Efficient path profiling

Ball, Thomas; Larus, James R.

doi:10.1109/micro.1996.566449

Cited by 323 publications

(426 citation statements)

References 10 publications

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“…The concept of profiling is well known [3,11]; it involves collecting statistics about the behavior of a program. Profiling is typically used to track control flow in an application -to find hot spots or compute code coverage.…”

Section: Profiling Traversalsmentioning

confidence: 99%

Automatic Prefetching by Traversal Profiling in Object Persistence Architectures

Ibrahim

Cook

2006

ECOOP 2006 – Object-Oriented Programming

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Abstract. Object persistence architectures support transparent access to persistent objects. For efficiency, many of these architectures support queries that can prefetch associated objects as part of the query result. While specifying prefetch manually in a query can significantly improve performance, correct prefetch specifications are difficult to determine and maintain, especially in modular programs. Incorrect prefetching is difficult to detect, because prefetch is only an optimization hint. This paper presents AutoFetch, a technique for automatically generating prefetch specifications using traversal profiling in object persistence architectures. AutoFetch generates prefetch specifications based on previous executions of similar queries. In contrast to previous work, AutoFetch can fetch arbitrary traversal patterns and can execute the optimal number of queries. AutoFetch has been implemented as an extension of Hibernate. We demonstrate that AutoFetch improves performance of traversals in the OO7 benchmark and can automatically predict prefetches that are equivalent to hand-coded queries, while supporting more modular program designs.

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Section: Profiling Traversalsmentioning

confidence: 99%

Automatic Prefetching by Traversal Profiling in Object Persistence Architectures

Ibrahim

Cook

2006

ECOOP 2006 – Object-Oriented Programming

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“…The original Ball-Larus path profiling algorithm recorded the execution frequency of intraprocedural, acyclic paths [4]. The program was instrumented in such a way that each path would generate a unique identifier during program execution.…”

Section: Related Workmentioning

confidence: 99%

Online Subpath Profiling

Oren

Matias

Sagiv

2002

Lecture Notes in Computer Science

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Abstract. We present an efficient online subpath profiling algorithm, OSP, that reports hot subpaths executed by a program in a given run. The hot subpaths can start at arbitrary basic block boundaries, and their identification is important for code optimization; e.g., to locate program traces in which optimizations could be most fruitful, and to help programmers in identifying performance bottlenecks. The OSP algorithm is online in the sense that it reports at any point during execution the hot subpaths as observed so far. It has very low memory and runtime overheads, and exhibits high accuracy in reports for benchmarks such as JLex and FFT. These features make the OSP algorithm potentially attractive for use in just-in-time (JIT) optimizing compilers, in which profiling performance is crucial and it is useful to locate hot subpaths as early as possible. The OSP algorithm is based on an adaptive sampling technique that makes effective utilization of memory with small overhead. Both memory and runtime overheads can be controlled, and the OSP algorithm can therefore be used for arbitrarily large applications, realizing a tradeoff between report accuracy and performance. We have implemented a Java prototype of the OSP algorithm for Java programs. The implementation was tested on programs from the Java Grande benchmark suite and exhibited a low average runtime overhead.

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“…where prob(v) is the probability of occurrence of the value v, TestCost(r, v) denotes the cost of testing whether a register r has a value v, 3 and φ is an empirically chosen threshold. This means that we will choose program point p and and register r for optimization if the benefit is high enough.…”

Section: A Cost Model For Value-profile-based Code Specializationmentioning

confidence: 99%

“…There has been a great deal of work on profiling techniques [2,3,13] and profileguided code optimization [6,7,17]. However, to the best of our knowledge this is the first work that aims to make the profiler goal-directed, i.e., aware of the potential utility of the profiling information that it is gathering, and able to modify its profiling actions based on this awareness, in order to reduce profiling overheads.…”

Section: Introductionmentioning

confidence: 99%

Goal-Directed Value Profiling

Watterson

Debray

2001

Lecture Notes in Computer Science

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Abstract. Compilers can exploit knowledge that a variable has a fixed known value at a program point for optimizations such as code specialization and constant folding. Recent work has shown that it is possible to take advantage of such optimizations, and thereby obtain significant performance improvements, even if a variable cannot be statically guaranteed to have a fixed constant value. To do this profitably, however, it is necessary to take into account information about the runtime distribution of values taken on by variables. This information can be obtained though value profiling. Unfortunately, existing approaches to value profiling incur high overheads, primarily because profiling is carried out without consideration for the way in which the resulting information will be used. In this paper, we describe an approach to reduce the cost of value profiling by making the value profiler aware of the utility of the value profiles being gathered. This allows our profiler to avoid wasting resources where the profile can be guaranteed to not be useful for optimization. This results in significant reductions in both the time and space requirements for value profiling. Our approach, implemented in the context of the alto link-time optimizer, is an order of magnitude faster, and uses about 5% of the space, of a straightforward implementation.

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Efficient path profiling

Abstract: Abstract

Cited by 323 publications

References 10 publications

Automatic Prefetching by Traversal Profiling in Object Persistence Architectures

Automatic Prefetching by Traversal Profiling in Object Persistence Architectures

Online Subpath Profiling

Goal-Directed Value Profiling

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