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Reactive NUCA: near-optimal block placement and replication in distributed caches
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International Symposium on Computer Architecture archive
Proceedings of the 36th annual international symposium on Computer architecture table of contents
Austin, TX, USA
SESSION: Cache organization table of contents
Pages 184-195  
Year of Publication: 2009
ISBN:978-1-60558-526-0
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Authors
Nikos Hardavellas  Carnegie Mellon University, Pittsburgh, PA, USA
Michael Ferdman  Carnegie Mellon University and Ecole Polytechnique Fédérale de Lausanne (EPFL), Pittsburgh, PA, USA
Babak Falsafi  Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Anastasia Ailamaki  Ecole Polytechnique Fédérale de Lausanne (EPFL) and Carnegie Mellon University, Lausanne, Switzerland
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Increases in on-chip communication delay and the large working sets of server and scientific workloads complicate the design of the on-chip last-level cache for multicore processors. The large working sets favor a shared cache design that maximizes the aggregate cache capacity and minimizes off-chip memory requests. At the same time, the growing on-chip communication delay favors core-private caches that replicate data to minimize delays on global wires. Recent hybrid proposals offer lower average latency than conventional designs, but they address the placement requirements of only a subset of the data accessed by the application, require complex lookup and coherence mechanisms that increase latency, or fail to scale to high core counts.

In this work, we observe that the cache access patterns of a range of server and scientific workloads can be classified into distinct classes, where each class is amenable to different block placement policies. Based on this observation, we propose Reactive NUCA (R-NUCA), a distributed cache design which reacts to the class of each cache access and places blocks at the appropriate location in the cache. R-NUCA cooperates with the operating system to support intelligent placement, migration, and replication without the overhead of an explicit coherence mechanism for the on-chip last-level cache. In a range of server, scientific, and multiprogrammed workloads, R-NUCA matches the performance of the best cache design for each workload, improving performance by 14% on average over competing designs and by 32% at best, while achieving performance within 5% of an ideal cache design.


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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Collaborative Colleagues:
Nikos Hardavellas: colleagues
Michael Ferdman: colleagues
Babak Falsafi: colleagues
Anastasia Ailamaki: colleagues