26,793 research outputs found

    Parallel Copy Elimination on Data Dependence Graphs

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    Register allocation regained much interest in recent years due to the development of decoupled strategies that split the problem into separate phases: spilling, register assignment, and copy elimination. Traditional approaches to copy elimination during register allocation are based on interference graphs and register coalescing. Variables are represented as nodes in a graph, which are coalesced, if they can be assigned the same register. However, decoupled approaches strive to avoid interference graphs and thus often resort to local recoloring. A common assumption of existing coalescing and recoloring approaches is that the original ordering of the instructions in the program is not changed. This work presents an extension of a local recoloring technique called Parallel Copy Motion. We perform code motion on data dependence graphs in order to eliminate useless copies and reorder instructions, while at the same time a valid register assignment is preserved. Our results show that even after traditional register allocation with coalescing our technique is able to eliminate an additional 3% (up to 9%) of the remaining copies and reduce the weighted costs of register copies by up to 25% for the SPECINT 2000 benchmarks. In comparison to Parallel Copy Motion, our technique removes 11% (up to 20%) more copies and up to 39% more of the copy costs

    Parameterized Construction of Program Representations for Sparse Dataflow Analyses

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    Data-flow analyses usually associate information with control flow regions. Informally, if these regions are too small, like a point between two consecutive statements, we call the analysis dense. On the other hand, if these regions include many such points, then we call it sparse. This paper presents a systematic method to build program representations that support sparse analyses. To pave the way to this framework we clarify the bibliography about well-known intermediate program representations. We show that our approach, up to parameter choice, subsumes many of these representations, such as the SSA, SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more frugal than the previous techniques used to construct SSI - Static Single Information - form programs. We produce intermediate representations isomorphic to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow problems that can be partitioned per variables. However, contrary to SEGs, we can handle - sparsely - problems that are not in this family

    Generalized Points-to Graphs: A New Abstraction of Memory in the Presence of Pointers

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    Flow- and context-sensitive points-to analysis is difficult to scale; for top-down approaches, the problem centers on repeated analysis of the same procedure; for bottom-up approaches, the abstractions used to represent procedure summaries have not scaled while preserving precision. We propose a novel abstraction called the Generalized Points-to Graph (GPG) which views points-to relations as memory updates and generalizes them using the counts of indirection levels leaving the unknown pointees implicit. This allows us to construct GPGs as compact representations of bottom-up procedure summaries in terms of memory updates and control flow between them. Their compactness is ensured by the following optimizations: strength reduction reduces the indirection levels, redundancy elimination removes redundant memory updates and minimizes control flow (without over-approximating data dependence between memory updates), and call inlining enhances the opportunities of these optimizations. We devise novel operations and data flow analyses for these optimizations. Our quest for scalability of points-to analysis leads to the following insight: The real killer of scalability in program analysis is not the amount of data but the amount of control flow that it may be subjected to in search of precision. The effectiveness of GPGs lies in the fact that they discard as much control flow as possible without losing precision (i.e., by preserving data dependence without over-approximation). This is the reason why the GPGs are very small even for main procedures that contain the effect of the entire program. This allows our implementation to scale to 158kLoC for C programs

    Compiling global name-space programs for distributed execution

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    Distributed memory machines do not provide hardware support for a global address space. Thus programmers are forced to partition the data across the memories of the architecture and use explicit message passing to communicate data between processors. The compiler support required to allow programmers to express their algorithms using a global name-space is examined. A general method is presented for analysis of a high level source program and along with its translation to a set of independently executing tasks communicating via messages. If the compiler has enough information, this translation can be carried out at compile-time. Otherwise run-time code is generated to implement the required data movement. The analysis required in both situations is described and the performance of the generated code on the Intel iPSC/2 is presented
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