From 9d9be42cb2893f0e1b0223f833a89a8eb34036c6 Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Fri, 16 Jan 2026 03:47:12 +0000
Subject: [PATCH] Optimize tridiagonal_solve_torch
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The optimized code applies **`@torch.compile(mode="reduce-overhead")`** to the tridiagonal solver function, achieving a **6% overall speedup** (26.2ms → 24.5ms). This optimization works by leveraging PyTorch's JIT compilation to reduce overhead from multiple sequential tensor operations.

**What changed:**
- Added `@torch.compile(mode="reduce-overhead")` decorator to the function
- No algorithmic changes—the Thomas algorithm implementation remains identical

**Why it's faster:**
The original code performs numerous small tensor operations in Python loops (indexing, arithmetic, divisions). Each operation incurs Python interpreter overhead and separate CUDA kernel launches. `torch.compile` with `"reduce-overhead"` mode:

1. **Fuses operations**: Combines multiple tensor operations into optimized fused kernels, reducing memory traffic
2. **Reduces kernel launch overhead**: Minimizes the cost of launching many small CUDA operations
3. **Optimizes memory access patterns**: Better utilizes GPU memory bandwidth through operation fusion

The `"reduce-overhead"` mode specifically targets reducing the fixed costs per operation, which is ideal for this workload with many sequential small tensor operations.

**Performance characteristics:**
- Test results show **dramatic improvements for larger systems**: 851% faster for n=100, 738% faster for n=50, 1012% faster for n=100 in different test configurations
- **Smaller systems see mixed results**: Some smaller systems (n=2-5) show 18-56% slowdown due to compilation overhead outweighing benefits
- **Sweet spot is medium-to-large systems** (n≥20): The compilation overhead amortizes well, and kernel fusion provides substantial gains

**Impact on workloads:**
Without function_references available, the general applicability depends on typical system sizes:
- If called repeatedly with large systems (n>50) in numerical simulations or scientific computing, the speedup compounds significantly
- First call incurs compilation overhead (~100ms typical), but subsequent calls benefit fully—ideal for iterative algorithms
- For applications solving many small systems (n<10), the original version may actually be preferable
---
 code_to_optimize/sample_code.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/code_to_optimize/sample_code.py b/code_to_optimize/sample_code.py
index d356ce807..b2f1d4040 100644
--- a/code_to_optimize/sample_code.py
+++ b/code_to_optimize/sample_code.py
@@ -225,6 +225,7 @@ def longest_increasing_subsequence_length_jax(arr):
     return int(jnp.max(dp))
 
 
+@torch.compile(mode="reduce-overhead")
 def tridiagonal_solve_torch(a, b, c, d):
     device = b.device
     dtype = b.dtype