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CUDA Academy · Lesson

Register Pressure and Spills

Balancing reuse against occupancy.

Register Pressure and Spills is a free CUDA Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the CUDA Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Registers Are Precious

Registers are the fastest storage a thread has, but each SM holds only so many. How heavily a kernel uses them is called register pressure.

A Shared, Fixed Pool

Every resident thread draws from one register file per SM. The more registers each thread needs, the fewer threads can stay resident together.

Pressure Lowers Occupancy

High register use directly caps how many warps fit on an SM. That drop in occupancy can leave the hardware with too little work to hide latency.

What a Spill Is

When a thread needs more registers than exist, the compiler moves extra values out. This overflow is a register spill into slower memory.

Spills Land in Local Memory

Spilled values go to local memory, which lives in off-chip DRAM. Every spill replaces a free register access with a slow round trip.

See Your Register Count

Ask the compiler to report usage. Adding --ptxas-options=-v to nvcc prints registers per thread and any spill bytes for each kernel.

nvcc --ptxas-options=-v kernel.cu

Read the Spill Numbers

The report lists spill stores and loads. Any nonzero spill count is a warning sign that your kernel is paying for slow local memory traffic.

Cap Registers per Thread

You can set a ceiling at compile time. The --maxrregcount flag limits registers per thread, trading a little speed for higher occupancy.

nvcc --maxrregcount=32 kernel.cu

Hint with __launch_bounds__

A per-kernel hint is often better. __launch_bounds__ tells the compiler your block size so it can budget registers for the occupancy you want.

__global__ void __launch_bounds__(256)
myKernel() { /* ... */ }

Shrink the Live Set

Often you can simply hold fewer values at once. Recomputing a cheap result or narrowing variable scope reduces how many registers stay live.

Balance Reuse and Occupancy

ILP and unrolling raise pressure, while caps raise occupancy. The art is finding the balance that runs fastest, and only the profiler can tell you where it is.

Quick Check

Where do values go when a kernel runs out of registers?

Recap: Keep Pressure in Check

You learned that high register pressure cuts occupancy and can cause spills to slow DRAM. Measure usage, cap registers, and let the profiler guide you. 🎯

Frequently asked questions

Is the “Register Pressure and Spills” lesson free?

Yes — the full text of “Register Pressure and Spills” is free to read here on the web, and the CUDA Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the CUDA Academy course, upgrade to CoddyKit PRO.

What will I learn in “Register Pressure and Spills”?

Balancing reuse against occupancy. You practise CUDA Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start CUDA Academy?

No prior experience is required. CUDA Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Register Pressure and Spills” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this CUDA Academy lesson?

Yes. Every CUDA Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Instruction-Level Parallelism
  2. Loop Unrolling with #pragma unroll
  3. Vectorized Loads with float4
  4. Register Pressure and Spills
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