pllmR12: Piranha: A GPU Platform for Secure Computation
Piranha separates device, protocol and application layers for GPU-accelerated secret-sharing computation. PLLM status: tracked.
Status: tracked
Year: 2022
Authors: Jean-Luc Watson, Sameer Wagh, Raluca Ada Popa
External source: Piranha: A GPU Platform for Secure Computation
Source contribution
Piranha separates device, protocol and application layers for GPU-accelerated secret-sharing computation.
PLLM perspective
Wrap original GPU kernels through a narrow native interface before selective Rust/CUDA ports. Preserve protocol party counts and numeric domains in comparisons.
Previous CPU limb/packing experiments are not a GPU Piranha reproduction.
This paper remains tracked. Named modules below are implementation targets, not finished PLLM components; no reproduction, integration, assurance, or matched benchmark is claimed.
The source's boundary remains binding: Kernel reuse does not turn a multi-online-worker protocol into the preferred single-evaluator topology.
PLLM modules and documentation
Target module identifiers: kernel.modular_gpu, runtime.device_buffers. These identifiers describe scoped work unless this page explicitly links an implementation source. Dependencies: None.
Backlog
This source is assigned to reimplementation priority 7, Preparation, compression, and acceleration. Its source-registry order is 12. Follow current gates and unfinished work in the research backlog.
R11: Factored Edge-Valued Binary Decision Diagrams
Factored edge-valued diagrams share subfunctions through additive and multiplicative edge normalization. PLLM status: tracked.
R13: SIGMA: Secure GPT Inference with Function Secret Sharing
SIGMA provides FSS-based secure transformer operations and GPU-backed inference with preprocessing. PLLM status: tracked.