Efficient implementation of lattice operations
ACM Transactions on Programming Languages and Systems (TOPLAS)
Efficiently computing static single assignment form and the control dependence graph
ACM Transactions on Programming Languages and Systems (TOPLAS)
An efficient representation for sparse sets
ACM Letters on Programming Languages and Systems (LOPLAS)
Nesting of reducible and irreducible loops
ACM Transactions on Programming Languages and Systems (TOPLAS)
Modern compiler implementation in Java
Modern compiler implementation in Java
Practical improvements to the construction and destruction of static single assignment form
Software—Practice & Experience
Static single assignment form for machine code
Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
Characterizations of Reducible Flow Graphs
Journal of the ACM (JACM)
On loops, dominators, and dominance frontier
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Code generator optimizations for the ST120 DSP-MCU core
CASES '00 Proceedings of the 2000 international conference on Compilers, architecture, and synthesis for embedded systems
Fast copy coalescing and live-range identification
PLDI '02 Proceedings of the ACM SIGPLAN 2002 Conference on Programming language design and implementation
LCPC'06 Proceedings of the 19th international conference on Languages and compilers for parallel computing
HiPEAC'08 Proceedings of the 3rd international conference on High performance embedded architectures and compilers
Register allocation via coloring of chordal graphs
APLAS'05 Proceedings of the Third Asian conference on Programming Languages and Systems
Register allocation for programs in SSA-Form
CC'06 Proceedings of the 15th international conference on Compiler Construction
Revisiting Out-of-SSA Translation for Correctness, Code Quality and Efficiency
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Linear scan register allocation on SSA form
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Graph-coloring and treescan register allocation using repairing
CASES '11 Proceedings of the 14th international conference on Compilers, architectures and synthesis for embedded systems
Efficient liveness computation using merge sets and DJ-graphs
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
A non-iterative data-flow algorithm for computing liveness sets in strict SSA programs
APLAS'11 Proceedings of the 9th Asian conference on Programming Languages and Systems
Constraint-Based register allocation and instruction scheduling
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization
Formal Verification of an SSA-Based Middle-End for CompCert
ACM Transactions on Programming Languages and Systems (TOPLAS)
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Liveness analysis is an important analysis in optimizing compilers. Liveness information is used in several optimizations and is mandatory during the code-generation phase. Two drawbacks of conventional liveness analyses are that their computations are fairly expensive and their results are easily invalidated by program transformations. We present a method to check liveness of variables that overcomes both obstacles. The major advantage of the proposed method is that the analysis result survives all program transformations except for changes in the control-flow graph. For common program sizes our technique is faster and consumes less memory than conventional data-flow approaches. Thereby, we heavily make use of SSA-form properties, which allow us to completely circumvent data-flow equation solving. We evaluate the competitiveness of our approach in an industrial strength compiler. Our measurements use the integer part of the SPEC2000 benchmarks and investigate the liveness analysis used by the SSA destruction pass. We compare the net time spent in liveness computations of our implementation against the one provided by that compiler. The results show that in the vast majority of cases our algorithm, while providing the same quality of information, needs less time: an average speed-up of 16%.