GCC vs Clang: Same Instructions, Different Performance (AGU Insight)

*I noticed something interesting while running a GCC vs Clang benchmark.
*
Same code. Same machine.
Both loops are scalar (no vectorization).

Yet… GCC consistently used fewer CPU cycles.

At first, this doesn’t make sense.

If both:

execute roughly the same instructions
are not vectorised

Why is there a performance gap?

🔍 The Missing Piece: It’s Not Just Instructions
Most people focus on:
instruction count
vectorization

But in this case, that’s not the full story.

What actually matters more is:

• how address computations are structured
• how instructions are scheduled
• how well latency is hidden

Here is the data

⚙️ AGU Pressure (Address Generation Units)

On x86 CPUs, memory instructions rely on AGUs (Address Generation Units).

Complex addressing patterns like:

base + index * scale + offset

👉 increase AGU pressure

Whereas simpler patterns like:
pointer++
👉 are cheaper and easier for the CPU to execute efficiently

🧪 What I Observed
GCC:
Generates simpler addressing patterns
Reduces AGU contention
Keeps execution more consistent
Clang:
Shows higher AGU pressure
More stalls
Less efficient scheduling (in this case)

⚡ Key Takeaway
It’s not just about what instructions exist.

It’s about:
How efficiently the compiler feeds the CPU pipeline

Same instruction count ≠ same performance.

📊 Why This Matters

In tight loops:

AGU pressure
addressing patterns
instruction scheduling

👉 can matter as much as (or more than) vectorization

🔗 Want to Dive Deeper?

👉 Full benchmark + assembly breakdown:

👉 Complete analysis article:

💬 Discussion

Have you seen cases where:

similar assembly
same instruction count

👉 still results in very different performance?

Would love to hear your observations.