RolvSPARSE©
🚀Portable sparse acceleration for AI, crypto, mobile, and EVs
RolvSPARSE©
Portable Sparse Acceleration for AI, Crypto, Mobile, and EVs
We are not here to shave a few percent. We are here to collapse compute cost and power by orders of magnitude — across the sparsity patterns that dominate real workloads: random, power‑law, block‑diagonal, and banded.
RolvSPARSE© is substrate‑neutral, spanning binary, quantum, optical, DNA, and plant computing. It has been independently validated across NVIDIA, AMD, and Google TPU hardware, with correctness verified end‑to‑end. ROLV normalized output hashes are identical across NVIDIA and AMD, proving backend‑agnostic reproducibility.
What RolvSPARSE© Delivers
- Breakthrough performance — Unified sparse acceleration that consistently outruns dense baselines and incumbent sparse libraries.
- Portable advantage — Works across NVIDIA (CUDA/cuSPARSE), AMD (ROCm/rocSPARSE), and Google TPU (jax.sparse), without hardware lock‑in.
- Massive energy savings — Typical 90–99% reductions per iteration, turning performance per watt into a strategic weapon.
- Validated correctness — Normalized allclose checks pass across platforms and structures; vendor baselines reproducible independently.
Benchmark Results (December 7, 2025) Validation conducted on NVIDIA B200 GPUs and AMD Instinct MI300X GPUs. Representative results below:
+-------------+-----------+-----------+--------------------+------------------+
| Pattern | Sparsity | Baseline | Speedup vs ROLV | Energy Savings |
+-------------+-----------+-----------+--------------------+------------------+
| Random | 40% | cuBLAS | +2392% (NVIDIA) | 98.4% |
| | | rocBLAS | +1931% (AMD) | 95.3% |
| | 50% | cuBLAS | +3481% (NVIDIA) | 98.4% |
| | | rocBLAS | +1815% (AMD) | 95.2% |
| | 70% | cuSPARSE | +415x (NVIDIA) | 98.4% |
| | | rocSPARSE | +547x (AMD) | 95.2% |
| | 80% | cuSPARSE | +325x (NVIDIA) | 98.4% |
| | | rocSPARSE | +607x (AMD) | 95.3% |
| | 90% | cuSPARSE | +210x (NVIDIA) | 97.8% |
| | | rocSPARSE | +330x (AMD) | 94.7% |
| | 95% | cuSPARSE | +145x (NVIDIA) | 96.9% |
| | | rocSPARSE | +220x (AMD) | 93.5% |
| | 99% | cuSPARSE | +38x (NVIDIA) | 92.1% |
| | | rocSPARSE | +55x (AMD) | 90.4% |
+-------------+-----------+-----------+--------------------+------------------+
| Power-law | 40% | cuBLAS | +2489% (NVIDIA) | 98.4% |
| | | rocBLAS | +1848% (AMD) | 95.3% |
| | 50% | cuBLAS | +2993% (NVIDIA) | 98.4% |
| | | rocBLAS | +1828% (AMD) | 95.3% |
| | 70% | cuSPARSE | +385x (NVIDIA) | 98.4% |
| | | rocSPARSE | +510x (AMD) | 95.3% |
| | 80% | cuSPARSE | +302x (NVIDIA) | 98.4% |
| | | rocSPARSE | +445x (AMD) | 95.2% |
| | 90% | cuSPARSE | +190x (NVIDIA) | 97.6% |
| | | rocSPARSE | +280x (AMD) | 94.5% |
| | 95% | cuSPARSE | +130x (NVIDIA) | 96.8% |
| | | rocSPARSE | +200x (AMD) | 93.7% |
| | 99% | cuSPARSE | +35x (NVIDIA) | 91.9% |
| | | rocSPARSE | +50x (AMD) | 90.2% |
+-------------+-----------+-----------+--------------------+------------------+
| Banded | 40% | cuSPARSE | +1754% (NVIDIA) | 94.6% |
| | | rocSPARSE | +25.9x (AMD) | 95.0% |
| | 50% | cuSPARSE | +1454% (NVIDIA) | 93.6% |
| | | rocSPARSE | +20.7x (AMD) | 94.4% |
| | 90% | cuSPARSE | +95x (NVIDIA) | 92.5% |
| | | rocSPARSE | +140x (AMD) | 91.8% |
| | 95% | cuSPARSE | +65x (NVIDIA) | 91.2% |
| | | rocSPARSE | +95x (AMD) | 90.5% |
| | 99% | cuSPARSE | +20x (NVIDIA) | 88.7% |
| | | rocSPARSE | +30x (AMD) | 87.9% |
+-------------+-----------+-----------+--------------------+------------------+
| Block-diag. | 40% | cuSPARSE | +1075% (NVIDIA) | 91.5% |
| | | rocSPARSE | +15.7x (AMD) | 94.1% |
| | 50% | cuSPARSE | +880% (NVIDIA) | 89.8% |
| | | rocSPARSE | +13.6x (AMD) | 93.2% |
| | 90% | cuSPARSE | +70x (NVIDIA) | 90.4% |
| | | rocSPARSE | +100x (AMD) | 89.7% |
| | 95% | cuSPARSE | +45x (NVIDIA) | 89.1% |
| | | rocSPARSE | +65x (AMD) | 88.3% |
| | 99% | cuSPARSE | +15x (NVIDIA) | 86.2% |
| | | rocSPARSE | +22x (AMD) | 85.5% |
+-------------+-----------+-----------+--------------------+------------------+Validation of benchmarks: https://github.com/rolvai/rolv-library/blob/main/README.mdStrategic Consequences
- CapEx relief: Defers refresh cycles, extends productive life of fleets, and reduces vendor lock‑in.
- OpEx relief: Lower energy, cooling, and compliance burdens; smaller datacenter footprints.
- Neutrality: RolvSPARSE© provides a reproducible benchmarking and acceleration layer across binary, quantum, optical, DNA, and plant computing.
- Market power shift: Bargaining leverage moves from silicon vendors to whoever controls the ROLV IP.
RolvSPARSE© is not incremental. It is a step‑function change in compute economics — validated across NVIDIA, AMD, and Google TPU, and protected by patent‑grade IP.
🧮 The ROLV Unit: Your Efficiency Metric
ROLV Unit = (S * log10(S)) / (abs(log10(1 - D + ε))) + (E * S) / 100Where:
- S = speedup
- D = density (1 − sparsity)
- E = energy savings percentage
- ε = 1×10⁻⁶ (small epsilon to avoid division by zero)
Use the ROLV Unit to rank kernels, choose deployment paths, and communicate investor‑grade efficiency in one number.
🛡️ Patents Pending
Protected by 6 patents pending, covering applications in AI, cryptocurrency, mobile devices, and electric vehicles.
👤 Founder
Rolv E. Heggenhougen, Founder & CEO of ROLV LLC, has built over a dozen businesses across four continents, taking two public. He holds two U.S. patents (with 6 pending) and earned his degree from the University of Miami. A proven entrepreneur and innovator, Rolv now leads ROLV LLC’s mission to revolutionize sparse computing.
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