app.py

import time
import random
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr

# Visualization style
plt.style.use('seaborn-v0_8-darkgrid')

# Random seed for reproducibility
random.seed(42)
np.random.seed(42)

def simulate_batches(num_workers=4, 
                     batch_time=500,     # ms
                     network_latency=200, # ms
                     mode='synchronous', 
                     num_batches=10):
    """
    Simulates mini-batch scheduling under synchronous vs asynchronous update strategies.
    Returns worker timelines and performance metrics.
    """
    timelines = []  # [(worker_id, start, end, active_flag)]
    current_time = [0] * num_workers  # track each worker's time progress

    for batch in range(num_batches):
        for w in range(num_workers):
            # Each worker takes a random batch processing time with jitter
            proc_time = random.uniform(batch_time * 0.8, batch_time * 1.2)
            start = current_time[w]
            end = start + proc_time
            timelines.append((w, start, end, 'active'))
            current_time[w] = end

        if mode == 'synchronous':
            # Barrier: wait for all workers to finish
            max_time = max(current_time)
            for w in range(num_workers):
                if current_time[w] < max_time:
                    timelines.append((w, current_time[w], max_time, 'idle'))
                    current_time[w] = max_time
            # Add sync overhead (e.g., gradient aggregation)
            current_time = [t + network_latency for t in current_time]
        else:
            # Asynchronous mode adds random network jitter
            current_time = [t + random.uniform(0, network_latency * 0.3) for t in current_time]

    total_time = max(current_time)
    idle_time = sum(
        end - start for (w, start, end, flag) in timelines if flag == 'idle'
    )
    total_blocks = sum(end - start for (_, start, end, _) in timelines)
    idle_percent = (idle_time / total_blocks) * 100
    throughput = (num_workers * num_batches * 1000) / total_time  # batches per second (approx)

    metrics = {
        "epoch_time_ms": total_time,
        "idle_percent": round(idle_percent, 2),
        "throughput": round(throughput, 2)
    }

    return timelines, metrics


def plot_timeline(timelines, metrics, num_workers):
    colors = {'active': '#4CAF50', 'idle': '#E74C3C'}
    fig, ax = plt.subplots(figsize=(10, 5))

    for (w, start, end, flag) in timelines:
        ax.barh(y=w, width=end-start, left=start, color=colors[flag], edgecolor='black')

    ax.set_xlabel("Time (ms)")
    ax.set_ylabel("Worker ID")
    ax.set_title("Batch Scheduler Simulation")
    ax.set_yticks(range(num_workers))
    ax.set_yticklabels([f"W{i}" for i in range(num_workers)])
    ax.invert_yaxis()

    text_summary = (
        f"Epoch Duration: {metrics['epoch_time_ms']:.2f} ms\n"
        f"Idle Time: {metrics['idle_percent']}%\n"
        f"Throughput: {metrics['throughput']} batches/sec"
    )

    plt.figtext(0.72, 0.35, text_summary, fontsize=10, 
                bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
    plt.tight_layout()
    return fig


def run_simulation(num_workers, batch_time, network_latency, mode, num_batches):
    timelines, metrics = simulate_batches(
        num_workers=int(num_workers),
        batch_time=float(batch_time),
        network_latency=float(network_latency),
        mode=mode,
        num_batches=int(num_batches)
    )
    fig = plot_timeline(timelines, metrics, num_workers)
    summary = (
        f"Mode: {mode.capitalize()}\n"
        f"Epoch Time: {metrics['epoch_time_ms']:.2f} ms\n"
        f"Idle Time: {metrics['idle_percent']} %\n"
        f"Throughput: {metrics['throughput']} batches/sec"
    )
    return fig, summary


interface = gr.Interface(
    fn=run_simulation,
    inputs=[
        gr.Slider(1, 8, value=4, step=1, label="Number of Workers"),
        gr.Slider(100, 1000, value=500, step=50, label="Batch Processing Time (ms)"),
        gr.Slider(50, 500, value=200, step=25, label="Network Latency (ms)"),
        gr.Radio(["synchronous", "asynchronous"], value="synchronous", label="Mode"),
        gr.Slider(5, 30, value=10, step=1, label="Number of Batches per Epoch"),
    ],
    outputs=[
        gr.Plot(label="Timeline Visualization"),
        gr.Textbox(label="Simulation Summary", lines=8, max_lines=12, show_copy_button=True)
    ],
    title="🧠 Batch Scheduler Simulator",
    description="Visualize how synchronous vs asynchronous batch scheduling affects throughput, idle time, and epoch duration.",
    examples=[
        [4, 500, 200, "synchronous", 10],
        [8, 400, 150, "asynchronous", 15]
    ]
)

if __name__ == "__main__":
    interface.launch()