fanhqme2

fanhqme2 / gpupi

Public

Computing 1000000000 digits of pi on GPU, in 5 seconds.

19
0
100% credibility
Found Mar 26, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Cuda
AI Summary

A graphics card-powered tool that computes up to one billion decimal digits of pi in record time using clever math shortcuts.

How It Works

1
🔍 Discover gpupi

You hear about a super-fast tool that uses your computer's graphics power to calculate billions of digits of pi in seconds.

2
📥 Get the files

Download the ready-to-use files from the project page to your computer.

3
🛠️ Prepare your setup

Check that your computer has a powerful graphics card and follow simple preparation steps to get everything ready.

4
🔨 Build the calculator

Run a quick build process to turn the files into your personal pi calculator program.

5
🚀 Calculate pi digits

Tell the program how many digits you want, and watch it crunch the numbers lightning-fast on your graphics card.

6
💾 Save your results

The digits pour out to a file or screen, ready for you to save and use.

🎉 Record-breaking pi!

You now have billions of accurate pi digits, faster than ever, perfect for math fun or records.

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Star Growth

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AI-Generated Review

What is gpupi?

GPU-accelerated CUDA tool that computes up to 1,000,000,000 digits of pi in seconds using the Chudnovsky algorithm on NVIDIA hardware. Feed it a digit count via CLI like `./pi_bs_gpu 1000000000 > pi.txt` for raw decimal output, or add `--benchmark` for summaries and previews, `--profile-stages` for timings, or `--full-print` for hex too. Outputs verified hashes to confirm accuracy, perfect for gpupi 1b runs on gpu linux setups.

Why is it gaining traction?

Crushes y-cruncher benchmarks—4.6s for 1e9 digits on RTX 5090 vs 5.1s—making it a go-to for gpupi download and record-chasing in deep computing github circles. Simple make-based build with CUDA 13, standalone tests for every kernel, and workspace stats help tune for scale computing github workloads. Stands out in gpu pixel android or gpupixel ios adjacent perf tests by prioritizing raw speed over generality.

Who should use this?

Math hobbyists or GPU benchmarkers chasing pi records like gpupi 3.3 or gpupi 32b on ampere computing github rigs. Researchers in confidential computing github, sustainable computing github, or visual computing github needing quick high-precision constants for simulations. Skip if you want general multiprecision—it's pi-tuned with NTT limits.

Verdict

Grab it for experimental gpu fun if you've got RTX firepower; 19 stars and 1.0% credibility score reflect niche maturity, but thorough docs, tests, and verifiable speed make it a solid prototype over scrap computing github alternatives.

(198 words)

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