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Cache performance of operating system and multiprogramming workloads
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Volume 6 ,  Issue 4  (November 1988) table of contents
Pages: 393 - 431  
Year of Publication: 1988
ISSN:0734-2071
Authors
Anant Agarwal  Computer Systems Laboratory, Stanford, CA
John Hennessy  Computer Systems Laboratory, Stanford, CA
Mark Horowitz  Computer Systems Laboratory, Stanford, CA
Publisher
ACM  New York, NY, USA
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  67


REVIEW

"Wilfred J. Hansen : Reviewer"

Cache memory between main memory and the processor increases both performance and hardware cost. Analysis of the trade-offs for proposed hardware is difficult and is usually done by simulation driven from some imagined or measured workload. In t  more...

Collaborative Colleagues:
Anant Agarwal: colleagues
John Hennessy: colleagues
Mark Horowitz: colleagues