# Deep Learning Compiler

{% hint style="info" %}
No active maintenance.
{% endhint %}

## System Architecture

* MLIR: Scaling Compiler Infrastructure for Domain Specific Computation (CGO 2021) \[[Paper](https://research.google/pubs/pub49988/)] \[[Homepage](https://mlir.llvm.org/)]
  * Google
* TVM: An Automated End-to-End Optimizing Compiler for Deep Learning ([OSDI 2018](/reading-notes/conference/osdi-2018.md)) \[[Paper](https://www.usenix.org/conference/osdi18/presentation/chen)] \[[Code](https://github.com/apache/tvm)] \[[Homepage](https://www.usenix.org/conference/osdi18/presentation/chen)]
  * UW & AWS & SJTU & UC Davis & Cornell

## Tensor Program Generation

### Megakernel Compilation

* Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel ([MLSys 2026](/reading-notes/conference/mlsys-2026.md)) \[[Paper](https://mlsys.org/virtual/2026/poster/3592)] \[[arXiv](https://arxiv.org/abs/2604.13327)]
  * CMU
  * Introduce **Event Tensor**, a unified compiler abstraction that represents tiled-task dependencies as first-class tensors for dynamic megakernels.
  * Support both symbolic-shape dynamism and data-dependent dynamism, then lower the abstraction through the **Event Tensor Compiler (ETC)** with static and dynamic scheduling transformations.
  * Achieve state-of-the-art LLM serving latency, including up to 1.40x speedup on fused GEMM and Reduce-Scatter kernels, up to 1.23x on MoE workloads, and up to 3.5x lower engine warmup overhead.
* Mirage Persistent Kernel: A Compiler and Runtime for Mega-Kernelizing Tensor Programs (arXiv:2512.22219) \[[arXiv](https://arxiv.org/abs/2512.22219)] \[[Code](https://github.com/mirage-project/mirage)] \[[Homepage](https://catalyst.cs.cmu.edu/projects/mpk.html)]
  * CMU
  * Introduce **Mirage Persistent Kernel (MPK)**, the first compiler and runtime system that automatically transforms multi-GPU model inference into a single high-performance megakernel.
  * Use an SM-level graph representation and an in-kernel parallel runtime with decentralized scheduling to enable cross-operator software pipelining and fine-grained kernel overlap.
  * Reduce end-to-end LLM inference latency by up to 1.7x over kernel-per-operator serving systems while preserving flexible tensor-programming workflows.

### General Tensor Program Generation

* Cocktailer: Analyzing and Optimizing Dynamic Control Flow in Deep Learning ([OSDI 2023](/reading-notes/conference/osdi-2023.md)) \[[Paper](https://www.usenix.org/conference/osdi23/presentation/zhang-chen)]
  * THU & MSRA
  * Co-optimize the execution of control flow and data flow.
* Welder: Scheduling Deep Learning Memory Access via Tile-graph ([OSDI 2023](/reading-notes/conference/osdi-2023.md)) \[[Paper](https://www.usenix.org/conference/osdi23/presentation/shi)]
  * PKU & MSRA
  * Optimize memory access.
* Effectively Scheduling Computational Graphs of Deep Neural Networks toward Their Domain-Specific Accelerators ([OSDI 2023](/reading-notes/conference/osdi-2023.md)) \[[Paper](https://www.usenix.org/conference/osdi23/presentation/zhao)]
  * Stream Computing
  * GraphTurbo: Scheduler for DSA.
* EINNET: Optimizing Tensor Programs with Derivation-Based Transformations ([OSDI 2023](/reading-notes/conference/osdi-2023.md)) \[[Paper](https://www.usenix.org/conference/osdi23/presentation/zheng)]
  * THU & CMU
  * Leverage *transformations* between general tensor algebra expressions.
* AStitch: Enabling a New Multi-dimensional Optimization Space for Memory-Intensive ML Training and Inference on Modern SIMT Architectures (ASPLOS 2022) \[[Paper](https://dl.acm.org/doi/10.1145/3503222.3507723)]
  * Alibaba
  * Memory-intensive operators.
* Ansor: Generating High-Performance Tensor Programs for Deep Learning ([OSDI 2020](/reading-notes/conference/osdi-2020.md)) \[[Paper](https://www.usenix.org/conference/osdi20/presentation/zheng)]
  * UC Berkeley

## Acronyms

* DSA: Domain-Specific Architecture


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://paper.lingyunyang.com/paper-list/systems-for-ml/deep-learning-compiler.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.