Online Phase-Adaptive Data Layout Selection

Zhang, Chengliang; Hirzel, Martin

doi:10.1007/978-3-540-70592-5_14

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…For LRU cache, reuse distance has been used by virtual machine systems to estimate the working set size [32] and to group simultaneously used objects [35]. There have been much research in operating systems to improve beyond LRU.…”

Section: Related Workmentioning

confidence: 99%

A generalized theory of collaborative caching

Ding

2012

Proceedings of the 2012 International Symposium on Memory Management

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Collaborative caching allows software to use hints to influence cache management in hardware. Previous theories have shown that such hints observe the inclusion property and can obtain optimal caching if the access sequence and the cache size are known ahead of time. Previously, the interface of a cache hint is limited, e.g., a binary choice between LRU and MRU.In this paper, we generalize the hint interface, where a hint is a number encoding a priority. We show the generality in a hierarchical relation where collaborative caching subsumes noncollaborative caching, and within collaborative caching, the priority hint subsumes the previous binary hint. We show two theoretical results for the general hint. The first is a new cache replacement policy, priority LRU, which permits the complete range of choices between MRU and LRU. We prove a new type of inclusion property-non-uniform inclusion-and give a one-pass algorithm to compute the miss rate for all cache sizes. Second, we show that priority hints can enable the use of the same hints to obtain optimal caching for all cache sizes, without having to know the cache size beforehand.

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Section: Related Workmentioning

confidence: 99%

A generalized theory of collaborative caching

Ding

2012

Proceedings of the 2012 International Symposium on Memory Management

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“…We note that, in general, the application of machine learning to Java runtime performance auto-tuning is a growing trend [26,39].…”

Section: Related Workmentioning

confidence: 99%

Garbage collection auto-tuning for Java mapreduce on multi-cores

Singer

Kovoor

Brown

et al. 2011

Proceedings of the International Symposium on Memory Management

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MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a fourcore, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management autotuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests.

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“…Finally, runtime behavior analysis [4, 8,24,31,46] in dynamic optimizers (e.g., the runtime systems of Java and C#), overcomes the limitations of static and profiling techniques by sampling and analyzing program executions on the fly. It has good adaptivity-being able to adapt to the changes in running environments and program inputs.…”

Section: Implications To Program Optimizationsmentioning

confidence: 99%

“…Some of them exploit runtime invariants through programmers' annotations or other efforts; examples include 'C from Kaashoek's group [32], Tempo from Consel's group [29], and DyC from Chambers and Eggers' group [15]. Others monitor execution through runtime profiling to optimize a program; examples include the dynamic feedback work by Diniz and Rinard [12], the continuous program optimizations by Kistler and Franz [22], the ADAPT project by Voss and Eigenmann [41], the CoCo project by Childers, Davidson and Soffa [9], the continuous program optimization (CPO) project by Wisniewski and his colleagues [44], the dynamic optimizations on LLVM [23], the runtime support for managed languages like Java and C# [4,8,24,31,39,46]. These techniques observe runtime behaviors directly, and typically employ reactive optimization scheme.…”

Section: Related Workmentioning

confidence: 99%

An input-centric paradigm for program dynamic optimizations

et al. 2010

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Accurately predicting program behaviors (e.g., locality, dependency, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited program inputs, a deciding factor for program behaviors.Triggered by the strong and predictive correlations between program inputs and behaviors that recent studies have uncovered, this work proposes to include program inputs into the focus of program behavior analysis, cultivating a new paradigm named input-centric program behavior analysis. This new approach consists of three components, forming a three-layer pyramid. At the base is program input characterization, a component for resolving the complexity in program raw inputs and the extraction of important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program's execution as soon as the execution starts. The top layer of the pyramid is input-centric adaptation, which capitalizes on the novel opportunities that the first two components create to facilitate proactive adaptation for program optimizations.By centering on program inputs, the new approach resolves a proactivity-adaptivity dilemma inherent in previous techniques. Its benefits are demonstrated through proactive dynamic optimizations and version selection, yielding significant performance improvement on a set of Java and C programs.

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Online Phase-Adaptive Data Layout Selection

Cited by 14 publications

References 40 publications

A generalized theory of collaborative caching

A generalized theory of collaborative caching

Garbage collection auto-tuning for Java mapreduce on multi-cores

An input-centric paradigm for program dynamic optimizations

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