benchmarks/vortex_100x.py

import torch
import time
import triton
import triton.language as tl

@triton.jit
def vortex_monolith_100x_kernel(X, Out, N, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < N
    x = tl.load(X + offsets, mask=mask)
    
    # 100 FUSED OPS (Pure Register Persistence)
    # This is how we break the 10x barrier
    res = x
    for _ in range(100):
        res = res * 1.001 + 0.001
    
    tl.store(Out + offsets, res, mask=mask)

def run_siege():
    N = 1024 * 1024 * 128
    print("--- BLITZ VORTEX: 100x FUSION SIEGE (H200) ---")
    X = torch.randn(N, device="cuda")
    Out = torch.empty_like(X)
    
    # 1. Standard Way (100 Kernel Launches)
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(10): 
        curr = X
        for _ in range(100): curr = curr * 1.001 + 0.001
    torch.cuda.synchronize()
    eager_ms = (time.time() - start) / 10 * 1000
    
    # 2. Blitz Way (1 Monolith Launch)
    grid = (triton.cdiv(N, 16384),)
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(10): vortex_monolith_100x_kernel[grid](X, Out, N, BLOCK_SIZE=16384)
    torch.cuda.synchronize()
    vortex_ms = (time.time() - start) / 10 * 1000
    
    print(f"Eager Latency (100 passes): {eager_ms:.4f}ms")
    print(f"Blitz Latency (1 pass): {vortex_ms:.4f}ms")
    print(f"SILICON ART SPEEDUP: {eager_ms/vortex_ms:.2f}x")

if __name__ == "__main__":
    run_siege()