Revisiting Out-of-SSA Translation for Correctness, Code Quality, and Efficiency

Compared to the previous versions, the only change is correcting an awful typo that made Algorithm 1 wrong. Line 18 is not "if b = loc(pred(b))" but simply "if b = loc(b)".Static single assignment (SSA) form is an intermediate program representation in which many code optimizations can be performed with fast and easy-to-implement algorithms. However, some of these optimizations create situations where the SSA variables arising from the same original variable now have overlapping live ranges. This complicates the translation out of SSA code into standard code. There are three issues to consider: correctness, code quality (elimination of copies), and algorithm efficiency (speed and memory footprint). Briggs et al. proposed patches to correct the initial approach of Cytron et al. A cleaner and more general approach was proposed by Sreedhar et al., along with techniques to reduce the number of generated copies. We propose a new approach based on coalescing and a precise view of interferences, in which correctness and optimizations are separated. Our approach is provably correct and simpler to implement, with no patches or particular cases as in previous solutions, while reducing the number of generated copies. Also, experiments with SPEC CINT2000 show that it is 2x faster and 10x less memory-consuming than the Method~III of Sreedhar et al., which makes it suitable for just-in-time compilation

On-stack replacement, distilled

On-stack replacement (OSR) is essential technology for adaptive optimization, allowing changes to code actively executing in a managed runtime. The engineering aspects of OSR are well-known among VM architects, with several implementations available to date. However, OSR is yet to be explored as a general means to transfer execution between related program versions, which can pave the road to unprecedented applications that stretch beyond VMs. We aim at filling this gap with a constructive and provably correct OSR framework, allowing a class of general-purpose transformation functions to yield a special-purpose replacement. We describe and evaluate an implementation of our technique in LLVM. As a novel application of OSR, we present a feasibility study on debugging of optimized code, showing how our techniques can be used to fix variables holding incorrect values at breakpoints due to optimizations

Towards register allocation of SSA-form programs

In this technical report, we present an architecture for register allocation on the SSA-form. We show, how the properties of SSA-form programs and their interference graphs can be exploited to develop new methods for spilling, coloring and coalescing. We present heuristic and optimal solution methods for these three subtasks

Future value based single assignment program representations and optimizations

An optimizing compiler internal representation fundamentally affects the clarity, efficiency and feasibility of optimization algorithms employed by the compiler. Static Single Assignment (SSA) as a state-of-the-art program representation has great advantages though still can be improved. This dissertation explores the domain of single assignment beyond SSA, and presents two novel program representations: Future Gated Single Assignment (FGSA) and Recursive Future Predicated Form (RFPF). Both FGSA and RFPF embed control flow and data flow information, enabling efficient traversal program information and thus leading to better and simpler optimizations. We introduce future value concept, the designing base of both FGSA and RFPF, which permits a consumer instruction to be encountered before the producer of its source operand(s) in a control flow setting. We show that FGSA is efficiently computable by using a series T1/T2/TR transformation, yielding an expected linear time algorithm for combining together the construction of the pruned single assignment form and live analysis for both reducible and irreducible graphs. As a result, the approach results in an average reduction of 7.7%, with a maximum of 67% in the number of gating functions compared to the pruned SSA form on the SPEC2000 benchmark suite. We present a solid and near optimal framework to perform inverse transformation from single assignment programs. We demonstrate the importance of unrestricted code motion and present RFPF. We develop algorithms which enable instruction movement in acyclic, as well as cyclic regions, and show the ease to perform optimizations such as Partial Redundancy Elimination on RFPF

On Extracting Course-Grained Function Parallelism from C Programs

To efficiently utilize the emerging heterogeneous multi-core architecture, it is essential to exploit the inherent coarse-grained parallelism in applications. In addition to data parallelism, applications like telecommunication, multimedia, and gaming can also benefit from the exploitation of coarse-grained function parallelism. To exploit coarse-grained function parallelism, the common wisdom is to rely on programmers to explicitly express the coarse-grained data-flow between coarse-grained functions using data-flow or streaming languages. This research is set to explore another approach to exploiting coarse-grained function parallelism, that is to rely on compiler to extract coarse-grained data-flow from imperative programs. We believe imperative languages and the von Neumann programming model will still be the dominating programming languages programming model in the future. This dissertation discusses the design and implementation of a memory data-flow analysis system which extracts coarse-grained data-flow from C programs. The memory data-flow analysis system partitions a C program into a hierarchy of program regions. It then traverses the program region hierarchy from bottom up, summarizing the exposed memory access patterns for each program region, meanwhile deriving a conservative producer-consumer relations between program regions. An ensuing top-down traversal of the program region hierarchy will refine the producer-consumer relations by pruning spurious relations. We built an in-lining based prototype of the memory data-flow analysis system on top of the IMPACT compiler infrastructure. We applied the prototype to analyze the memory data-flow of several MediaBench programs. The experiment results showed that while the prototype performed reasonably well for the tested programs, the in-lining based implementation may not efficient for larger programs. Also, there is still room in improving the effectiveness of the memory data-flow analysis system. We did root cause analysis for the inaccuracy in the memory data-flow analysis results, which provided us insights on how to improve the memory data-flow analysis system in the future

Formalizing the SSA-based Compiler for Verified Advanced Program Transformations

Compilers are not always correct due to the complexity of language semantics and transformation algorithms, the trade-offs between compilation speed and verifiability,etc.The bugs of compilers can undermine the source-level verification efforts (such as type systems, static analysis, and formal proofs) and produce target programs with different meaning from source programs. Researchers have used mechanized proof tools to implement verified compilers that are guaranteed to preserve program semantics and proved to be more robust than ad-hoc non-verified compilers. The goal of the dissertation is to make a step towards verifying an industrial strength modern compiler--LLVM, which has a typed, SSA-based, and general-purpose intermediate representation, therefore allowing more advanced program transformations than existing approaches. The dissertation formally defines the sequential semantics of the LLVM intermediate representation with its type system, SSA properties, memory model, and operational semantics. To design and reason about program transformations in the LLVM IR, we provide tools for interacting with the LLVM infrastructure and metatheory for SSA properties, memory safety, dynamic semantics, and control-flow-graphs. Based on the tools and metatheory, the dissertation implements verified and extractable applications for LLVM that include an interpreter for the LLVM IR, a transformation for enforcing memory safety, translation validators for local optimizations, and verified SSA construction transformation. This dissertation shows that formal models of SSA-based compiler intermediate representations can be used to verify low-level program transformations, thereby enabling the construction of high-assurance compiler passes

XARK: an extensible framework for automatic recognition of computational kernels

This is a post-peer-review, pre-copyedit version of an article published in ACM Transactions on Programming Languages and Systems. The final authenticated version is available online at: http://dx.doi.org/10.1145/1391956.1391959[Abstract] The recognition of program constructs that are frequently used by software developers is a powerful mechanism for optimizing and parallelizing compilers to improve the performance of the object code. The development of techniques for automatic recognition of computational kernels such as inductions, reductions and array recurrences has been an intensive research area in the scope of compiler technology during the 90's. This article presents a new compiler framework that, unlike previous techniques that focus on specific and isolated kernels, recognizes a comprehensive collection of computational kernels that appear frequently in full-scale real applications. The XARK compiler operates on top of the Gated Single Assignment (GSA) form of a high-level intermediate representation (IR) of the source code. Recognition is carried out through a demand-driven analysis of this high-level IR at two different levels. First, the dependences between the statements that compose the strongly connected components (SCCs) of the data-dependence graph of the GSA form are analyzed. As a result of this intra-SCC analysis, the computational kernels corresponding to the execution of the statements of the SCCs are recognized. Second, the dependences between statements of different SCCs are examined in order to recognize more complex kernels that result from combining simpler kernels in the same code. Overall, the XARK compiler builds a hierarchical representation of the source code as kernels and dependence relationships between those kernels. This article describes in detail the collection of computational kernels recognized by the XARK compiler. Besides, the internals of the recognition algorithms are presented. The design of the algorithms enables to extend the recognition capabilities of XARK to cope with new kernels, and provides an advanced symbolic analysis framework to run other compiler techniques on demand. Finally, extensive experiments showing the effectiveness of XARK for a collection of benchmarks from different application domains are presented. In particular, the SparsKit-II library for the manipulation of sparse matrices, the Perfect benchmarks, the SPEC CPU2000 collection and the PLTMG package for solving elliptic partial differential equations are analyzed in detail.Ministeiro de Educación y Ciencia; TIN2004-07797-C02Ministeiro de Educación y Ciencia; TIN2007-67537-C03Xunta de Galicia; PGIDIT05PXIC10504PNXunta de Galicia; PGIDIT06PXIB105228P