Competitive prefetching for concurrent sequential I/O

Li, Chuanpeng; Shen, Kai; Papathanasiou, Athanasios

doi:10.1145/1272996.1273017

Cited by 43 publications

(23 citation statements)

References 20 publications

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“…The intention of such change was likely to reduce wasted prefetching on unneeded data. However, it might be a wrong tradeoff given that the existing I/O prefetching depth is already far below a balanced level [8]. Anomaly cause #5 (new), afflicting 2.6.23: The fourth highest ranked metric in Figure 11(E), the device-level I/O request size, was helpful.…”

Section: Discovered Anomaly Causesmentioning

confidence: 99%

Reference-driven performance anomaly identification

Shen

Stewart

et al. 2009

Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems

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Complex system software allows a variety of execution conditions on system configurations and workload properties. This paper explores a principled use of reference executions-those of similar execution conditions from the target-to help identify the symptoms and causes of performance anomalies. First, to identify anomaly symptoms, we construct change profiles that probabilistically characterize expected performance deviations between target and reference executions. By synthesizing several single-parameter change profiles, we can scalably identify anomalous reference-totarget changes in a complex system with multiple execution parameters. Second, to narrow the scope of anomaly root cause analysis, we filter anomaly-related low-level system metrics as those that manifest very differently between target and reference executions. Our anomaly identification approach requires little expert knowledge or detailed models on system internals and consequently it can be easily deployed. Using empirical case studies on the Linux I/O subsystem and a J2EE-based distributed online service, we demonstrate our approach's effectiveness in identifying performance anomalies over a wide range of execution conditions as well as multiple system software versions. In particular, we discovered five previously unknown performance anomaly causes in the Linux 2.6.23 kernel. Additionally, our preliminary results suggest that online anomaly detection and system reconfiguration may help evade performance anomalies in complex online systems.

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Section: Discovered Anomaly Causesmentioning

confidence: 99%

Reference-driven performance anomaly identification

Shen

Stewart

et al. 2009

Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems

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“…I/O prefetching is also a very effective way of improving I/O performance [33,1,7,41,21,13]. Mowry et al [33] used compilerguided information to manage prefetch commands more effectively.…”

Section: Related Workmentioning

confidence: 99%

“…Li and Shen proposed a memory management scheme that handles non-accessed but prefetched pages separately from the rest of the memory buffer cache [21]. More recent studies to improve conventional I/O prefetching using additional file and access history information include Diskseen [41], Competitive Prefetching [7] and AMP [15]. In comparison to these studies, our work targets multiple-CPU execution scenarios.…”

Section: Related Workmentioning

confidence: 99%

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Profiler and compiler assisted adaptive I/O prefetching for shared storage caches

Son

Muralidhara

Öztürk

et al. 2008

Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques

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I/O prefetching has been employed in the past as one of the mechanisms to hide large disk latencies. However, I/O prefetching in parallel applications is problematic when multiple CPUs share the same set of disks due to the possibility that prefetches from different CPUs can interact on shared memory caches in the I/O nodes in complex and unpredictable ways. In this paper, we (i) quantify the impact of compiler-directed I/O prefetching -developed originally in the context of sequential execution -on shared caches at I/O nodes. The experimental data collected shows that while I/O prefetching brings benefits, its effectiveness reduces significantly as the number of CPUs is increased; (ii) identify inter-CPU misses due to harmful prefetches as one of the main sources for this reduction in performance with the increased number of CPUs; and (iii) propose and experimentally evaluate a profiler and compiler assisted adaptive I/O prefetching scheme targeting shared storage caches. The proposed scheme obtains inter-thread data sharing information using profiling and, based on the captured data sharing patterns, divides the threads into clusters and assigns a separate (customized) I/O prefetcher thread for each cluster. In our approach, the compiler generates the I/O prefetching threads automatically. We implemented this new I/O prefetching scheme using a compiler and the PVFS file system running on Linux, and the empirical data collected clearly underline the importance of adapting I/O prefetching based on program phases. Specifically, our proposed scheme improves performance, on average, by 19.9%, 11.9% and 10.3% over the cases without I/O prefetching, with independent I/O prefetching (each CPU is performing compiler-directed I/O prefetching independently), and with one CPU prefetching (one CPU is reserved for prefetching on behalf of others), respectively, when 8 CPUs are used.

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“…To increase the prediction accuracy, previous kernel prefetching techniques have generally focused on taking advantage of sequential access patterns that are relatively easy to detect and quite effective [1]- [3]. However, it is observed that there are two major problems of these kernel prefetching techniques.…”

Section: Introductionmentioning

confidence: 99%

RPP: Reference Pattern Based Kernel Prefetching Controller

Lee

Doh

Kim

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYConventional kernel prefetching schemes have focused on taking advantage of sequential access patterns that are easy to detect. However, it is observed that, on random and even sequential references, they may cause performance degradation due to inaccurate pattern prediction and overshooting. To address these problems, we propose a novel approach to work with existing kernel prefetching schemes, called Reference Pattern based kernel Prefetching (RPP). The RPP can reduce negative effects of existing schemes by identifying one more reference pattern, i.e., looping, in addition to random and sequential patterns and delaying starting prefetching until patterns are confirmed to be sequential or looping.

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Competitive prefetching for concurrent sequential I/O

Cited by 43 publications

References 20 publications

Reference-driven performance anomaly identification

Reference-driven performance anomaly identification

Profiler and compiler assisted adaptive I/O prefetching for shared storage caches

RPP: Reference Pattern Based Kernel Prefetching Controller

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