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MLIR: Scaling Compiler Infrastructure for Domain Specific Computation
IEEE/ACM International Symposium on Code Generation and Optimization, 2021This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR addresses software fragmentation, compilation for heterogeneous hardware, significantly reducing the cost of building domain specific compilers ...
Chris Lattner +9 more
semanticscholar +1 more source
An LLM Compiler for Parallel Function Calling
International Conference on Machine Learning, 2023The reasoning capabilities of the recent LLMs enable them to execute external function calls to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skills, or lack of access to private data.
Sehoon Kim +6 more
semanticscholar +1 more source
A comprehensive study of deep learning compiler bugs
ESEC/SIGSOFT FSE, 2021There are increasing uses of deep learning (DL) compilers to generate optimized code, boosting the runtime performance of DL models on specific hardware.
Qingchao Shen +5 more
semanticscholar +1 more source
ACM Computing Surveys, 2020
Virtually any software running on a computer has been processed by a compiler or a compiler-like tool. Because compilers are such a crucial piece of infrastructure for building software, their correctness is of paramount importance.
Junjie Chen +6 more
semanticscholar +1 more source
Virtually any software running on a computer has been processed by a compiler or a compiler-like tool. Because compilers are such a crucial piece of infrastructure for building software, their correctness is of paramount importance.
Junjie Chen +6 more
semanticscholar +1 more source
Triton: an intermediate language and compiler for tiled neural network computations
MAPL@PLDI, 2019The validation and deployment of novel research ideas in the field of Deep Learning is often limited by the availability of efficient compute kernels for certain basic primitives.
Philippe Tillet +2 more
semanticscholar +1 more source
Annual Review in Automatic Programming, 1963
Publisher Summary This chapter presents detailed specification of a system for describing the form and meaning of the statements in a phrase structure language, for example, a scientific autocode. Given such a description, the compiler compiler will generate a compiler for the language, that is, a program that can read and translate another program ...
Derrick Morris +3 more
openaire +2 more sources
Publisher Summary This chapter presents detailed specification of a system for describing the form and meaning of the statements in a phrase structure language, for example, a scientific autocode. Given such a description, the compiler compiler will generate a compiler for the language, that is, a program that can read and translate another program ...
Derrick Morris +3 more
openaire +2 more sources
The verified CakeML compiler backend
Journal of Functional Programming, 2019 The CakeML compiler is, to the best of our knowledge, the most realistic verified compiler for a functional programming language to date. The architecture of the compiler, a sequence of intermediate languages through which high-level features are ...
Yong Kiam Tan +2 more
exaly +2 more sources
Optimization aspects of compiler-compilers
ACM SIGPLAN Notices, 1970A decade of experience with prototype versions of compiler-compilers, some of which have been successful and some of which have not been so successful, leads us to the conclusion that we can now engineer good compiler-compilers which can generate efficient compilers that generate efficient object code.
Thomas A. Standish, Thomas E. Cheatham
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CHET: an optimizing compiler for fully-homomorphic neural-network inferencing
ACM-SIGPLAN Symposium on Programming Language Design and Implementation, 2019Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations on encrypted data without requiring a secret key. Recent cryptographic advances have pushed FHE into the realm of practical applications.
Roshan Dathathri +7 more
semanticscholar +1 more source
Compiler-based graph representations for deep learning models of code
International Conference on Compiler Construction, 2020In natural language processing, novel methods in deep learning, like recurrent neural networks (RNNs) on sequences of words, have been very successful. In contrast to natural languages, programming languages usually have a well-defined structure.
Alexander Brauckmann +3 more
semanticscholar +1 more source