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This paper studies static analysis on programs that create and traverse dynamic pointer-linked data structures. It introduces a new type of auxiliary structures, called {\em link graphs}, to depict the alias information of pointers and connection relationships of dynamic pointer-linked data structures. The link graphs can be used by compilers to detect side effects, to identify the patterns of traversal, and to gather the DEF-USE information of dynamic pointer-linked data structures. The results of the above compile-time analysis are essential for parallelization and optimizations on communication and synchronization overheads. Algorithms that perform compile-time analysis on side effects and DEF-USE information using link graphs will be proposed.

This paper presents a technique for dependence analysis on programs with pointers or dynamic recursive data structures. It differs from previously proposed approaches in analyzing structure access conflicts between traversal patterns before gathering alias and connection information. Conflict analysis is conducted under the assumption that each unique path leads to a distinct storage location, and hence traversal patterns can be analytically compared to identify possible conflicts. The rationale of this assumption is that if statements are deemed to be dependent by this approach, they are inherently sequential regardless of the shapes of the data structures they traverse. Consequently, there is no need to perform alias/connection analysis on the statements that construct such data structures. Furthermore, the information of traversal patterns gathered in conflict analysis phase can direct alias/connection analysis algorithm to focus on statements that are crucial to optimizations or parallelization. A such {\em traversal-pattern-sensitive} pointer analysis algorithm will also be presented.

Pointer analysis is essential for optimizing and parallelizing compilers. It examines pointer assignment statements and estimates pointer-induced aliases among pointer variables or possible shapes of dynamic recursive data structures. However, previously proposed techniques are not able to gather useful information or have to give up further optimizations when overall recursive data structures appear to be cyclic even though patterns of traversal are linear. The reason is that these proposed techniques perform pointer analysis without the knowledge of traversal patterns of dynamic recursive data structures to be constructed. This paper proposes an approach, {\em traversal-pattern-aware pointer analysis}, that has the ability to first identify the structures specified by traversal patterns of programs from cyclic data structures and then perform analysis on the specified structures. This paper presents an algorithm to perform shape analysis on the structures specified by traversal patterns. The advantage of this approach is that if the specified structures are recognized to be acyclic, parallelization or optimizations can be applied even when overall data structures might be cyclic. The DEF/USE information of pointer statements is used to relate the identified traversal patterns to the pointer statements which build recursive data structures. (Also cross-referenced as UMIACS-TR-97-66)

This paper presents a technique for dependence analysis on programs with pointers or dynamic recursive data structures. It differs from previously proposed approaches in analyzing structure access conflicts between traversal patterns before gathering alias and connection information. Conflict analysis is conducted under the assumption that each unique path leads to a distinct storage location, and hence traversal patterns can be analytically compared to identify possible conflicts. The rationale of this assumption is that if statements are deemed to be dependent by this approach, they are inherently sequential regardless of the shapes of the data structures they traverse. Consequently, there is no need to perform alias/connection analysis on the statements that construct such data structures. Furthermore, the information of traversal patterns gathered in conflict analysis phase can direct alias/connection analysis algorithm to focus on statements that are crucial to optimizations or parallelization. A such {\em traversal-pattern-sensitive} pointer analysis algorithm will also be presented.

Pointer analysis is essential for optimizing and parallelizing compilers. It examines pointer assignment statements and estimates pointer-induced aliases among pointer variables or possible shapes of dynamic recursive data structures. However, previously proposed techniques are not able to gather useful information or have to give up further optimizations when overall recursive data structures appear to be cyclic even though patterns of traversal are linear. The reason is that these proposed techniques perform pointer analysis without the knowledge of traversal patterns of dynamic recursive data structures to be constructed. This paper proposes an approach, {\em traversal-pattern-aware pointer analysis}, that has the ability to first identify the structures specified by traversal patterns of programs from cyclic data structures and then perform analysis on the specified structures. This paper presents an algorithm to perform shape analysis on the structures specified by traversal patterns. The advantage of this approach is that if the specified structures are recognized to be acyclic, parallelization or optimizations can be applied even when overall data structures might be cyclic. The DEF/USE information of pointer statements is used to relate the identified traversal patterns to the pointer statements which build recursive data structures. (Also cross-referenced as UMIACS-TR-97-66)

This paper studies static analysis on programs that create and traverse dynamic pointer-linked data structures. It introduces a new type of auxiliary structures, called {\em link graphs}, to depict the alias information of pointers and connection relationships of dynamic pointer-linked data structures. The link graphs can be used by compilers to detect side effects, to identify the patterns of traversal, and to gather the DEF-USE information of dynamic pointer-linked data structures. The results of the above compile-time analysis are essential for parallelization and optimizations on communication and synchronization overheads. Algorithms that perform compile-time analysis on side effects and DEF-USE information using link graphs will be proposed.

Pointer analysis is essential for optimizing and parallelizing compilers. It examines pointer assignment statements and estimates pointer-induced aliases among pointer variables or possible shapes of dynamic recursive data structures. However, previously proposed techniques are not able to gather useful information or have to give up further optimizations when overall recursive data structures appear to be cyclic even though patterns of traversal are linear. The reason is that these proposed techniques perform pointer analysis without the knowledge of traversal patterns of dynamic recursive data structures to be constructed. This paper proposes an approach, {\em traversal-pattern-aware pointer analysis}, that has the ability to first identify the structures specified by traversal patterns of programs from cyclic data structures and then perform analysis on the specified structures. This paper presents an algorithm to perform shape analysis on the structures specified by traversal patterns. The advantage of this approach is that if the specified structures are recognized to be acyclic, parallelization or optimizations can be applied even when overall data structures might be cyclic. The DEF/USE information of pointer statements is used to relate the identified traversal patterns to the pointer statements which build recursive data structures. (Also cross-referenced as UMIACS-TR-97-66)

Procedures are presented that are designed to help users efficiently program irregular problems (e.g. unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial dif- ferential equations solvers) on distributed memory machines. These procedures are also designed for use in compilers for distributed memory multiprocessors. The portable CHAOS pro- cedures are designed to support dynamic data distributions and to automatically generate send and receive messsage by capturing communications patterns at runtime. (Also cross-referenced as UMIACS-TR-95-34)

In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions. (Also cross-referenced as UMIACS-TR-94-55)

In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions. (Also cross-referenced as UMIACS-TR-94-55)

Procedures are presented that are designed to help users efficiently program irregular problems (e.g. unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial dif- ferential equations solvers) on distributed memory machines. These procedures are also designed for use in compilers for distributed memory multiprocessors. The portable CHAOS pro- cedures are designed to support dynamic data distributions and to automatically generate send and receive messsage by capturing communications patterns at runtime. (Also cross-referenced as UMIACS-TR-95-34)

In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions. (Also cross-referenced as UMIACS-TR-94-55)

Procedures are presented that are designed to help users efficiently program irregular problems (e.g. unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial dif- ferential equations solvers) on distributed memory machines. These procedures are also designed for use in compilers for distributed memory multiprocessors. The portable CHAOS pro- cedures are designed to support dynamic data distributions and to automatically generate send and receive messsage by capturing communications patterns at runtime. (Also cross-referenced as UMIACS-TR-95-34)

CHARMM "Chemistry at Harvard Macromolecular Mechanics" is a program that is widely used to model and simulate macromolecular systems. CHARMM has been parallelized by using the CHAOS runtime support library on distributed memory architectures. This implementation distributes both data and computations over processors. This data-parallel strategy should make it possible to simulate very large molecules on large numbers of processors. In order to minimize communication among processors and to balance computational load, a variety of partitioning approaches are employed to distribute the atoms and computations over processors. In this implementation, atoms are partitioned based on geometrical positions and computational load by using unweighted or weighted recursive coordinate bisection. The experimental results reveal that taking computational load into account is essential. The performance of two iteration partitioning algorithms, atom decomposition and force decomposition, is also compared. A new irregular force decomposition algorithm is introduced and implemented. The CHAOS library is designed to facilitate parallelization of irregular applications. This library "1" couples partitioners to the application programs, "2" remaps data and partitions work among processors, and "3" optimizes interprocessor communications. This paper presents an application of CHAOS that can be used to support efficient execution of irregular problems on distributed memory machines.