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moven0831 merged 1 commit into from
Jul 11, 2025
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Profiling results #91

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22 changes: 22 additions & 0 deletions README.md
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Expand Up @@ -6,6 +6,25 @@ By adopting sparse matrices, it improves the Pippenger algorithm [Pip76](https:/

One thing to highlight is that our implementation runs most computations on the GPU, but it’s still slower than the CPU-only solution like [Arkworks](https://github.com/arkworks-rs). However, because we target client-side devices with limited resources, applying a hybrid approach, leveraging both CPU and GPU for MSM tasks and combining the results at the end, can yield an implementation slightly faster than a pure-CPU one. Check the write-up below for estimated speedups with this hybrid method.

## Profiling summary (v1 vs v2)

Environment: M1 Pro, macOS 15.2, curve `ark_bn254`, dataset 2^20 unless stated. Medians of 5 runs.

### v2 → v1

| metric | v1[^1] | v2[^2] | gain |
|---|---|---|---|
| end-to-end latency | 10.3 s | **0.42 s** | **×24** |
| GPU occupancy | 32 % | 76 % | +44 pp |
| CPU share | 19 % | **<3 %** | –16 pp |
| peak VRAM | 1.6 GB | **220 MB** | –7.3× |

Key changes:

* single sparse-matrix kernel eliminates most launches and memory thrash
* CSR buckets keep data on-device → near-zero host↔GPU traffic
* on-GPU radix sort makes preprocessing parallel

## How to use

Metal MSM v2 works with `arkworks v0.4.x`; just include the crate in your `Cargo.toml`.
Expand Down Expand Up @@ -144,3 +163,6 @@ mod tests {
## Acknowledgements

This work was initially sponsored by a joint grant from [PSE](https://pse.dev/) and [0xPARC](https://0xparc.org/). It is currently incubated by PSE.

[^1]: https://hackmd.io/@yaroslav-ya/rJkpqc_Nke
[^2]: https://hackmd.io/@yaroslav-ya/HyFA7XAQll
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