Dead-code elimination | EPFL Graph Search

In compiler theory, dead-code elimination (DCE, dead-code removal, dead-code stripping, or dead-code strip) is a compiler optimization to remove dead code (code that does not affect the program results). Removing such code has several benefits: it shrinks program size, an important consideration in some contexts, and it allows the running program to avoid executing irrelevant operations, which reduces its running time. It can also enable further optimizations by simplifying program structure. Dead code includes code that can never be executed (unreachable code), and code that only affects dead variables (written to, but never read again), that is, irrelevant to the program. Examples Consider the following example written in C. int foo(void) { int a = 24; int b = 25; /* Assignment to dead variable / int c; c = a * 4; return c; b = 24; / Unreachable code */ return 0; } Simple analysis of the uses of values would show that the value of b after the first assig

Optimizing Higher-Order Functions in Scala

Iulian Dragos

Scala relies on libraries as the default mechanism for language extension. While this provides an elegant solution for growing the language, the performance penalty incurred by call-by-name parameters, boxing and anonymous functions is high. We show that inlining alone is not enough to remove this overhead, and present a solution based on decompilation of library code combined with inlining, dead code elimination, and copy propagation. We evaluate our approach on two language extensions, Java-like assert and C-like for-loops and show improvements of up to 45%.

2008

Compiling Scala for Performance

Iulian Dragos

Scala is a new programming language bringing together object-oriented and functional programming. Its defining features are uniformity and extensibility. Scala offers great flexibility for programmers, allowing them to grow the language through libraries. Oftentimes what seems like a language feature is in fact implemented in a library, effectively giving programmers the power of language designers. The downside of this flexibility is that familiar looking code may hide unexpected performance costs. It is important for Scala compilers to bring down this cost as much as possible. We identify several areas of impact for Scala performance: higher-order functions and closures, and generic containers used with primitive types. We present two complementary approaches for improving performance in these areas: optimizations and specialization. Compiler optimization can bring down the cost through a combination of aggressive inlining of higher-order functions, an extended version of copy-propagation and dead-code elimination. Both anonymous functions and boxing can be eliminated by this approach. We show on a number of benchmarks that these language features can be up to 5 times faster when properly optimized, on current day JVMs. We propose a new approach to compiling parametric polymorphism for performance at primitive types. We mix a homogeneous translation scheme with user-directed specialization for primitive types. Type parameters may be annotated to require specialization of code depending on them. We propose definition-site specialization for primitive types, achieving separate compilation and no boxing when both the definition and call site are specialized. Specialized classes are compatible with unspecialized code, and specialization agnostic code can work with specialized instances, meaning that specialization is opportunistic. We present a formalism of a small subset of Scala with specialization and prove that specialization preserves types. We implemented this translation in the Scala compiler and report on improvements on a set of benchmarks, showing that specialization can make programs more than two times faster.

EPFL2010

Linker call graph with full Dotty support

Nicolas Alexander Stucki

This project consisted in bringing the linker call graph up to date with dotty/master, restructure the code, make it support the full Scala language and make it support calls to Java code. Also implemented dead code elimination of unreachable methods based on the call graph as a first optimization. Also describes a new graph visualization.

2017

Compiling Scala for Performance

Iulian Dragos

EPFL2010