MLIR (software)

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Explore the main themes, entities and connections around MLIR (software). Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Dialects

Static single-assignment form Control flow Debugging Parsing Functions

17 related topics

Ecosystem

Computation Hardware platforms CPUs Ahead-of-time (AOT)Just-in-time (JIT) compilation

13 related topics

History

XLA XLA Runtime Tensor Processing Unit Xilinx

7 related topics

Transformations

Objects Type conversion Type system CPUs GPUs

6 related topics

Key facts & relationships

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Developer: LLVM Developer Group
License: Apache License 2.0 with LLVM Exception
Operating system: Cross-platform
Original authors: Chris Lattner, Mehdi Amini, Uday Bondhugula, and others
Release: 2019; 7 years ago (2019)
Type: Compiler

Topics to explore

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Overview

Compiler
LLVM
Intermediate representation
Domain-specific compilers Domain-specific language
Heterogeneous computing
Dialects MLIR (software)
Machine code
Chris Lattner
Google
Machine learning
Hardware acceleration
High-level synthesis
TensorFlow
Mojo Mojo (programming language)
Apache License 2.0

History

XLA Accelerated Linear Algebra
XLA Source-to-source compiler
Runtime Runtime system
Tensor Processing Unit
Xilinx
Digital signal processing
PyTorch

Dialects

Static single-assignment form
Control flow
Debugging
Parsing
Functions Function (computer programming)
Codebase
Mathematical operations
Integers
Floating-point
Affine Affine transformation
Polyhedral
Optimization
Structured
GPU
Programming Computer programming
Inference
LLVM IR

Operation definition specification

Transformations

Objects Object (computer science)
Type conversion
Type system
CPUs Central processing unit
GPUs Graphics processing unit
Greedily Greedy algorithm

Traits and interfaces

Applications

Ecosystem

Computation
Hardware platforms Hardware platform
CPUs CPU
Ahead-of-time (AOT) Ahead-of-time compilation
Just-in-time (JIT) compilation Just-in-time compilation
Code generation Code generation (compiler)
ONNX Open Neural Network Exchange
Reference implementation
Dataflow architecture
Triton Triton (programming language)?action=edit&redlink=1
Kernels Kernel (operating system)
Systems programming
Python Python (programming language)

Advanced semantic analysis

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Map overview Semantic statistics

Nodes75

Edges74

Triples38

Avg. degree1.97

Density0.026667

Components1

How this topic connects Entity context

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MLIR (software)

Top relations

Developer · 1

MLIR (software) → LLVM Developer Group

License · 1

MLIR (software) → Apache License 2.0 with LLVM Exception

Operating system · 1

MLIR (software) → Cross-platform

Original authors · 1

MLIR (software) → Chris Lattner, Mehdi Amini, Uday Bondhugula, and others

Release · 1

MLIR (software) → 2019; 7 years ago (2019)

Type · 1

MLIR (software) → Compiler

Website · 1

MLIR (software) → mlir.llvm.org

Written in · 1

MLIR (software) → C++

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

mlir compiler dialects operations intermediate representation llvm operation code used hardware ecosystem dialect tensorflow provides compilation also project infrastructure framework

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
MLIR (software)	Developer	LLVM Developer Group	1.00	infobox
MLIR (software)	License	Apache License 2.0 with LLVM Exception	1.00	infobox
MLIR (software)	Operating system	Cross-platform	1.00	infobox
MLIR (software)	Original authors	Chris Lattner, Mehdi Amini, Uday Bondhugula, and others	1.00	infobox
MLIR (software)	Release	2019; 7 years ago (2019)	1.00	infobox
MLIR (software)	Type	Compiler	1.00	infobox
MLIR (software)	Website	mlir.llvm.org	1.00	infobox
MLIR (software)	Written in	C++	1.00	infobox
machine learning	instance of	It was designed to address challenges in building compilers for modern workloads	0.80	text
hardware acceleration	instance of	It was designed to address challenges in building compilers for modern workloads	0.80	text
and high-level synthesis by providing reusable components	instance of	It was designed to address challenges in building compilers for modern workloads	0.80	text
standardizing the representation of intermediate computations across different programming languages	instance of	It was designed to address challenges in building compilers for modern workloads	0.80	text

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Connections between topic areas Semantic bridges

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Min side: 3