Value profiling

Calder, Brad; Feller, Peter; Eustace, Alan

doi:10.1109/micro.1997.645816

Cited by 107 publications

(101 citation statements)

References 9 publications

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“…al note that instructions other than loads may be highly invariant [4]. For example, consider the following code:…”

Section: Goal-directed Value Profilingmentioning

confidence: 99%

“…We then compute our threshold by subtracting this minimum count from the execution frequency of p. We know that if the count of the other field ever exceeds this threshold, then the optimizer can not select this value for specialization (since count(v) can not be high enough) 4 . This means, that if the other threshold is exceeded, we should stop profiling this point, since it will no longer be selected for optimization.…”

Section: Dynamic Elimination Of Unprofitable Candidatesmentioning

confidence: 99%

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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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“…al note that instructions other than loads may be highly invariant [4]. For example, consider the following code:…”

Section: Goal-directed Value Profilingmentioning

confidence: 99%

Section: Dynamic Elimination Of Unprofitable Candidatesmentioning

confidence: 99%

Goal-Directed Value Profiling

Watterson

Debray

2001

Lecture Notes in Computer Science

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“…The approaches in [4][9] use profiling to detect frequent loops to map to a special address region that an architecture would then map to a small low-power loop cache, while the approach in [12] compresses those regions to reduce memory traffic and hence power. The approach in [6] profiles values of variables or subroutine parameters to detect pseudo-constants that can aid a compiler in optimizing for performance, or even for reduced energy [7].…”

Section: Introductionmentioning

confidence: 99%

A fast on-chip profiler memory

Lysecky

Cotterell

Vahid

2002

Proceedings 2002 Design Automation Conference (IEEE Cat. No.02CH37324)

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Profiling an application executing on a microprocessor is part of the solution to numerous software and hardware optimization and design automation problems. Most current profiling techniques suffer from runtime overhead, inaccuracy, or slowness, and the traditional non-intrusive method of using a logic analyzer doesn't work for today's system-on-a-chip having embedded cores. We introduce a novel on-chip memory architecture that overcomes these limitations. The architecture, which we call ProMem, is based on a pipelined binary tree structure. It achieves single-cycle throughput, so it can keep up with today's fastest pipelined processors. It can also be laid out efficiently and scales very well, becoming more efficient the larger it gets. The memory can be used in a wide-variety of common profiling situations, such as instruction profiling, value profiling, and network traffic profiling, which in turn can be used to guide numerous design automation tasks.

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“…A similar situation holds in partial evaluation, where a variable has to be static in order to permit specialization [15]. In practice, it is often the case that an expression at a point in a program "almost always" takes on a particular value [6]. As an example, in the SPEC-95 benchmark perl, the function memmove is called close to 24 million times.…”

Section: Introductionmentioning

confidence: 99%

Code Specialization Based on Value Profiles

2000

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Abstract. It is often the case at runtime that variables and registers in programs are "quasi-invariant," i.e., the distribution of the values they take on is very skewed, with a small number of values occurring most of the time. Knowledge of such frequently occurring values can be exploited by a compiler to generate code that optimizes for the common cases without sacrificing the ability to handle the general case. The idea can be generalized to the notion of expression profiles, which profile the runtime values of arbitrary expressions and can permit optimizations that may not be possible using simple value profiles. Since this involves the introduction of runtime tests, a careful cost-benefit analysis is necessary to make sure that the benefits from executing the code specialized for the common values outweigh the cost of testing for these values. This paper describes a static cost-benefit analysis that allows us to discover when such specialization is profitable. Experimental results, using such an analysis and an implementation of low-level code specialization based on value and expression profiles within a link-time code optimizer, are given to validate our approach.

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Value profiling

Cited by 107 publications

References 9 publications

Goal-Directed Value Profiling

Goal-Directed Value Profiling

A fast on-chip profiler memory

Code Specialization Based on Value Profiles

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