Conditional Constant Propagation Research Articles

SSA Translation Is an Abstract Interpretation

Static single assignment (SSA) form is a popular intermediate representation that helps implement useful static analyses, including global value numbering (GVN), sparse dataflow analyses, or SMT-based abstract interpretation or model checking. However, the precision of the SSA translation itself depends on static analyses, and a priori static analysis is even indispensable in the case of low-level input languages like machine code.To solve this chicken-and-egg problem, we propose to turn the SSA translation into a standard static analysis based on abstract interpretation. This allows the SSA translation to be combined with other static analyses in a single pass, taking advantage of the fact that it is more precise to combine analyses than applying passes in sequence.We illustrate the practicality of these results by writing a simple dataflow analysis that performs SSA translation, optimistic global value numbering, sparse conditional constant propagation, and loop-invariant code motion in a single small pass; and by presenting a multi-language static analyzer for both C and machine code that uses the SSA abstract domain as its main intermediate representation.

Pluggable abstract domains for analyzing embedded software

Many abstract value domains such as intervals, bitwise, constants, and value-sets have been developed to support dataflow analysis. Different domains offer alternative tradeoffs between analysis speed and precision. Furthermore, some domains are a better match for certain kinds of code than others. This paper presents the design and implementation of cXprop, an analysis and transformation tool for C that implements "conditional X propagation," a generalization of the well-known conditional constant propagation algorithm where X is an abstract value domain supplied by the user. cXprop is interprocedural, context-insensitive, and achieves reasonable precision on pointer-rich codes. We have applied cXprop to sensor network programs running on TinyOS, in order to reduce code size through interprocedural dead code elimination, and to find limited-bitwidth global variables. Our analysis of global variables is supported by a novel concurrency model for interrupt-driven software. cXprop reduces TinyOS application code size by an average of 9.2% and predicts an average data size reduction of 8.2% through RAM compression.

A Functional Perspective on SSA Optimisation Algorithms

The static single assignment (SSA) form is central to a range of optimisation algorithms relying on data flow information, and hence, to the correctness of compilers employing those algorithms. It is well known that the SSA form is closely related to lambda terms (i.e., functional programs), and, considering the large amount of energy expended on theories and frameworks for formal reasoning in the lambda calculus, it seems only natural to leverage this connection to improve our capabilities to reason about compiler optimisations. In this paper, we discuss a new formalisation of the mapping from SSA programs to a restricted form of lambda terms, called administrative normal form (ANF). We conjecture that this connection improves our ability to reason about SSA-based optimisation algorithms and provide a first data point by presenting an ANF variant of a well known SSA-based conditional constant propagation algorithm.

Which pointer analysis should I use?

During the past two decades many different pointer analysis algorithms have been published. Although some descriptions include measurements of the effectiveness of the algorithm, qualitative comparisons among algorithms are difficult because of varying infrastructure, benchmarks, and performance metrics. Without such comparisons it is not only difficult for an implementor to determine which pointer analysis is appropriate for their application, but also for a researcher to know which algorithms should be used as a basis for future advances. This paper describes an empirical comparison of the effectiveness of five pointer analysis algorithms on C programs. The algorithms vary in their use of control flow information (flow-sensitivity) and alias data structure, resulting in worst-case complexity from linear to polynomial. The effectiveness of the analyses is quantified in terms of compile-time precision and efficiency. In addition to measuring the direct effects of pointer analysis, precision is also reported by determining how the information computed by the five pointer analyses affects typical client analyses of pointer information: Mod/Ref analysis, live variable analysis and dead assignment identification, reaching definitions analysis, dependence analysis, and conditional constant propagation and unreachable code identification. Efficiency is reported by measuring analysis time and memory consumption of the pointer analyses and their clients.

A Constant Propagation Algorithm for Explicitly Parallel Programs

In this paper, we present a constant propagation algorithm for explicitly parallel programs, which we call the Concurrent Sparse Conditional Constant propagation algorithm. This algorithm is an extension of the Sparse Conditional Constant propagation algorithm. Without considering the interaction between threads, classical optimizations lead to an incorrect program transformation for parallel programs. To make analyzing parallel programs possible, a new intermediate representation is needed. We introduce the Concurrent Static Single Assignment (CSSA) form to represent explicitly parallel programs with interleaving semantics and synchronization. The only parallel construct considered in this paper is cobegin/coend. A new confluence function, the π-assignment, which summarizes the information of interleaving statements between threads, is introduced. The Concurrent Control Flow Graph, which contains information about conflicting statements, control flow, and synchronization, is used as an underlying representation for the CSSA from.

Toward a complete transformational toolkit for compilers

PIM is an equational logic designed to function as a “transformational toolkit” for compilers and other programming tools that analyze and manipulate imperative languages. It has been applied to such problems as program slicing, symbolic evaluation, conditional constant propagation, and dependence analysis. PIM consists of the untyped lambda calculus extended with an algebraic data type that characterizes the behavior of lazy stores and generalized conditionals. A graph form of PIM terms is by design closely related to several intermediate representations commonly used in optimizing compilers. In this article, we show that PIM's core algebraic component, PIM t , possesses a complete equational axiomatization (under the assumption of certain reasonable restrictions on term formation). This has the practical consequence of guaranteeing that every semantics-preserving transformation on a program representable in PIM t can be derived by application of PIM t rules. We systematically derive the complete PIM t logic as the culmination of a sequence of increasingly powerful equational systems starting from a straightforward “interpreter” for closed PIM t terms. This work is an intermediate step in a larger program to develop a set of well-founded tools for manipulation of imperative programs by compilers and other systems that perform program analysis.