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Smart Cache

About: Smart Cache is a research topic. Over the lifetime, 7680 publications have been published within this topic receiving 180618 citations.


Papers
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Proceedings ArticleDOI
01 Aug 2000
TL;DR: This paper focuses on the features of the M340 cache sub-system and illustrates the effect on power and performance through benchmark analysis and actual silicon measurements.
Abstract: Advances in technology have allowed portable electronic devices to become smaller and more complex, placing stringent power and performance requirements on the devices components. The M7CORE M3 architecture was developed specifically for these embedded applications. To address the growing need for longer battery life and higher performance, an 8-Kbyte, 4-way set-associative, unified (instruction and data) cache with pro-grammable features was added to the M3 core. These features allow the architecture to be optimized based on the applications requirements. In this paper, we focus on the features of the M340 cache sub-system and illustrate the effect on power and perfor-mance through benchmark analysis and actual silicon measure-ments.

253 citations

Journal ArticleDOI
TL;DR: This article presents the design, implementation, and performance of a file system that integrates application-controlled caching, prefetching, and disk scheduling and shows that this combination of techniques greatly improves the performance of the file system.
Abstract: As the performance gap between disks and micropocessors continues to increase, effective utilization of the file cache becomes increasingly immportant. Application-controlled file caching and prefetching can apply application-specific knowledge to improve file cache management. However, supporting application-controlled file caching and prefetching is nontrivial because caching and prefetching need to be integrated carefully, and the kernel needs to allocate cache blocks among processes appropriately. This article presents the design, implementation, and performance of a file system that integrates application-controlled caching, prefetching, and disk scheduling. We use a two-level cache management strategy. The kernel uses the LRU-SP (Least-Recently-Used with Swapping and Placeholders) policy to allocate blocks to processes, and each process integrates application-specific caching and prefetching based on the controlled-aggressive policy, an algorithm previously shown in a theoretical sense to be nearly optimal. Each process also improves its disk access latency by submittint its prefetches in batches so that the requests can be scheduled to optimize disk access performance. Our measurements show that this combination of techniques greatly improves the performance of the file system. We measured that the running time is reduced by 3% to 49% (average 26%) for single-process workloads and by 5% to 76% (average 32%) for multiprocess workloads.

249 citations

Journal ArticleDOI
TL;DR: A program tracing technique called ATUM (Address Tracing Using Microcode) is developed that captures realistic traces of multitasking workloads including the operating system that shows that both the operating System and multiprogramming activity significantly degrade cache performance, with an even greater proportional impact on large caches.
Abstract: Large caches are necessary in current high-performance computer systems to provide the required high memory bandwidth. Because a small decrease in cache performance can result in significant system performance degradation, accurately characterizing the performance of large caches is important. Although measurements on actual systems have shown that operating systems and multiprogramming can affect cache performance, previous studies have not focused on these effects. We have developed a program tracing technique called ATUM (Address Tracing Using Microcode) that captures realistic traces of multitasking workloads including the operating system. Examining cache behavior using these traces from a VAX processor shows that both the operating system and multiprogramming activity significantly degrade cache performance, with an even greater proportional impact on large caches. From a careful analysis of the causes of this degradation, we explore various techniques to reduce this loss. While seemingly little can be done to mitigate the effect of system references, multitasking cache miss activity can be substantially reduced with small hardware additions.

244 citations

Journal ArticleDOI
TL;DR: It is shown that cache profiling, using the CProf cache profiling system, improves program performance by focusing a programmer's attention on problematic code sections and providing insight into appropriate program transformations.
Abstract: A vital tool-box component, the CProf cache profiling system lets programmers identify hot spots by providing cache performance information at the source-line and data-structure level. Our purpose is to introduce a broad audience to cache performance profiling and tuning techniques. Although used sporadically in the supercomputer and multiprocessor communities, these techniques also have broad applicability to programs running on fast uniprocessor workstations. We show that cache profiling, using our CProf cache profiling system, improves program performance by focusing a programmer's attention on problematic code sections and providing insight into appropriate program transformations. >

242 citations

Proceedings ArticleDOI
01 Jun 1998
TL;DR: Chunk-based caching allows fine granularity caching, and allows queries to partially reuse the results of previous queries with which they overlap, and a new organization for relational tables, which is called a “chunked file” is proposed.
Abstract: Caching has been proposed (and implemented) by OLAP systems in order to reduce response times for multidimensional queries Previous work on such caching has considered table level caching and query level caching Table level caching is more suitable for static schemes On the other hand, query level caching can be used in dynamic schemes, but is too coarse for “large” query results Query level caching has the further drawback for small query results in that it is only effective when a new query is subsumed by a previously cached query In this paper, we propose caching small regions of the multidimensional space called “chunks” Chunk-based caching allows fine granularity caching, and allows queries to partially reuse the results of previous queries with which they overlap To facilitate the computation of chunks required by a query but missing from the cache, we propose a new organization for relational tables, which we call a “chunked file” Our experiments show that for workloads that exhibit query locality, chunked caching combined with the chunked file organization performs better than query level caching An unexpected benefit of the chunked file organization is that, due to its multidimensional clustering properties, it can significantly improve the performance of queries that “miss” the cache entirely as compared to traditional file organizations

238 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202350
2022114
20215
20201
20198
201818