CAMAL/plot.py at main · Zia-Rashid/CAMAL · GitHub

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#!/usr/bin/env python3
import csv
import sys
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

CSV = sys.argv[1] if len(sys.argv) > 1 else "./resources/bench.csv"


def load(path):
    with open(path) as f:
        return list(csv.DictReader(f))


def where(rows, **kv):
    out = []
    for r in rows:
        if all(r[k] == v for k, v in kv.items()):
            out.append(r)
    return out


def save(fig, name):
    fig.tight_layout()
    fig.savefig(name, dpi=120)
    plt.close(fig)
    print("wrote", name)


def chart_utilization(rows):
    r = where(rows, kind="array_size")
    labels = [f"{x['R']}x{x['C']}" for x in r]
    util = [float(x["utilization"]) * 100 for x in r]
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.bar(labels, util, color="#4C72B0")
    ax.set_xlabel("array size (R x C)")
    ax.set_ylabel("MAC utilization (%)")
    ax.set_title("utilization vs array size (M=N=32, K=64)")
    for i, v in enumerate(util):
        ax.text(i, v + 0.4, f"{v:.1f}", ha="center", fontsize=9)
    save(fig, "chart_utilization.png")


def chart_dataflow(rows):
    r = where(rows, kind="dataflow")
    labels = [x["dataflow"] for x in r]
    cycles = [int(x["total_cycles"]) for x in r]
    fig, ax = plt.subplots(figsize=(5, 4))
    ax.bar(labels, cycles, color=["#4C72B0", "#DD8452"])
    ax.set_ylabel("total cycles")
    ax.set_title("OS vs WS, same GEMM (32x32x64, 8x8 array)")
    for i, v in enumerate(cycles):
        ax.text(i, v, str(v), ha="center", va="bottom", fontsize=9)
    save(fig, "chart_dataflow.png")


def chart_sched(rows):
    r = where(rows, kind="sched")
    shapes, naive, pipe = [], [], []
    for x in r:
        if x["sched"] != "naive":
            continue
        shapes.append(f"{x['M']}x{x['N']}x{x['K']}")
        naive.append(int(x["total_cycles"]))
    for x in where(r, sched="pipelined"):
        pipe.append(int(x["total_cycles"]))
    idx = range(len(shapes))
    w = 0.38
    fig, ax = plt.subplots(figsize=(7, 4))
    ax.bar([i - w / 2 for i in idx], naive, w, label="naive", color="#C44E52")
    ax.bar([i + w / 2 for i in idx], pipe, w, label="pipelined", color="#55A868")
    ax.set_xticks(list(idx))
    ax.set_xticklabels(shapes)
    ax.set_ylabel("total cycles")
    ax.set_title("naive vs pipelined (double-buffered)")
    ax.legend()
    save(fig, "chart_sched.png")


def chart_vs_cpu(rows):
    r = where(rows, kind="vs_cpu")
    labels = [f"{x['M']}x{x['N']}x{x['K']}" for x in r]
    speed = [float(x["speedup_vs_cpu"]) for x in r]
    fig, ax = plt.subplots(figsize=(7, 4))
    ax.bar(labels, speed, color="#8172B3")
    ax.axhline(64, ls="--", color="gray", lw=1)
    ax.text(0, 64, " 64x ceiling (R*C MACs/cycle)", va="bottom", fontsize=8, color="gray")
    ax.set_ylabel("speedup vs scalar CPU")
    ax.set_title("accelerator vs scalar CPU (pipelined, 8x8 array)")
    for i, v in enumerate(speed):
        ax.text(i, v + 0.5, f"{v:.1f}x", ha="center", fontsize=9)
    save(fig, "chart_vs_cpu.png")


def main():
    rows = load(CSV)
    chart_utilization(rows)
    chart_dataflow(rows)
    chart_sched(rows)
    chart_vs_cpu(rows)


if __name__ == "__main__":
    main()