Using annotations to reduce dynamic optimization time

KrintzChandra,; CalderBrad,

doi:10.1145/381694.378831

Cited by 18 publications

(12 citation statements)

References 12 publications

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“…The availability of such structure can decrease the runtime monitoring overhead by restricting the monitoring process explicitly to specific code sections. A similar scheme is proposed in [10], where the java bytecode is annotated with structural information present in the bytecode itself and information obtained through off-line profiling. However, this methodology has two drawbacks: a part of the program structure is already lost during the generation of bytecode; and off-line profiling may not yield useful information pertaining to future program runs.…”

Section: Resultsmentioning

confidence: 99%

Runtime optimization utilizing program structure

Gorti

Somani

2012

2012 18th International Conference on Advanced Computing and Communications (ADCOM)

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Dynamic Binary Optimization and dynamic compilation based schemes are widely employed for runtime program optimization. They are effective in specific program scenarios, but their scope of applicability is limited by the associated runtime overhead. Their capabilities can be exploited well with the knowledge of program parameters and execution conditions. These high level structures affect the runtime optimization decisions, thereby reducing the overhead in rediscovering them. In the current program flow process, the binary executable files are devoid of this program high-level structure. Such structures exist at earlier stages, i.e., at higher level program in the compiler front end generated parse and attributed abstract syntax trees, and data structures generated in the compiler back end for optimization, i.e. a call graph. However, the information is discarded at the final code generation stage.In this paper, we develop a framework that automatically captures attributes of program structure, which are carried forward through static compilation. They are used to make the runtime optimization process faster and lightweight in nature. We also develop a novel Runtime Management Module (RMM) to control the program execution process and reoptimize the code to better suit the current execution conditions, as needed. Our metadata extraction techniques coupled with the effectiveness of RMM results into significant performance gains during execution of diverse benchmarks from SPEC and Splash 2 benchmark suites.

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Section: Resultsmentioning

confidence: 99%

Runtime optimization utilizing program structure

Gorti

Somani

2012

2012 18th International Conference on Advanced Computing and Communications (ADCOM)

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“…However, if attributes expected by a tool are absent from the class file, or are no longer valid because of instrumentation, the tool may function incorrectly. Annotations on bytecode were applied in research, for example, to pass information from a static compiler to JIT [18,30,2,13], or to store program analysis results [28].…”

Section: Annotationsmentioning

confidence: 99%

“…Calder [18], are used to reduce the overhead of dynamic compilation or to improve performance. Such annotations usually rely on intra-procedural analysis, probably augmented by hierarchy and inheritance information, since in dynamic languages, inter-procedural analyses are rarely reliable.…”

Section: Modementioning

confidence: 99%

“…Krintz and Calder [18] address the problem of class file corruption that causes invalid annotations, and distinguish between annotations that are safe to use (probably at the price of performance) even if invalid, and those that are unsafe and thus must be validated if used. They suggested using only annotations that do not affect program correctness or verifying the annotations at runtime.…”

Section: Related Workmentioning

confidence: 99%

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Instrumenting annotated programs

Biberstein

Sreedhar

Mendelson

et al. 2005

Proceedings of the 1st ACM/USENIX International Conference on Virtual Execution Environments

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Instrumentation is commonly used to track application behavior: to collect program profiles; to monitor component health and performance; to aid in component testing; and more. Program annotation enables developers and tools to pass extra information to later stages of software development and execution. For example, the .NET runtime relies on annotations for a significant chunk of the services it provides. Both mechanisms are evolving into important parts of software development in the context of modern platforms such as Java and .NET.Instrumentation tools are generally not aware of the semantics of information passed via the annotation mechanism. This is especially true for post-compiler, e.g., runtime, instrumentation. The problem is that instrumentation may affect the correctness of annotations, rendering them invalid or misleading, and producing unforeseen side-effects during program execution. This problem has not been addressed so far.In this paper, we show the subtle interaction that takes place between annotations and instrumentation using several real-life examples. Many annotations are intended to provide information for the runtime; the virtual environment is a prominent annotation consumer, and must be aware of this conflict. It may also be required to provide runtime support to other annotation consumers. We propose an annotation taxonomy and show how instrumentation affects various annotations that were used in research and in industrial applications. We show how the annotations can expose enough information about themselves to prevent the instrumentation from accidentally corrupting the annotations. We demonstrate this approach on our annotations benchmark.

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“…Our methodology is similar to off-line, profile-based optimization research in which a set of representative program inputs are used to collect profile information that is used to guide optimization [18,26,17]. Empirical experiments then use a different set of inputs to evaluate the efficacy of the technique.…”

Section: Nwslitementioning

confidence: 99%

NWSLite

Gurun

Krintz

Wolski

2004

Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services

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Computation off-loading, i.e., remote execution, has been shown to be effective for extending the computational power and battery life of resource-restricted devices, e.g., hand-held, wearable, and pervasive computers. Remote execution systems must predict the cost of executing both locally and remotely to determine when offloading will be most beneficial. These costs however, are dependent upon the execution behavior of the task being considered and the highly-variable performance of the underlying resources, e.g., CPU (local and remote), bandwidth, and network latency. As such, remote execution systems must employ sophisticated, prediction techniques that accurately guide computation off-loading. Moreover, these techniques must be efficient, i.e., they cannot consume significant resources, e.g., energy, execution time, etc., since they are performed on the mobile device.In this paper, we present NWSLite, a computationally efficient, highly accurate prediction utility for mobile devices. NWSLite is an extension to the Network Weather Service (NWS), a dynamic forecasting toolkit for adaptive scheduling of high-performance Computational Grid applications. We significantly scaled down the NWS to reduce its resource consumption yet still achieve accuracy that exceeds that of extant remote execution prediction methods. We empirically analyze and compare both the prediction accuracy and the cost of NWSLite and a number of different forecasting methods from existing remote execution systems. We evaluate the efficacy of the different methods using a wide range of mobile applications and resources.

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Using annotations to reduce dynamic optimization time

Abstract: Abstract

Cited by 18 publications

References 12 publications

Runtime optimization utilizing program structure

Runtime optimization utilizing program structure

Instrumenting annotated programs

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