Abstract
In this paper, we describe our experiences in co-designing a domain-specific compilation stack. Our motivation stems from the missed optimization opportunities we observed while implementing a computer vision library in Java. To tackle the performance shortcomings, we developed Indigo, a computer vision API co-designed with a compilation plugin for optimizing computer vision applications.
Indigo exploits the extensible nature of the Graal compiler which provides invocation plugins, that replace methods with dedicated nodes, and generates machine code compatible with both the Java Virtual Machine (JVM) and the SIMD hardware unit. Our approach improves performance by up to 66.75x when compared to pure Java implementations and by up to 2.75x when compared to the original C++ implementation. These performance improvements are the result of low-level concurrency, idiomatic implementation of algorithms, and by keeping temporary objects in the wider vector unit registers.
Indigo exploits the extensible nature of the Graal compiler which provides invocation plugins, that replace methods with dedicated nodes, and generates machine code compatible with both the Java Virtual Machine (JVM) and the SIMD hardware unit. Our approach improves performance by up to 66.75x when compared to pure Java implementations and by up to 2.75x when compared to the original C++ implementation. These performance improvements are the result of low-level concurrency, idiomatic implementation of algorithms, and by keeping temporary objects in the wider vector unit registers.
| Original language | English |
|---|---|
| Title of host publication | 14th International Conference on Managed Languages & Runtimes (ManLang) |
| Publisher | Association for Computing Machinery |
| DOIs | |
| Publication status | Published - 1 Sept 2017 |