IRHash: Multi-Language Compiler Caching Using Intermediate Representation Hashing
By
matt_d
Right out the toaster. Reliable, with some real depth.
Summary
IRHash is a compiler caching technique that uses intermediate representation (IR) level hashing to improve compilation efficiency across multiple programming languages. Unlike traditional approaches that hash source code or preprocessed tokens, IRHash operates at the AST level to achieve higher accuracy and better end-to-end savings by reducing redundant compilations while minimizing cache lookup overhead.
Key quotes
· 4 pulledCompilation caches (CCs) save time, energy, and money by avoiding redundant compilations
Conceptually, a CC pays off if the achieved savings by cache hits outweigh the extra costs for cache lookups
Most techniques try to detect a cache hit early in the compilation process by hashing the (preprocessed/tokenized) source code
Hashing the AST has also been suggested to achieve even higher end-to-end savings, as the increased accuracy outweighs the addition
You might also wanna read
Go Language Performance Improvements: Shifting Memory Allocations from Heap to Stack
The article discusses recent efforts by the Go programming language team to improve performance by shifting more memory allocations from the
go.dev·3mo ago
Understanding Function Call Overhead and Compiler Inlining Optimization
The article discusses the performance implications of function calls in programming, explaining how compilers optimize code through techniqu
lemire.me·3mo ago
Understanding Partial Inlining: How Compilers Optimize Functions with Fast and Slow Paths
This technical blog post explores partial inlining as a compiler optimization technique, explaining how compilers can selectively inline onl
How Compiler Optimizers Recognize Patterns and Simplify Obfuscated Code
The article discusses how modern compilers and optimizers can recognize patterns in code and simplify complex-looking operations into effici
Why Average LLM Use Is Likely Destroying Value in Software Development
The author argues that, contrary to prevailing hype, the average use of Large Language Models (LLMs) is likely destroying value rather than
unessays.substack.com·8h ago
How AI Accelerated Prototyping: From Idea to Tangible in Record Time
The author reflects on how AI has transformed their prototyping workflow. Previously, the biggest bottleneck was the time needed to scaffold
darylcecile.net·8h ago