L. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Transactions on Parallel and Distributed Systems, 1(1):35--47, January 1990. Home/Search Document Not in Database Summary Related Articles Check |
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Pointer Analysis: Haven't We Solved This Problem Yet? - Hind (2001) (26 citations) (Correct)
....is the incorporation of client analysis information into procedure level pointer alias summaries [81] Clientdriven pointer analyses may also be able to selectively apply more aggressive approaches to obtaining precision, such as those of [68] 4. 6 Heap Modeling Shape analysis algorithms [53, 43, 9, 19, 36, 35, 18, 74, 28, 29, 87, 88, 89, 103, 21, 54] have demonstrated high precision, over schemes that name objects based on allocation site (as in [47, 44, 83, 43, 9] but their scalability to even medium programs is uncertain. Mooly Sagiv summarizes the current state of the eld: Although I believe we are making good progress, in all honesty, ....
L. J. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Transactions on Parallel and Distributed Systems, 1(1):35-47, Jan. 1990.
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....pools. We use an analysis described in Section 2 to identify logically disjoint data structures, and use it to compute a representation we call the Disjoint Data Structure Graph. Our analysis to construct these graphs is similar to previous work on heap connection analysis and shape analysis [18, 17, 14, 30, 22], but differs from that work in a few key ways (discussed in more detail in Section 6) We perform the data structure analysis and subsequent transformations entirely at link time, using a compilation framework called LLVM, described briefly in subsection 1.1. Link time is an appropriate place for ....
.... updates of those two structures can be performed in parallel (if computations on one structure do not use values from the other) This is a different style of parallelism than that extracted by other parallelization strategies for nonarray based programs [20] and for tree like data structures [17]. Those strategies focus on data parallelism within computations on the same data structure or set of structures, whereas we would obtain parallelism between computations on different structures. This might only yield small degrees of parallelism, but such parallelism may in fact be well suited to ....
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L. J. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Transactions on Parallel and Distributed System, pages 35--47, 1990.
Interprocedural Shape Analysis - Rinetzky (Correct)
....Such an approach is taken in [CRL99, WL95] for a points to analysis. A Points to analysis nds aliasing between pointer variables. However the relation between heap allocated objects is not tracked. Besides shape analysis there are other methods to handle programs with pointer including [HN90, GH96, Deu94, CBC93]. These methods are incomparable to our method. The power of shape analysis stems from the ability to handle arbitrary programs and to conduct strong updates, even for example, when the program manipulates a cyclic list. The power Deutsch s method is the ability to precisely handle recursive ....
L. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Trans. on Par. and Dist. Syst., 1(1):35-47, January 1990.
Interprocedural Shape Analysis - Rinetzky (Correct)
....analyzes a program to determine information about the heap allocated data structures that the program manipulates. The analysis algorithm is conservative, i.e. the discovered information is true for every input. The information can be used to understand, verify, optimize [GH98] or parallelize [LH88, Hen90, AW93, PCK93, Zap99] programs. For example, it can be utilized to check at compile time for the absence of certain types of memory management errors, such as memory leakage or dereference of null pointers [DRS98, DRS00] In the past two decades, many shape analysis algorithms have been developed [JM81, JM82, LH88, ....
....of recursive calls, di erent incarnations of a local variable may point to the same heap cell. The ability to have distinctions between invisible instances of variables based on their local properties is the reason for the di erence in precision between our method and the methods described in [LH88, Hen90, LH88, CWZ90, AW93, GH96, SRW98]. In Chapter 4, we also exploit properties that capture relationships between the stack and the heap. In many cases, the ability to have these distinctions also leads to a more ecient analysis. Our algorithm was developed within the parametric framework described in [SRW99, LAS00] That framework ....
L. Hendren. Parallelizing Programs with Recursive Data Structures. PhD thesis, Cornell Univ., Ithaca, NY, Jan 1990.
Interprocedural Shape Analysis for Recursive Programs - Rinetzky, Sagiv (2001) (3 citations) (Correct)
....analyzes a program to determine information about the heap allocated data structures that the program manipulates. The analysis algorithm is conservative, i.e. the discovered information is true for every input. The information can be used to understand, verify, optimize [6] or parallelize [1, 8, 12] programs. For example, it can be utilized to check at compile time for the absence of certain types of memory management errors, such as memory leakage or dereference of null pointers [5] This paper addresses the problem of shape analysis in the presence of recursive procedures. This problem is ....
....of recursive calls, di erent incarnations of a local variable may point to the same heap cell. The ability to have distinctions between invisible instances of variables based on their local properties is the reason for the di erence in precision between our method and the methods described in [1, 2, 7, 8, 12, 14]. In Sect. 4, we also exploit properties that capture relationships between the stack and the heap. In many cases, the ability to have these distinctions also leads to a more ecient analysis. Technically, these properties and the analysis algorithm itself are explained (and implemented) using the ....
L. Hendren. Parallelizing Programs with Recursive Data Structures. PhD thesis, Cornell Univ., Ithaca, NY, Jan 1990.
Context-Sensitive Interprocedural Points-to Analysis in.. - Emami, Ghiya, Hendren (1994) (220 citations) Self-citation (Hendren) (Correct)
....location called heap in our abstract stack for the points to analysis. All heap directed pointers point to this location. We have designed a separate family of abstractions to capture meaningful relationships between these heapdirected pointers [16] based on the path matrix model proposed in [23]. Both the original path matrix analysis [23] and the heap based method proposed by Chase et al. 6] also assume that pointer fields in heap nodes only point to heap nodes, and do not point to variables (which are locations on stack) It is important to note that our points to method provides a ....
....for the points to analysis. All heap directed pointers point to this location. We have designed a separate family of abstractions to capture meaningful relationships between these heapdirected pointers [16] based on the path matrix model proposed in [23] Both the original path matrix analysis [23] and the heap based method proposed by Chase et al. 6] also assume that pointer fields in heap nodes only point to heap nodes, and do not point to variables (which are locations on stack) It is important to note that our points to method provides a safe approximation even in the presence of ....
L. J. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Transactions on Parallel and Distributed Systems, 1(1):35-- 47, January 1990.
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L. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Transactions on Parallel and Distributed Systems, 1(1):35--47, January 1990.
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L. J. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Transactions on Parallel and Distributed Systems, 1(1):35--47, January 1990. 37
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L. Hendren and A. Nicolau. Parallelizing programs with recursive data structures. IEEE Trans. on Par. and Dist. Syst., 1(1):35-47, January 1990.
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Hendren, L. Parallelizing programs with recursive data structures. In IEEE transactions on Parallel and Distributed Processing, volume 1, pages 35-47. January 1990.
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