Add GPU benchmark script, Julia set generator, and updated docs

remote-gpu-client/bench_gpu.py

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+import torch
+import time
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+import sys
+import numpy as np
+
+def run_benchmark():
+    print("=" * 60)
+    print(f"🚀 SYSTEM BENCHMARK on {os.uname().nodename}")
+    print("=" * 60)
+    
+    print(f"Python: {sys.version.split()[0]}")
+    print(f"PyTorch: {torch.__version__}")
+    
+    cuda_available = torch.cuda.is_available()
+    if cuda_available:
+        gpu_name = torch.cuda.get_device_name(0)
+        print(f"✅ GPU DETECTED: {gpu_name}")
+        print(f"   Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")
+    else:
+        print("⚠️  NO GPU DETECTED. Running on CPU.")
+        print("   (To enable GPU, switch Hardware in HF Space settings)")
+        
+    print("-" * 60)
+    
+    times = {}
+    
+    # MATRIX SIZE
+    N = 4000
+    
+    # CPU TEST
+    print(f"1️⃣  CPU Test ({N}x{N} Matrix Mul)...")
+    start_time = time.time()
+    a_cpu = torch.randn(N, N)
+    b_cpu = torch.randn(N, N)
+    c_cpu = torch.matmul(a_cpu, b_cpu)
+    cpu_time = time.time() - start_time
+    times['CPU'] = cpu_time
+    print(f"   ⏱️  Time: {cpu_time:.4f} seconds")
+    
+    # GPU TEST
+    if cuda_available:
+        print(f"2️⃣  GPU Test ({N}x{N} Matrix Mul)...")
+        # Warmup
+        torch.matmul(torch.randn(100,100).cuda(), torch.randn(100,100).cuda())
+        
+        start_time = time.time()
+        a_gpu = torch.randn(N, N).cuda()
+        b_gpu = torch.randn(N, N).cuda()
+        # Synchronize for accurate timing
+        torch.cuda.synchronize()
+        start_computation = time.time()
+        c_gpu = torch.matmul(a_gpu, b_gpu)
+        torch.cuda.synchronize()
+        gpu_time = time.time() - start_computation
+        times['GPU'] = gpu_time
+        print(f"   ⏱️  Time: {gpu_time:.4f} seconds")
+        
+        speedup = cpu_time / gpu_time
+        print(f"   🚀 SPEEDUP: {speedup:.2f}x")
+    else:
+        print("2️⃣  GPU Test SKIPPED (No CUDA)")
+        times['GPU'] = 0
+
+    # PLOT
+    print("-" * 60)
+    print("Creating comparison chart...")
+    plt.figure(figsize=(10, 6))
+    
+    models = list(times.keys())
+    durations = list(times.values())
+    colors = ['gray', 'green'] if cuda_available else ['gray', 'red']
+    
+    bars = plt.bar(models, durations, color=colors)
+    plt.ylabel('Secons (Lower is better)')
+    plt.title(f'Benchmark CPU vs GPU ({N}x{N} Matrix Mul)\n{gpu_name if cuda_available else "CPU Only"}')
+    
+    for bar in bars:
+        yval = bar.get_height()
+        if yval > 0:
+            plt.text(bar.get_x() + bar.get_width()/2, yval, f'{yval:.4f}s', ha='center', va='bottom')
+            
+    filename = "gpu_benchmark.png"
+    plt.savefig(filename)
+    print(f"💾 Saved to {filename}")
+
+if __name__ == "__main__":
+    run_benchmark()

remote-gpu-client/julia.py

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+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+import os
+
+print(f"🚀 Starting Julia Set Generation on {os.uname().nodename}...")
+
+def julia_set(h, w, c, max_iter=100):
+    """Generate Julia set for a given complex parameter c."""
+    y, x = np.ogrid[-1.5:1.5:h*1j, -1.5:1.5:w*1j]
+    z = x + y*1j
+    divtime = max_iter + np.zeros(z.shape, dtype=int)
+
+    for i in range(max_iter):
+        z = z**2 + c
+        diverge = z*np.conj(z) > 2**2 
+        div_now = diverge & (divtime == max_iter)
+        divtime[div_now] = i
+        z[diverge] = 2
+    
+    return divtime
+
+# Configuration
+H, W = 1000, 1500
+MAX_ITER = 200
+# Interesting Julia set parameter
+C = complex(-0.7, 0.27015) 
+
+print(f"Computing Julia set ({W}x{H}) for c={C}...")
+start_time = time.time()
+
+divtime = julia_set(H, W, C, MAX_ITER)
+
+compute_time = time.time() - start_time
+print(f"✅ Computation done in {compute_time:.4f}s")
+
+# Plot
+print("Rendering image...")
+plt.figure(figsize=(15, 10))
+plt.imshow(divtime, cmap='twilight', extent=[-1.5, 1.5, -1.5, 1.5])
+plt.axis('off')
+plt.title(f'Julia Set c={C} (Compute: {compute_time:.2f}s)')
+
+filename = "julia_gpu.png"
+plt.savefig(filename, bbox_inches='tight', pad_inches=0, dpi=150)
+print(f"💾 Saved to {filename}")