Scientific Abstract
Dense activation patterns incur severe memory bandwidth bottlenecks during multi-turn agent reasoning. Polyhedral loop transformations and automatic kernel fusion eliminate redundant global memory round-trips.
Methodology & Computational Modeling
Polyhedral loop transformations and automatic kernel fusion eliminate redundant global memory round-trips.
MATHEMATICAL BASIS & CONTINUUM EQUATIONS:
Achieved 3.4x higher token generation speed on sparse MoE models; cut VRAM footprint by 44%.Observational Telemetry Datasets
This project ingests and assimilates open data streams from international registries:
NVIDIA H100, RTX 4090, Apple Silicon Metal Performance Shaders
Validated Empirical Findings
- Polyhedral Transformations
- Kernel Fusion
- Constant Memory Execution
- 3.4x Speedup
Investigators & Collaborators
CUDA · Georbit Research
Triton · Georbit Research
C++ · Georbit Research
PyTorch · Georbit Research
LLVM · Georbit Research
Data Access & Reproducibility
Under Georbit's open science charter, all compiler runtimes, verification proofs, and deterministic state benchmarks are archived permanently for peer review.