packages/11/5b/0198ef6cb7016eca6d895d392106012138127fab23f46637e76d5e25c9f5/numpy-2.4.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:993a88d8fdd8554466a8765cd8bacd97ba56b70ca6b0a04bcdca77f5afed4222", size = 17086218, upload-time = "2026-05-15T20:23:56.646Z" },
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{ url = "https://files.pythonhosted.org/packages/f0/fe/8821f3cfc660ae84c92ee158505941874b62c56a42e035a41425228cd8cf/numpy-2.4.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:84f58bed609b5669f5ad3d597901a4f1f86ee5b3c3708aaa55f05b4fe6e0f656", size = 18403542, upload-time = "2026-05-15T20:23:59.173Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/0e/00/e64ecaf498865e7b091f57658b2c522503e5d1b70e43b807f5f8247e1d88/numpy-2.4.5-cp313-cp313t-win32.whl", hash = "sha256:7200c58f3f933ca61e66346667dcc8510bb111995e9ce15398a731e6a4afa4bb", size = 6084903, upload-time = "2026-05-15T20:24:01.506Z" },
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[[package]]
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[[package]]
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name = "tzdata"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz", hash = "sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10", size = 198254, upload-time = "2026-04-24T15:22:08.651Z" }
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@@ -0,0 +1,34 @@
|
||||
"""First-flow packet-per-bin stats from firstflow_bins.csv.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def _stats(x: np.ndarray) -> dict[str, str]:
|
||||
return {
|
||||
"max-pkts": str(int(x.max())),
|
||||
"mean-pkts": f"{x.mean():.2f}",
|
||||
"median-pkts": str(int(np.median(x))),
|
||||
"p95-pkts": str(int(np.percentile(x, 95))),
|
||||
"p99-pkts": str(int(np.percentile(x, 99))),
|
||||
"sd-pkts": f"{x.std(ddof=1):.2f}",
|
||||
"n-bins": str(x.size),
|
||||
}
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
out: dict[str, str] = {}
|
||||
sources: list[Path] = []
|
||||
for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
|
||||
bins_path = exp_dir / "firstflow_bins.csv"
|
||||
if not bins_path.exists():
|
||||
continue
|
||||
sources.append(bins_path)
|
||||
df = pd.read_csv(bins_path)
|
||||
for sol, sub in df.groupby("solution", sort=True):
|
||||
x = sub["packets"].to_numpy()
|
||||
for k, v in _stats(x).items():
|
||||
out[f"{exp_dir.name}/bins/{sol}/{k}"] = v
|
||||
return out, sources
|
||||
@@ -0,0 +1,28 @@
|
||||
"""Sender + receiver CPU per (experiment, solution): mean and sample SD in %."""
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
METRICS = {
|
||||
"sender-cpu": "cpu_sender",
|
||||
"receiver-cpu": "cpu_receiver",
|
||||
}
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
out: dict[str, str] = {}
|
||||
sources: list[Path] = []
|
||||
for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
|
||||
runs = exp_dir / "runs.csv"
|
||||
if not runs.exists():
|
||||
continue
|
||||
sources.append(runs)
|
||||
df = pd.read_csv(runs)
|
||||
for sol, sub in df.groupby("solution", sort=True):
|
||||
for metric, col in METRICS.items():
|
||||
x = sub[col].to_numpy()
|
||||
base = f"{exp_dir.name}/{metric}/{sol}"
|
||||
out[f"{base}/mean-pct"] = f"{x.mean():.2f}"
|
||||
out[f"{base}/sd-pct"] = f"{x.std(ddof=1):.2f}"
|
||||
return out, sources
|
||||
@@ -0,0 +1,60 @@
|
||||
"""Per-flow IDT per (experiment, solution): percentiles, mean, SD in microseconds.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def _stats(x: np.ndarray) -> dict[str, str]:
|
||||
q05, q25, q50, q75, q95, q99 = np.percentile(x, [5, 25, 50, 75, 95, 99])
|
||||
return {
|
||||
"mean-us": f"{x.mean():.2f}",
|
||||
"sd-us": f"{x.std(ddof=1):.2f}",
|
||||
"median-us": f"{q50:.2f}",
|
||||
"p05-us": f"{q05:.2f}",
|
||||
"p25-us": f"{q25:.2f}",
|
||||
"p75-us": f"{q75:.2f}",
|
||||
"p95-us": f"{q95:.2f}",
|
||||
"p99-us": f"{q99:.2f}",
|
||||
"iqr-us": f"{q75 - q25:.2f}",
|
||||
"n-samples": str(x.size),
|
||||
}
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
out: dict[str, str] = {}
|
||||
sources: list[Path] = []
|
||||
for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
|
||||
idts_path = exp_dir / "idts.csv"
|
||||
if not idts_path.exists():
|
||||
continue
|
||||
sources.append(idts_path)
|
||||
df = pd.read_csv(idts_path, usecols=["solution", "idt_us"])
|
||||
per_sol: dict[str, dict[str, str]] = {}
|
||||
for sol, sub in df.groupby("solution", sort=True):
|
||||
x = sub["idt_us"].to_numpy()
|
||||
stats = _stats(x)
|
||||
per_sol[sol] = stats
|
||||
for k, v in stats.items():
|
||||
out[f"{exp_dir.name}/idt/{sol}/{k}"] = v
|
||||
|
||||
# IQR is meaningful only when the baseline solution actually spreads
|
||||
# the bulk; pre-pacing IDT is bimodal (back-to-back packets, heavy
|
||||
# tail above p95) so its IQR is often zero. Guard the ratio.
|
||||
if "tso-pacing" in per_sol:
|
||||
pac_sd = float(per_sol["tso-pacing"]["sd-us"])
|
||||
pac_iqr = float(per_sol["tso-pacing"]["iqr-us"])
|
||||
for other in ("no-tso", "tso", "cake"):
|
||||
if other not in per_sol:
|
||||
continue
|
||||
o_sd = float(per_sol[other]["sd-us"])
|
||||
o_iqr = float(per_sol[other]["iqr-us"])
|
||||
base = f"{exp_dir.name}/idt/tso-pacing-vs-{other}"
|
||||
if o_sd > 0:
|
||||
out[f"{base}/sd-ratio-pct"] = f"{100 * pac_sd / o_sd:.1f}"
|
||||
out[f"{base}/sd-reduction-pct"] = f"{100 * (1 - pac_sd / o_sd):.1f}"
|
||||
if o_iqr > 0:
|
||||
out[f"{base}/iqr-ratio-pct"] = f"{100 * pac_iqr / o_iqr:.1f}"
|
||||
out[f"{base}/iqr-reduction-pct"] = f"{100 * (1 - pac_iqr / o_iqr):.1f}"
|
||||
return out, sources
|
||||
@@ -0,0 +1,15 @@
|
||||
"""NFP-4000 memory constants (capacities + access latencies).
|
||||
|
||||
Source: Netronome Network Flow Processor 4xxx Family datasheet
|
||||
(netronomeNetronomeNetworkFlow2018).
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
return {
|
||||
"nfp4000/lmem/capacity-kb": "4",
|
||||
"nfp4000/lmem/latency-cycles": "1-3",
|
||||
"nfp4000/ctm/capacity-kb": "256",
|
||||
"nfp4000/ctm/latency-cycles": "50-100",
|
||||
}, []
|
||||
@@ -0,0 +1,60 @@
|
||||
"""RTT per (experiment, solution): mean, SD, quantiles, spread.
|
||||
|
||||
Also emits tso-pacing's variance-reduction ratios vs the other solutions.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def _stats(x: np.ndarray) -> dict[str, str]:
|
||||
q05, q25, q50, q75, q95 = np.percentile(x, [5, 25, 50, 75, 95])
|
||||
return {
|
||||
"mean-ms": f"{x.mean():.2f}",
|
||||
"sd-ms": f"{x.std(ddof=1):.2f}",
|
||||
"median-ms": f"{q50:.2f}",
|
||||
"iqr-ms": f"{q75 - q25:.2f}",
|
||||
"p05-ms": f"{q05:.2f}",
|
||||
"p95-ms": f"{q95:.2f}",
|
||||
"spread-5-95-ms": f"{q95 - q05:.2f}",
|
||||
"n-samples": str(x.size),
|
||||
}
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
out: dict[str, str] = {}
|
||||
sources: list[Path] = []
|
||||
for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
|
||||
rtts = exp_dir / "rtts.csv"
|
||||
if not rtts.exists():
|
||||
continue
|
||||
sources.append(rtts)
|
||||
df = pd.read_csv(rtts)
|
||||
per_sol: dict[str, dict[str, str]] = {}
|
||||
for sol, sub in df.groupby("solution", sort=True):
|
||||
x = sub["rtt_us"].to_numpy() / 1000.0
|
||||
stats = _stats(x)
|
||||
per_sol[sol] = stats
|
||||
for k, v in stats.items():
|
||||
out[f"{exp_dir.name}/rtt/{sol}/{k}"] = v
|
||||
|
||||
if "tso-pacing" in per_sol:
|
||||
pac_sd = float(per_sol["tso-pacing"]["sd-ms"])
|
||||
pac_spread = float(per_sol["tso-pacing"]["spread-5-95-ms"])
|
||||
pac_iqr = float(per_sol["tso-pacing"]["iqr-ms"])
|
||||
for other in ("no-tso", "tso", "cake"):
|
||||
if other not in per_sol:
|
||||
continue
|
||||
o = per_sol[other]
|
||||
o_sd = float(o["sd-ms"])
|
||||
o_spread = float(o["spread-5-95-ms"])
|
||||
o_iqr = float(o["iqr-ms"])
|
||||
base = f"{exp_dir.name}/rtt/tso-pacing-vs-{other}"
|
||||
out[f"{base}/sd-ratio-pct"] = f"{100 * pac_sd / o_sd:.1f}"
|
||||
out[f"{base}/spread-ratio-pct"] = f"{100 * pac_spread / o_spread:.1f}"
|
||||
out[f"{base}/iqr-ratio-pct"] = f"{100 * pac_iqr / o_iqr:.1f}"
|
||||
out[f"{base}/sd-reduction-pct"] = f"{100 * (1 - pac_sd / o_sd):.1f}"
|
||||
out[f"{base}/spread-reduction-pct"] = f"{100 * (1 - pac_spread / o_spread):.1f}"
|
||||
out[f"{base}/iqr-reduction-pct"] = f"{100 * (1 - pac_iqr / o_iqr):.1f}"
|
||||
return out, sources
|
||||
@@ -0,0 +1,109 @@
|
||||
"""Pairwise hypothesis tests across solutions.
|
||||
|
||||
Means: Welch's t (two-sided + one-sided) and Mann-Whitney U.
|
||||
Variance: Brown-Forsythe (Levene, median-centered) and Fligner-Killeen.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy import stats
|
||||
|
||||
PAIRS = [
|
||||
("no-tso", "tso"),
|
||||
("no-tso", "tso-pacing"),
|
||||
("no-tso", "cake"),
|
||||
("tso", "tso-pacing"),
|
||||
("tso", "cake"),
|
||||
("tso-pacing", "cake"),
|
||||
]
|
||||
|
||||
ALPHA = 0.05
|
||||
|
||||
|
||||
def _fmt(x: float) -> str:
|
||||
return f"{x:.4g}"
|
||||
|
||||
|
||||
def _mean_tests(out: dict, base: str, a: np.ndarray, b: np.ndarray) -> None:
|
||||
_, p_t = stats.ttest_ind(a, b, equal_var=False)
|
||||
_, p_tl = stats.ttest_ind(a, b, equal_var=False, alternative="less")
|
||||
_, p_tg = stats.ttest_ind(a, b, equal_var=False, alternative="greater")
|
||||
_, p_u = stats.mannwhitneyu(a, b, alternative="two-sided")
|
||||
out[f"{base}/welch-p"] = _fmt(p_t)
|
||||
out[f"{base}/welch-less-p"] = _fmt(p_tl)
|
||||
out[f"{base}/welch-greater-p"] = _fmt(p_tg)
|
||||
out[f"{base}/mwu-p"] = _fmt(p_u)
|
||||
|
||||
|
||||
def _variance_tests(out: dict, base: str, a: np.ndarray, b: np.ndarray) -> None:
|
||||
bf = stats.levene(a, b, center="median")
|
||||
fl = stats.fligner(a, b)
|
||||
out[f"{base}/bf-p"] = _fmt(bf.pvalue)
|
||||
out[f"{base}/fligner-p"] = _fmt(fl.pvalue)
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
out: dict[str, str] = {}
|
||||
sources: list[Path] = []
|
||||
for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
|
||||
runs_path = exp_dir / "runs.csv"
|
||||
rtts_path = exp_dir / "rtts.csv"
|
||||
|
||||
if runs_path.exists():
|
||||
sources.append(runs_path)
|
||||
runs = pd.read_csv(runs_path)
|
||||
for metric, col in (("sender-cpu", "cpu_sender"),
|
||||
("receiver-cpu", "cpu_receiver")):
|
||||
vals = {sol: sub[col].to_numpy() for sol, sub in runs.groupby("solution")}
|
||||
for a, b in PAIRS:
|
||||
if a not in vals or b not in vals:
|
||||
continue
|
||||
base = f"{exp_dir.name}/{metric}/test/{a}-vs-{b}"
|
||||
_mean_tests(out, base, vals[a], vals[b])
|
||||
|
||||
if rtts_path.exists():
|
||||
sources.append(rtts_path)
|
||||
rtts = pd.read_csv(rtts_path)
|
||||
vals = {sol: sub["rtt_us"].to_numpy() / 1000.0
|
||||
for sol, sub in rtts.groupby("solution")}
|
||||
for a, b in PAIRS:
|
||||
if a not in vals or b not in vals:
|
||||
continue
|
||||
base = f"{exp_dir.name}/rtt/test/{a}-vs-{b}"
|
||||
_mean_tests(out, base, vals[a], vals[b])
|
||||
_variance_tests(out, base, vals[a], vals[b])
|
||||
|
||||
idts_path = exp_dir / "idts.csv"
|
||||
if idts_path.exists():
|
||||
sources.append(idts_path)
|
||||
idts = pd.read_csv(idts_path, usecols=["solution", "idt_us"])
|
||||
vals = {sol: sub["idt_us"].to_numpy()
|
||||
for sol, sub in idts.groupby("solution")}
|
||||
for a, b in PAIRS:
|
||||
if a not in vals or b not in vals:
|
||||
continue
|
||||
base = f"{exp_dir.name}/idt/test/{a}-vs-{b}"
|
||||
_mean_tests(out, base, vals[a], vals[b])
|
||||
_variance_tests(out, base, vals[a], vals[b])
|
||||
|
||||
# Bonferroni-corrected thresholds. Count one entry per comparison
|
||||
# (welch-p for mean families, bf-p for variance) so the threshold reflects
|
||||
# the number of pairwise comparisons, not the number of test statistics.
|
||||
def n_with(in_pattern: str, suffix: str) -> int:
|
||||
return sum(1 for k in out if in_pattern in k and k.endswith(suffix))
|
||||
|
||||
families = {
|
||||
"cpu": n_with("-cpu/test/", "/mwu-p"),
|
||||
"rtt-mean": n_with("/rtt/test/", "/mwu-p"),
|
||||
"rtt-variance": n_with("/rtt/test/", "/bf-p"),
|
||||
"idt-mean": n_with("/idt/test/", "/mwu-p"),
|
||||
"idt-variance": n_with("/idt/test/", "/bf-p"),
|
||||
}
|
||||
for name, n in families.items():
|
||||
if n > 0:
|
||||
out[f"bonferroni/{name}-n"] = str(n)
|
||||
out[f"bonferroni/{name}-alpha"] = _fmt(ALPHA / n)
|
||||
out["bonferroni/alpha-uncorrected"] = _fmt(ALPHA)
|
||||
|
||||
return out, sources
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Throughput per (experiment, solution): mean and sample SD in Gbps."""
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
|
||||
out: dict[str, str] = {}
|
||||
sources: list[Path] = []
|
||||
for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
|
||||
runs = exp_dir / "runs.csv"
|
||||
if not runs.exists():
|
||||
continue
|
||||
sources.append(runs)
|
||||
df = pd.read_csv(runs)
|
||||
for sol, sub in df.groupby("solution", sort=True):
|
||||
thr = sub["throughput_bps"].to_numpy() / 1e9
|
||||
base = f"{exp_dir.name}/throughput/{sol}"
|
||||
out[f"{base}/mean-gbps"] = f"{thr.mean():.3f}"
|
||||
out[f"{base}/sd-gbps"] = f"{thr.std(ddof=1):.3f}"
|
||||
out[f"{base}/n-runs"] = str(len(thr))
|
||||
return out, sources
|
||||
+21
@@ -0,0 +1,21 @@
|
||||
# docker compose up -> runs `make all`, writes eval/out/.
|
||||
# RAW_DATA_ROOT=/path docker compose up -> point at a different raw_data tree.
|
||||
#
|
||||
# RAW_DATA_ROOT outside the container = host path to mount (read by compose).
|
||||
# Inside the container it always resolves to /raw_data via the `environment:`
|
||||
# clause below; the host value is only consumed by the volume substitution.
|
||||
|
||||
services:
|
||||
eval:
|
||||
build: .
|
||||
image: eval
|
||||
user: "${UID:-1000}:${GID:-1000}"
|
||||
volumes:
|
||||
- .:/work/eval
|
||||
- ${RAW_DATA_ROOT:-../raw_data}:/raw_data:ro
|
||||
environment:
|
||||
RAW_DATA_ROOT: /raw_data
|
||||
HOME: /tmp
|
||||
UV_CACHE_DIR: /tmp/uv-cache
|
||||
working_dir: /work/eval
|
||||
command: make all
|
||||
@@ -0,0 +1 @@
|
||||
source("renv/activate.R")
|
||||
@@ -0,0 +1,11 @@
|
||||
out/
|
||||
renv/library/
|
||||
renv/local/
|
||||
renv/cellar/
|
||||
renv/lock/
|
||||
renv/sandbox/
|
||||
renv/staging/
|
||||
.Rproj.user/
|
||||
.Rhistory
|
||||
.RData
|
||||
.Ruserdata
|
||||
@@ -0,0 +1,170 @@
|
||||
-include ../local.mk
|
||||
|
||||
EXP ?= datacenter-fq
|
||||
SETUP ?= datacenter_fq
|
||||
SOLUTIONS ?= no-tso tso tso-pacing
|
||||
FIG_STEMS ?= throughput throughput_zoom rtt rtt_zoom sender_cpu receiver_cpu timeseries idt_cdf idt_cdf_zoom
|
||||
|
||||
# Global figure size and font size (inches, pt). Inherited by every Rscript
|
||||
# via the export below; common.R reads these as Sys.getenv("FIG_*").
|
||||
# Override on the command line: make FIG_WIDTH=2.5 FIG_HEIGHT=1.8 FIG_FONTSIZE=8 datacenter-fq
|
||||
FIG_WIDTH ?= 2.5
|
||||
FIG_HEIGHT ?= 2.0
|
||||
FIG_FONTSIZE ?= 9
|
||||
export FIG_WIDTH FIG_HEIGHT FIG_FONTSIZE
|
||||
|
||||
THROUGHPUT_SIZE_ARGS ?=
|
||||
RTT_SIZE_ARGS ?=
|
||||
THROUGHPUT_ARGS ?=
|
||||
RTT_ARGS ?=
|
||||
THROUGHPUT_ZOOM_ARGS ?= --ymin 3.5 --ymax 4.0 --ystep 0.1
|
||||
RTT_ZOOM_ARGS ?= --ymin 4 --ymax 14 --ystep 2
|
||||
SENDER_CPU_ARGS ?=
|
||||
RECEIVER_CPU_ARGS ?=
|
||||
TIMESERIES_ARGS ?=
|
||||
IDT_CDF_ARGS ?=
|
||||
|
||||
ROOT := ..
|
||||
RAW_DATA_ROOT ?= $(ROOT)/../raw_data
|
||||
RAW := $(RAW_DATA_ROOT)/$(EXP)/aggregates
|
||||
DERIVED := $(ROOT)/derived/$(EXP)
|
||||
DERIVED_ROOT := $(ROOT)/derived
|
||||
OUT := ../out/$(EXP)
|
||||
VALUES_TEX := ../out/values.tex
|
||||
VALUES_PLUGINS := $(wildcard $(ROOT)/analysis/values/*.py)
|
||||
SKIP_PLUGINS ?=
|
||||
|
||||
CSVS := $(addprefix $(DERIVED)/, runs.csv rtts.csv idts.csv firstflow_bins.csv)
|
||||
PDFS := $(addprefix $(OUT)/, $(addsuffix .pdf, $(FIG_STEMS)))
|
||||
TEXS := $(addprefix $(OUT)/, $(addsuffix .tex, $(FIG_STEMS)))
|
||||
|
||||
.PHONY: all derive figures tikz sanity values pdfs tex clean datacenter-fq datacenter-fq-tikz direct-link direct-link-tikz all-experiments all-experiments-tikz
|
||||
.DELETE_ON_ERROR:
|
||||
|
||||
all: all-experiments
|
||||
|
||||
derive: $(DERIVED)/.stamp
|
||||
|
||||
$(DERIVED)/.stamp: $(ROOT)/analysis/aggregate.py
|
||||
cd $(ROOT)/analysis && uv run python aggregate.py --raw $(abspath $(RAW)) --setup-prefix $(SETUP) --solutions $(SOLUTIONS) --out $(abspath $(DERIVED))
|
||||
touch $@
|
||||
|
||||
$(CSVS): $(DERIVED)/.stamp
|
||||
|
||||
sanity: $(DERIVED)/.stamp
|
||||
cd $(ROOT)/analysis && uv run python sanity_check.py --derived $(abspath $(DERIVED)) --solutions $(SOLUTIONS)
|
||||
|
||||
figures: all-experiments
|
||||
tikz: all-experiments-tikz
|
||||
|
||||
pdfs: $(PDFS)
|
||||
tex: $(TEXS)
|
||||
|
||||
FMT_FLAG = $(if $(filter %.tex,$@),--tikz,--pdf)
|
||||
|
||||
$(OUT)/throughput.pdf $(OUT)/throughput.tex: fig_throughput.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ $(THROUGHPUT_SIZE_ARGS) $(THROUGHPUT_ARGS)
|
||||
|
||||
$(OUT)/throughput_zoom.pdf $(OUT)/throughput_zoom.tex: fig_throughput.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ --zoom $(THROUGHPUT_SIZE_ARGS) $(THROUGHPUT_ZOOM_ARGS)
|
||||
|
||||
$(OUT)/rtt.pdf $(OUT)/rtt.tex: fig_rtt.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ $(RTT_SIZE_ARGS) $(RTT_ARGS)
|
||||
|
||||
$(OUT)/rtt_zoom.pdf $(OUT)/rtt_zoom.tex: fig_rtt.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ --zoom $(RTT_SIZE_ARGS) $(RTT_ZOOM_ARGS)
|
||||
|
||||
$(OUT)/sender_cpu.pdf $(OUT)/sender_cpu.tex: fig_sender_cpu.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ $(SENDER_CPU_ARGS)
|
||||
|
||||
$(OUT)/receiver_cpu.pdf $(OUT)/receiver_cpu.tex: fig_receiver_cpu.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ $(RECEIVER_CPU_ARGS)
|
||||
|
||||
$(OUT)/timeseries.pdf $(OUT)/timeseries.tex: fig_timeseries.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ $(TIMESERIES_ARGS)
|
||||
|
||||
$(OUT)/idt_cdf.pdf $(OUT)/idt_cdf.tex: fig_idt_cdf.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ $(IDT_CDF_ARGS)
|
||||
|
||||
$(OUT)/idt_cdf_zoom.pdf $(OUT)/idt_cdf_zoom.tex: fig_idt_cdf.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) $(FMT_FLAG) $@ --zoom $(IDT_CDF_ARGS)
|
||||
|
||||
$(OUT)/%.pdf: fig_%.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) --pdf $@
|
||||
|
||||
$(OUT)/%_zoom.pdf: fig_%.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) --pdf $@ --zoom
|
||||
|
||||
$(OUT)/%.tex: fig_%.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) --tikz $@
|
||||
|
||||
$(OUT)/%_zoom.tex: fig_%.R common.R $(CSVS) Makefile
|
||||
@mkdir -p $(OUT)
|
||||
Rscript $< --data $(DERIVED) --tikz $@ --zoom
|
||||
|
||||
values: $(VALUES_TEX)
|
||||
|
||||
# SKIP_PLUGINS is a runtime decision, not a file dependency: force rebuild
|
||||
# whenever the user sets it on the command line.
|
||||
ifneq ($(SKIP_PLUGINS),)
|
||||
.PHONY: $(VALUES_TEX)
|
||||
endif
|
||||
|
||||
$(VALUES_TEX): $(ROOT)/analysis/gen_values.py $(VALUES_PLUGINS) $(DERIVED_ROOT)/datacenter-fq/.stamp $(DERIVED_ROOT)/direct-link/.stamp Makefile
|
||||
@mkdir -p $(dir $(VALUES_TEX))
|
||||
cd $(ROOT)/analysis && uv run python gen_values.py --derived $(abspath $(DERIVED_ROOT)) --out $(abspath $(VALUES_TEX)) $(if $(SKIP_PLUGINS),--skip $(SKIP_PLUGINS))
|
||||
|
||||
clean:
|
||||
rm -rf $(OUT) $(DERIVED)
|
||||
|
||||
# Per-experiment shortcut targets. Each recursively invokes the makefile
|
||||
# with the right EXP / SETUP / FIG_STEMS / zoom overrides.
|
||||
|
||||
# Per-experiment knobs. Each shortcut target reuses these via $(call ...).
|
||||
QUARTER_SIZE := --width 1.4 --height 1.5
|
||||
|
||||
DATACENTER_FQ_ARGS = EXP=datacenter-fq SETUP=datacenter_fq \
|
||||
SOLUTIONS="no-tso tso tso-pacing cake:datacenter_cake_no-tso" \
|
||||
FIG_STEMS="throughput throughput_zoom rtt rtt_zoom sender_cpu receiver_cpu timeseries idt_cdf idt_cdf_zoom" \
|
||||
THROUGHPUT_SIZE_ARGS="$(QUARTER_SIZE)" \
|
||||
RTT_SIZE_ARGS="$(QUARTER_SIZE)" \
|
||||
THROUGHPUT_ZOOM_ARGS="--ymin 3.5 --ymax 4.0 --ystep 0.1" \
|
||||
RTT_ZOOM_ARGS="--ymin 4 --ymax 14 --ystep 2"
|
||||
|
||||
DIRECT_LINK_ARGS = EXP=direct-link SETUP=direct-link_fq \
|
||||
SOLUTIONS="no-tso tso tso-pacing cake:direct-link_cake_no-tso" \
|
||||
FIG_STEMS="throughput throughput_zoom rtt rtt_zoom sender_cpu receiver_cpu" \
|
||||
THROUGHPUT_SIZE_ARGS="$(QUARTER_SIZE)" \
|
||||
RTT_SIZE_ARGS="$(QUARTER_SIZE)" \
|
||||
THROUGHPUT_ARGS="--ymax 10 --ystep 2" \
|
||||
THROUGHPUT_ZOOM_ARGS="--ymin 9.0 --ymax 10.0 --ystep 0.2" \
|
||||
RTT_ZOOM_ARGS="--ymin 1.75 --ymax 4 --ystep 1" \
|
||||
SENDER_CPU_ARGS="--ystep 1" \
|
||||
RECEIVER_CPU_ARGS="--ystep 5"
|
||||
|
||||
datacenter-fq:
|
||||
$(MAKE) pdfs $(DATACENTER_FQ_ARGS)
|
||||
|
||||
datacenter-fq-tikz:
|
||||
$(MAKE) tex $(DATACENTER_FQ_ARGS)
|
||||
|
||||
direct-link:
|
||||
$(MAKE) pdfs $(DIRECT_LINK_ARGS)
|
||||
|
||||
direct-link-tikz:
|
||||
$(MAKE) tex $(DIRECT_LINK_ARGS)
|
||||
|
||||
all-experiments: datacenter-fq direct-link
|
||||
all-experiments-tikz: datacenter-fq-tikz direct-link-tikz
|
||||
@@ -0,0 +1,128 @@
|
||||
suppressPackageStartupMessages({
|
||||
library(argparse)
|
||||
library(ggplot2)
|
||||
library(dplyr)
|
||||
library(tidyr)
|
||||
library(readr)
|
||||
library(scales)
|
||||
library(tikzDevice)
|
||||
})
|
||||
|
||||
SOLUTION_ORDER <- c("tso", "tso-pacing", "cake")
|
||||
|
||||
SOLUTION_LABELS <- c(
|
||||
"no-tso" = "TSO Off",
|
||||
"tso" = "TSO On",
|
||||
"tso-pacing" = "TSO Pacing",
|
||||
"cake" = "Cake"
|
||||
)
|
||||
|
||||
SOLUTION_COLORS <- c(
|
||||
"no-tso" = "#009E73",
|
||||
"tso" = "#E69F00",
|
||||
"tso-pacing" = "#0072B2",
|
||||
"cake" = "#CC79A7"
|
||||
)
|
||||
|
||||
SOLUTION_LINETYPES <- c(
|
||||
"no-tso" = "longdash",
|
||||
"tso" = "dashed",
|
||||
"tso-pacing" = "solid",
|
||||
"cake" = "twodash"
|
||||
)
|
||||
|
||||
# Global figure size and font size, overridable via env vars
|
||||
# (Makefile exports FIG_WIDTH / FIG_HEIGHT / FIG_FONTSIZE).
|
||||
.env_num <- function(var, default) {
|
||||
v <- Sys.getenv(var)
|
||||
if (nchar(v) == 0) default else as.numeric(v)
|
||||
}
|
||||
DEFAULT_WIDTH <- .env_num("FIG_WIDTH", 3.33)
|
||||
DEFAULT_HEIGHT <- .env_num("FIG_HEIGHT", 2.4)
|
||||
DEFAULT_FONTSIZE <- .env_num("FIG_FONTSIZE", 10)
|
||||
|
||||
theme_paper <- function(base_size = DEFAULT_FONTSIZE) {
|
||||
theme_bw(base_size = base_size) +
|
||||
theme(
|
||||
legend.position = "bottom",
|
||||
legend.title = element_blank(),
|
||||
legend.margin = margin(t = -4, l = -20),
|
||||
legend.text = element_text(size = base_size - 1),
|
||||
legend.key.size = unit(base_size, "pt"),
|
||||
axis.text = element_text(size = base_size - 1),
|
||||
strip.background = element_rect(fill = "grey90"),
|
||||
panel.grid.minor = element_blank(),
|
||||
plot.margin = margin(2, 4, 2, 2)
|
||||
)
|
||||
}
|
||||
|
||||
prepare_solution <- function(df) {
|
||||
df %>%
|
||||
filter(solution %in% SOLUTION_ORDER) %>%
|
||||
mutate(solution = factor(solution, levels = SOLUTION_ORDER,
|
||||
labels = SOLUTION_LABELS[SOLUTION_ORDER]))
|
||||
}
|
||||
|
||||
LABEL_COLORS <- setNames(SOLUTION_COLORS[SOLUTION_ORDER], SOLUTION_LABELS[SOLUTION_ORDER])
|
||||
LABEL_LINETYPES <- setNames(SOLUTION_LINETYPES[SOLUTION_ORDER], SOLUTION_LABELS[SOLUTION_ORDER])
|
||||
|
||||
COL_WIDTH <- 3.33
|
||||
TEXT_WIDTH <- 7.0
|
||||
|
||||
# ACM page-fraction presets, in inches.
|
||||
HALF_WIDTH <- 3.3 # two figures side-by-side under one \begin{figure}
|
||||
THIRD_WIDTH <- 2.2 # three figures side-by-side
|
||||
|
||||
label_pct <- function() percent_format(accuracy = 1)
|
||||
label_us <- function() "µs"
|
||||
pct_sign <- function() "%"
|
||||
|
||||
fig_parser <- function(default_width = DEFAULT_WIDTH, default_height = DEFAULT_HEIGHT,
|
||||
description = "Generate figure") {
|
||||
p <- ArgumentParser(description = description)
|
||||
p$add_argument("--data", required = TRUE, help = "derived CSV directory")
|
||||
p$add_argument("--pdf", help = "write PDF to this path")
|
||||
p$add_argument("--tikz", help = "write tikz .tex to this path")
|
||||
p$add_argument("--width", type = "double", default = default_width)
|
||||
p$add_argument("--height", type = "double", default = default_height)
|
||||
p$add_argument("--zoom", action = "store_true", help = "use zoomed axis limits")
|
||||
p$add_argument("--debug-margins", action = "store_true",
|
||||
help = "color plot.background / panel.background / panel.border to visualise layout")
|
||||
p
|
||||
}
|
||||
|
||||
# Optional theme overlay that colors the plot canvas, plot panel, and panel
|
||||
# border so layout / whitespace can be inspected. Returns NULL when off
|
||||
# (adding NULL to a ggplot is a no-op).
|
||||
theme_debug_margins <- function(args) {
|
||||
if (!isTRUE(args$debug_margins)) return(NULL)
|
||||
theme(
|
||||
plot.background = element_rect(fill = "#FFF7B3", colour = "black"),
|
||||
panel.background = element_rect(fill = "#CCE5FF", colour = NA),
|
||||
panel.border = element_rect(colour = "red", fill = NA, linewidth = 0.6)
|
||||
)
|
||||
}
|
||||
|
||||
save_figure <- function(plot, args) {
|
||||
if (is.null(args$pdf) && is.null(args$tikz)) {
|
||||
stop("save_figure: pass --pdf and/or --tikz")
|
||||
}
|
||||
if (!is.null(args$pdf)) {
|
||||
dir.create(dirname(args$pdf), showWarnings = FALSE, recursive = TRUE)
|
||||
ggsave(args$pdf, plot, width = args$width, height = args$height, device = "pdf")
|
||||
message("wrote ", args$pdf)
|
||||
}
|
||||
if (!is.null(args$tikz)) {
|
||||
dir.create(dirname(args$tikz), showWarnings = FALSE, recursive = TRUE)
|
||||
old <- options(
|
||||
tikzSanitizeCharacters = c(getOption("tikzSanitizeCharacters"), "µ"),
|
||||
tikzReplacementCharacters = c(getOption("tikzReplacementCharacters"), "$\\mu$")
|
||||
)
|
||||
on.exit(options(old), add = TRUE)
|
||||
tikz(args$tikz, width = args$width, height = args$height,
|
||||
standAlone = FALSE, sanitize = TRUE)
|
||||
print(plot)
|
||||
dev.off()
|
||||
message("wrote ", args$tikz)
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,43 @@
|
||||
#!/usr/bin/env Rscript
|
||||
source("common.R")
|
||||
|
||||
# Random sample size per solution. stat_ecdf emits one tikz path segment per
|
||||
# unique x value; the raw idts.csv has ~120 K unique values per solution,
|
||||
# producing a ~500 K-line tikz file that exceeds pdflatex main_memory.
|
||||
# 5000 samples per solution preserve the CDF shape (including the 92.5-100 %
|
||||
# zoom tail: ~375 points) and keep the tikz file manageable.
|
||||
SAMPLE_PER_SOLUTION <- 20000
|
||||
|
||||
parser <- fig_parser(description = "Per-flow inter-departure-time CDF")
|
||||
parser$add_argument("--sample", type = "integer", default = SAMPLE_PER_SOLUTION,
|
||||
help = "downsample to N points per solution (0 = no downsampling)")
|
||||
parser$add_argument("--seed", type = "integer", default = 1,
|
||||
help = "RNG seed for downsampling")
|
||||
args <- parser$parse_args()
|
||||
|
||||
set.seed(args$seed)
|
||||
idts <- read_csv(file.path(args$data, "idts.csv"), show_col_types = FALSE) %>%
|
||||
prepare_solution()
|
||||
if (args$sample > 0) {
|
||||
idts <- idts %>%
|
||||
group_by(solution) %>%
|
||||
slice_sample(n = args$sample) %>%
|
||||
ungroup()
|
||||
}
|
||||
|
||||
p <- ggplot(idts, aes(x = idt_us, colour = solution, linetype = solution)) +
|
||||
stat_ecdf(linewidth = 0.9, pad = FALSE, key_glyph = "rect") +
|
||||
scale_x_log10(labels = label_comma()) +
|
||||
scale_y_continuous(labels = label_pct()) +
|
||||
scale_colour_manual(values = LABEL_COLORS) +
|
||||
scale_linetype_manual(values = LABEL_LINETYPES) +
|
||||
labs(x = paste0("Inter-departure time within flow (", label_us(), ")"), y = "CDF") +
|
||||
theme_paper() +
|
||||
theme(legend.text = element_text(size = rel(0.8)),
|
||||
legend.key.size = unit(6, "pt"))
|
||||
|
||||
if (args$zoom) {
|
||||
p <- p + coord_cartesian(xlim = c(10, 1000), ylim = c(0.925, 1.0))
|
||||
}
|
||||
|
||||
save_figure(p, args)
|
||||
@@ -0,0 +1,27 @@
|
||||
#!/usr/bin/env Rscript
|
||||
source("common.R")
|
||||
|
||||
parser <- fig_parser(description = "Receiver CPU bar chart")
|
||||
parser$add_argument("--ystep", type = "double",
|
||||
help = "y-axis major tick spacing (omit = auto)")
|
||||
args <- parser$parse_args()
|
||||
|
||||
runs <- read_csv(file.path(args$data, "runs.csv"), show_col_types = FALSE) %>%
|
||||
prepare_solution() %>%
|
||||
group_by(solution) %>%
|
||||
summarise(mean_pct = mean(cpu_receiver),
|
||||
sd_pct = sd(cpu_receiver), .groups = "drop")
|
||||
|
||||
p <- ggplot(runs, aes(x = solution, y = mean_pct, fill = solution)) +
|
||||
geom_col(width = 0.6, colour = "black") +
|
||||
geom_errorbar(aes(ymin = mean_pct - sd_pct, ymax = mean_pct + sd_pct),
|
||||
width = 0.2) +
|
||||
scale_fill_manual(values = LABEL_COLORS, guide = "none") +
|
||||
labs(x = NULL, y = paste0("Average CPU usage (", pct_sign(), ")")) +
|
||||
theme_paper()
|
||||
|
||||
if (!is.null(args$ystep)) {
|
||||
p <- p + scale_y_continuous(breaks = scales::breaks_width(args$ystep))
|
||||
}
|
||||
|
||||
save_figure(p, args)
|
||||
@@ -0,0 +1,41 @@
|
||||
#!/usr/bin/env Rscript
|
||||
source("common.R")
|
||||
|
||||
parser <- fig_parser(description = "RTT box plot")
|
||||
parser$add_argument("--ymin", type = "double", default = 4)
|
||||
parser$add_argument("--ymax", type = "double", default = 14)
|
||||
parser$add_argument("--ystep", type = "double", default = 2)
|
||||
args <- parser$parse_args()
|
||||
|
||||
stats <- read_csv(file.path(args$data, "rtts.csv"), show_col_types = FALSE) %>%
|
||||
mutate(rtt_ms = rtt_us / 1000) %>%
|
||||
prepare_solution() %>%
|
||||
group_by(solution) %>%
|
||||
summarise(
|
||||
ymin = quantile(rtt_ms, 0.05),
|
||||
lower = quantile(rtt_ms, 0.25),
|
||||
middle = median(rtt_ms),
|
||||
upper = quantile(rtt_ms, 0.75),
|
||||
ymax = quantile(rtt_ms, 0.95),
|
||||
.groups = "drop"
|
||||
)
|
||||
|
||||
p <- ggplot(stats, aes(x = solution, fill = solution)) +
|
||||
geom_boxplot(aes(ymin = ymin, lower = lower, middle = middle,
|
||||
upper = upper, ymax = ymax),
|
||||
stat = "identity", width = 0.6, colour = "black") +
|
||||
scale_fill_manual(values = LABEL_COLORS, guide = "none") +
|
||||
labs(x = NULL, y = "RTT (ms)") +
|
||||
theme_paper() +
|
||||
theme(axis.text.x = element_text(angle = 45, hjust = 1),
|
||||
plot.margin = margin(2, 4, 0, 2)) +
|
||||
theme_debug_margins(args)
|
||||
|
||||
if (args$zoom) {
|
||||
p <- p + coord_cartesian(ylim = c(args$ymin, args$ymax)) +
|
||||
scale_y_continuous(breaks = seq(args$ymin, args$ymax, args$ystep))
|
||||
} else {
|
||||
p <- p + coord_cartesian(ylim = c(0, NA))
|
||||
}
|
||||
|
||||
save_figure(p, args)
|
||||
@@ -0,0 +1,27 @@
|
||||
#!/usr/bin/env Rscript
|
||||
source("common.R")
|
||||
|
||||
parser <- fig_parser(description = "Sender CPU bar chart")
|
||||
parser$add_argument("--ystep", type = "double",
|
||||
help = "y-axis major tick spacing (omit = auto)")
|
||||
args <- parser$parse_args()
|
||||
|
||||
runs <- read_csv(file.path(args$data, "runs.csv"), show_col_types = FALSE) %>%
|
||||
prepare_solution() %>%
|
||||
group_by(solution) %>%
|
||||
summarise(mean_pct = mean(cpu_sender),
|
||||
sd_pct = sd(cpu_sender), .groups = "drop")
|
||||
|
||||
p <- ggplot(runs, aes(x = solution, y = mean_pct, fill = solution)) +
|
||||
geom_col(width = 0.6, colour = "black") +
|
||||
geom_errorbar(aes(ymin = mean_pct - sd_pct, ymax = mean_pct + sd_pct),
|
||||
width = 0.2) +
|
||||
scale_fill_manual(values = LABEL_COLORS, guide = "none") +
|
||||
labs(x = NULL, y = paste0("Average CPU usage (", pct_sign(), ")")) +
|
||||
theme_paper()
|
||||
|
||||
if (!is.null(args$ystep)) {
|
||||
p <- p + scale_y_continuous(breaks = scales::breaks_width(args$ystep))
|
||||
}
|
||||
|
||||
save_figure(p, args)
|
||||
@@ -0,0 +1,35 @@
|
||||
#!/usr/bin/env Rscript
|
||||
source("common.R")
|
||||
|
||||
parser <- fig_parser(description = "Throughput bar chart")
|
||||
parser$add_argument("--ymin", type = "double")
|
||||
parser$add_argument("--ymax", type = "double")
|
||||
parser$add_argument("--ystep", type = "double")
|
||||
args <- parser$parse_args()
|
||||
|
||||
runs <- read_csv(file.path(args$data, "runs.csv"), show_col_types = FALSE) %>%
|
||||
prepare_solution() %>%
|
||||
group_by(solution) %>%
|
||||
summarise(mean_gbps = mean(throughput_bps) / 1e9,
|
||||
sd_gbps = sd(throughput_bps) / 1e9, .groups = "drop")
|
||||
|
||||
p <- ggplot(runs, aes(x = solution, y = mean_gbps, fill = solution)) +
|
||||
geom_col(width = 0.6, colour = "black") +
|
||||
geom_errorbar(aes(ymin = mean_gbps - sd_gbps, ymax = mean_gbps + sd_gbps),
|
||||
width = 0.2) +
|
||||
scale_fill_manual(values = LABEL_COLORS, guide = "none") +
|
||||
labs(x = NULL, y = "Throughput (Gbps)") +
|
||||
theme_paper() +
|
||||
theme(axis.text.x = element_text(angle = 45, hjust = 1),
|
||||
plot.margin = margin(2, 4, 0, 2)) +
|
||||
theme_debug_margins(args)
|
||||
|
||||
if (!is.null(args$ymax)) {
|
||||
ymin <- if (is.null(args$ymin)) 0 else args$ymin
|
||||
p <- p + coord_cartesian(ylim = c(ymin, args$ymax))
|
||||
if (!is.null(args$ystep)) {
|
||||
p <- p + scale_y_continuous(breaks = seq(ymin, args$ymax, args$ystep))
|
||||
}
|
||||
}
|
||||
|
||||
save_figure(p, args)
|
||||
@@ -0,0 +1,37 @@
|
||||
#!/usr/bin/env Rscript
|
||||
source("common.R")
|
||||
|
||||
parser <- fig_parser(description = "First-flow packet timeseries")
|
||||
parser$add_argument("--xmin", type = "double", default = 100.0)
|
||||
parser$add_argument("--xmax", type = "double", default = 105.0)
|
||||
args <- parser$parse_args()
|
||||
|
||||
bins <- read_csv(file.path(args$data, "firstflow_bins.csv"), show_col_types = FALSE) %>%
|
||||
group_by(solution) %>%
|
||||
mutate(t_ms = t_ms - min(t_ms)) %>%
|
||||
ungroup() %>%
|
||||
filter(t_ms >= args$xmin, t_ms <= args$xmax) %>%
|
||||
prepare_solution()
|
||||
|
||||
pacing_label <- SOLUTION_LABELS[["tso-pacing"]]
|
||||
back <- bins %>% filter(solution == pacing_label)
|
||||
front <- bins %>% filter(solution != pacing_label)
|
||||
|
||||
p <- ggplot() +
|
||||
geom_area(data = back, aes(x = t_ms, y = packets, fill = solution),
|
||||
alpha = 0.22, show.legend = FALSE) +
|
||||
geom_line(data = back, aes(x = t_ms, y = packets, colour = solution,
|
||||
linetype = solution), linewidth = 0.9, key_glyph = "rect") +
|
||||
geom_line(data = front, aes(x = t_ms, y = packets, colour = solution,
|
||||
linetype = solution), linewidth = 0.9, key_glyph = "rect") +
|
||||
scale_colour_manual(values = LABEL_COLORS, breaks = names(LABEL_COLORS)) +
|
||||
scale_fill_manual(values = LABEL_COLORS, breaks = names(LABEL_COLORS)) +
|
||||
scale_linetype_manual(values = LABEL_LINETYPES, breaks = names(LABEL_LINETYPES)) +
|
||||
scale_x_continuous(breaks = seq(args$xmin, args$xmax, 1)) +
|
||||
scale_y_continuous(breaks = seq(0, 45, 5), limits = c(0, 45)) +
|
||||
labs(x = "Time elapsed (ms)", y = paste0("Packets per 50 ", label_us(), " bin")) +
|
||||
theme_paper() +
|
||||
theme(legend.text = element_text(size = rel(0.8)),
|
||||
legend.key.size = unit(6, "pt"))
|
||||
|
||||
save_figure(p, args)
|
||||
+2296
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,7 @@
|
||||
library/
|
||||
local/
|
||||
cellar/
|
||||
lock/
|
||||
python/
|
||||
sandbox/
|
||||
staging/
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"bioconductor.version": null,
|
||||
"external.libraries": [],
|
||||
"ignored.packages": [],
|
||||
"lockfile.sanitize": true,
|
||||
"package.dependency.fields": [
|
||||
"Imports",
|
||||
"Depends",
|
||||
"LinkingTo"
|
||||
],
|
||||
"ppm.enabled": null,
|
||||
"ppm.ignored.urls": [],
|
||||
"r.version": null,
|
||||
"snapshot.dev": false,
|
||||
"snapshot.type": "implicit",
|
||||
"use.cache": true,
|
||||
"vcs.ignore.cellar": true,
|
||||
"vcs.ignore.library": true,
|
||||
"vcs.ignore.local": true,
|
||||
"vcs.manage.ignores": true
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
# Copy to local.mk and edit. Use ?= so a shell env var still overrides.
|
||||
# Read from both eval/Makefile and eval/figures/Makefile; absolute paths
|
||||
# avoid resolution ambiguity between the two cwds.
|
||||
|
||||
RAW_DATA_ROOT ?= /absolute/path/to/raw_data
|
||||
Reference in New Issue
Block a user