Dynamic Improvement of Locality in Virtual Memory Systems

Baier, J.-L.; Sager, Gary R.

doi:10.1109/tse.1976.233801

Cited by 59 publications

(22 citation statements)

References 10 publications

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“…In this section, we will focus on Miss Correlating Prefetchers (MCPs) [3], the most effective of current hardware prefetchers applicable to generalized memory access patterns. Prefetching relies on two key mechanisms to successfully fetch and place data prior to a processor reference: (1) an accurate memory address predictor to predict "which" data to prefetch, and (2) and an accurate predictor of "when" to prefetch the data. MCPs rely on correlating cache miss addresses to predict both which data to prefetch and when to prefetch it.…”

Section: Miss Correlating Prefetchersmentioning

confidence: 99%

“…Figure 6 (bars labeled "A") presents the prediction accuracy and coverage of a DBP for a 32K L1 direct-mapped cache. The graphs plot the fraction of correct dead-block predictions (hits), the fraction of incorrect predictions 1. We evaluated DBP's and DBCP's prediction accuracy/coverage for all of SPEC95 and those SPEC2K benchmarks compiled for SimpleScalar.…”

Section: Lookahead Opportunitymentioning

confidence: 99%

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Dead-block prediction & dead-block correlating prefetchers

Lai

Fide

Falsafi

2001

Proceedings of the 28th Annual International Symposium on Computer Architecture - ISCA '01

245

176

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Section: Miss Correlating Prefetchersmentioning

confidence: 99%

Section: Lookahead Opportunitymentioning

confidence: 99%

Dead-block prediction & dead-block correlating prefetchers

Lai

Fide

Falsafi

2001

Proceedings of the 28th Annual International Symposium on Computer Architecture - ISCA '01

245

176

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show abstract

“…A widely used predictor in systems similar to ours is based on Markov chain theory [2,23]. The main idea is that given a string …”

Section: Prefetchingmentioning

confidence: 99%

“…Thus, the first copy will be assigned the first interval and labeled (0, 1) while the second copy will be assigned the second interval and labeled (2,3). It is important to notice that the number of additional tuples to be inserted in the HIER table depends on the ordering of the nodes.…”

Section: Arbitrary Hierarchiesmentioning

confidence: 99%

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Scalable Visual Hierarchy Exploration

Stroe

Rundensteiner

Ward

2000

Lecture Notes in Computer Science

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More and more modern computer applications, from business decision support to scientific data analysis, utilize visualization techniques to support exploratory activities.Most visualization tools do not scale well with regard to the size of the dataset upon which they operate. Specifically, the level of cluttering on the screen is typically unacceptable and the performance is poor. To solve the problem of cluttering at the interface level, visualization tools have recently been extended to support hierarchical views of the data, with support for focusing and drilling-down using interactive brushes. To solve the scalability problem, this thesis investigates how best to couple such a visualization tool with a database management system without losing the real-time characteristic required in any interactive application.This integration must be done carefully, since visual user interactions implemented as main memory operations do not map directly into efficient database operations. In our context, the main efficiency issue is to avoid the recursive processing required for hierarchical data retrieval. For this problem, we have developed a tree labeling method, called MinMax tree, that allows the movement of the on-line recursive processing into an off-line precomputation step. Thus, at run time, the recursive processing operations translate into linear cost range queries. Secondly, we employ a main memory access strategy to support incremental loading of data into the main memory. To further reduce the response time in the system, we have designed a speculative non-pure prefetcher that brings data into memory when the system is idle. The techniques have been incorporated into XmdvTool, a multidimensional visual exploration tool, in order to achieve scalability.The tool efficiently scales up now to datasets of the order 10 5 10 7 records. Lastly, we report experimental results that illustrate the impact of the proposed techniques on the system's overall performance. AcknowledgementsMany thanks to my professors and to my friends for their support.

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Working set size reduction by restructuring APL workspaces

Kropp

Wrobel

1978

Information Systems Methodology

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APL Workspaces usually have a random distribution of the defined APL functions. The physical location of a defined APL function within an APL workspace depends on its history of creation~ modification~ or copying. Furthermore in APL a good programming style results in a large number of small functions. The execution of an APL program in a virtual storage environment usually leads to comparatively large working set sizes because the APL functions are scattered over the workspace. However~ the interrelation between the functions can be analysed and exploited to reorganize a workspace and thus reduce the working set sizes. Methods of restructuring an APL workspace are proposed. Results of an investigation on a large APL/CMS workspace using these methods are presented. (2) Hatfield, D. J.: Experiments on Page Size~ Program Access Patterns~ and Virtual Memory Performance. IBM Journal of Research and Development 16, i~ 58-66~ 1972.

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Dynamic Improvement of Locality in Virtual Memory Systems

Cited by 59 publications

References 10 publications

Dead-block prediction & dead-block correlating prefetchers

Dead-block prediction & dead-block correlating prefetchers

Scalable Visual Hierarchy Exploration

Working set size reduction by restructuring APL workspaces

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