# Prism: Symbolic Superoptimization of Tensor Programs

arXiv: https://arxiv.org/abs/2604.15272 · HTML: https://mlsanity.com/paper/2604.15272/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Beyond Data and Model Parallelism for Deep Neural Networks](https://mlsanity.com/paper/1807.05358/) (2018)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [NVIDIA Tensor Core Programmability, Performance & Precision](https://mlsanity.com/paper/1803.04014/) (2018)
- [Astra: A Multi-Agent System for GPU Kernel Performance Optimization](https://mlsanity.com/paper/2509.07506/) (2025)
- [Mirage: A Multi-Level Superoptimizer for Tensor Programs](https://mlsanity.com/paper/2405.05751/) (2024)
- [Equality Saturation for Tensor Graph Superoptimization](https://mlsanity.com/paper/2101.01332/) (2021)
- [KernelFoundry: Hardware-aware evolutionary GPU kernel optimization](https://mlsanity.com/paper/2603.12440/) (2026)
- [GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism](https://mlsanity.com/paper/2406.17145/) (2024)
- [Optimal Kernel Orchestration for Tensor Programs with Korch](https://mlsanity.com/paper/2406.09465/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cleave: Scaling Tensor Program Optimization via Decoupled Algebraic Search and Operator Scheduling](https://mlsanity.com/paper/2610.07742/) (2026)

## Abstract (arXiv)

This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large classes of tensor programs by symbolically representing some execution parameters. Prism organizes optimization as a two-level search: it constructs symbolic graphs that represent families of programs, and then instantiates them into concrete implementations. This formulation enables structured pruning of provably suboptimal regions of the search space using symbolic reasoning over operator semantics, algebraic identities, and hardware constraints. We develop techniques for efficient symbolic graph generation, equivalence verification via e-graph rewriting, and parameter instantiation through auto-tuning. Together, these components allow Prism to bridge the rigor of exhaustive search with the scalability required for modern ML workloads. Evaluation on five commonly used LLM workloads shows that Prism achieves up to 2.2× speedup over best superoptimizers and 4.9× over best compiler-based approaches, while reducing end-to-end optimization time by up to 3.4×.