submission 66197

import torch
import triton
import triton.language as tl

@triton.jit
def vectoradd_kernel(
    a_ptr,  # Pointer to first input tensor
    b_ptr,  # Pointer to second input tensor
    c_ptr,  # Pointer to output tensor
    N,      # Total number of elements
    BLOCK_SIZE: tl.constexpr,  # Number of elements per block
):
    """
    Optimized float16 vector addition kernel.
    Each program processes BLOCK_SIZE elements.
    """
    # Get program ID
    pid = tl.program_id(0)
    
    # Compute block start offset
    block_start = pid * BLOCK_SIZE
    
    # Generate offsets for this block
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    
    # Create mask for boundary checking (handles non-multiple of BLOCK_SIZE)
    mask = offsets < N
    
    # Load data with vectorized operations (float16)
    # Use eviction policy for better cache utilization
    a = tl.load(a_ptr + offsets, mask=mask, other=0.0, eviction_policy='evict_first')
    b = tl.load(b_ptr + offsets, mask=mask, other=0.0, eviction_policy='evict_first')
    
    # Perform addition
    c = a + b
    
    # Store result with vectorized write
    tl.store(c_ptr + offsets, c, mask=mask)


def custom_kernel(data):
    """
    Main kernel function that launches the Triton kernel.
    
    Args:
        data: Tuple of (A, B, C) where A and B are input tensors,
              and C is the pre-allocated output tensor.
    
    Returns:
        C: Output tensor containing element-wise sum
    """
    A, B, C = data
    
    # Ensure inputs are contiguous for optimal memory access
    A = A.contiguous()
    B = B.contiguous()
    
    # Get tensor dimensions
    N = A.numel()
    
    # Choose optimal block size based on GPU architecture
    # Larger block sizes for better memory coalescing and fewer kernel launches
    # Powers of 2 work best for GPU memory systems
    BLOCK_SIZE = 1024  # Optimal for most modern GPUs (A100, H100, B200)
    
    # Calculate grid size (number of programs to launch)
    grid = lambda meta: (triton.cdiv(N, meta['BLOCK_SIZE']),)
    
    # Launch kernel
    vectoradd_kernel[grid](
        A, B, C, N,
        BLOCK_SIZE=BLOCK_SIZE,
    )
    
    return C