dfa-for-co-slr/pubs.csv

1	ID	D1	D2	D3	D4	D5	D6	D7	D8	D9
2	P1	Kildall, Gary A.	1973	A unified approach to global program optimization	Allows straight-line optimization techniques for branch structure		General Techniques	None	Constant Propagation, Common Subexpression Elimination, Register Optimization
3	P2	Rastislav Bodik; Rajiv Gupta; Mary Lou Soffa	1997	Interprocedural conditional branch elimination	Reduction of instruction count	Exponential/Polynomial worst-case time complexity	ICC	C	Conditional Branch Elimination, Elimination of correlated conditionals and operations
4	P3	Ramsey, Norman; Dias, João; Peyton Jones, Simon	2010	Hoopl: a modular, reusable library for dataflow analysis and transformation	Reusable library for DFA	DFA typically entangled with compiler, Algorithms complicated and hard to understand	Library, used by GHC	Custom	Interleaved analysis and rewriting, speculative rewriting, computing fixed points, dynamic fault isolation	Only usable from Haskell
5	P4	Edvinsson, Marcus; Lowe, Welf	2010	A multi-threaded approach for data-flow analysis	Accuracy more important than speed since at compile time	Low usability for JIT because of time consumption, DFA is computation intense, DFA often implemented sequentially	Custom	SSA, Java	High speed-up for analysis, for benchmarks without benefit max loss of 13% speed	Only speed-up of 1.78 on 8 cores
6	P5	Joisha, Pramod G.; Schreiber, Robert S.; Banerjee, Prithviraj; Boehm, Hans J.; Chakrabarti, Dhruva R.	2011	A technique for the effective and automatic reuse of classical compiler optimizations on multithreaded code		Sequential transformations can not be applied to parallel code, Need to watch out for data races and data synchronization	GCC	C	Bidirectional DFA across synchronizations in well-synchronized programs, Can reuse existing optimizations, Value numbering, Copy propagation
7	P6	Edvinsson, Marcus; Lundberg, Jonas; Löwe, Welf	2011	Parallel points-to analysis for multi-core machines	Points-To Analysis analyses whole program	SSA nodes in Points-to SSA are sequentially dependent	Custom	SSA, Java	Parallel Points-to Analysis
8	P7	Tang, Xiaolong; Järvi, Jaakko	2012	Summary-based data-flow analysis that understands regular composite objects and iterators		Hard to make assumptions about user-defined types	LLVM	LLVM IR	Common Sub-expression elimination, Copy propagation, Equational reasoning
9	P8	Urban, Bernhard; Steinlechner, Harald	2013	Implementing a Java JIT compiler in Haskell: case study			Custom JIT	Java Bytecode	Liveness Analysis
10	P9	Duboscq, Gilles; Stadler, Lukas; Würthinger, Thomas; Simon, Doug; Wimmer, Christian; Mössenböck, Hanspeter	2013	Graal IR: An Extensible Declarative Intermediate Representation	Easier optimization implementation with Graph-IR		GraalVM, uses P3	Java Bytecode	IR which is simple to run optimizations on	Not implemented: commutative edges on nodes for better congruent detection
11	P10	Zaidi, Ali Mustafa; Greaves, David	2015	Value State Flow Graph: A Dataflow Compiler IR for Accelerating Control-Intensive Code in Spatial Hardware	Performance improvement through execution of dataflow graph		Custom LLVM Backend	LLVM IR		Structs, Multidimensional-Arrays not supported
12	P11	Ginsbach, Philip; Crawford, Lewis; O'Boyle, Michael F. P.	2018	CAnDL: a domain specific language for compiler analysis	DSL for optimization implementation makes implementation simpler and iterations quicker	Optimizations are hard to implement in LLVM, Simple peephole optimization is 30000 LOC	DSL to LLVM Pass	LLVM IR		Moderate compile time increase, no formal verification
13	P12	Pathade, Komal; Khedker, Uday P.	2019	Path sensitive MFP solutions in presence of intersecting infeasible control flow path segments		Path insensitive solutions overapproximate data flow values	TCS Embedded Code Analyzer	C	Reaching Definition, Def-Use Pairs, Unitialized Variables, 300% precision increase	100% analysis time increase
14	P13	Reissmann, Nico; Meyer, Jan Christian; Bahmann, Helge; Själander, Magnus	2020	RVSDG: An Intermediate Representation for Optimizing Compilers		Structures like loops not encoded in SSA	Custom	LLVM IR	Common Node Elimination, Dead Node Elimination
15	P14	Shi, Qingkai; Zhang, Charles	2020	Pipelining bottom-up data flow analysis		Calling dependence limit parallelism of bottom-up DFA	Custom based on LLVM	LLVM IR	2x to 3x speedup by relaxing calling dependence	Inline assembly and c++ stl not modeled
16	P15	Aigner, Christoph; Barany, Gergö; Mössenböck, Hanspeter	2024	Lazy Sparse Conditional Constant Propagation in the Sea of Nodes		Detecting all compile time constant is undecidable problem	GraalVM	Sea of Nodes / Graal IR	Lazy iteration to reduce portion of necessary graph