DELTA: Distributed Locality-Aware Cache Partitioning for Tile-based Chip Multiprocessors
Paper i proceeding, 2020

Cache partitioning in tile-based CMP architectures is a challenging problem because of i) the need to determine capacity allocations with low computational overhead and ii) the need to place allocations close to where they are used, in order to reduce access latency. Although, previous solutions have addressed the problem of reducing the computational overhead and incorporating locality-awareness, they suffer from the overheads of centrally determining allocations.In this paper, we propose DELTA, a novel distributed and locality-aware cache partitioning solution which works by exchanging asynchronous challenges among cores. The distributed nature of the algorithm coupled with the low computational complexity allows for frequent reconfigurations at negligible cost and for the scheme to be implemented directly in hardware. The allocation algorithm is supported by an enforcement mechanism which enables locality-aware placement of data. We evaluate DELTA on 16-and 64-core tiled CMPs with multi-programmed workloads. Our evaluation shows that DELTA improves performance by 9% and 16%, respectively, on average, compared to an unpartitioned shared last-level cache.

multicore architectures

performance isolation

cache partitioning

Författare

Nadja Holtryd

Chalmers, Data- och informationsteknik, Datorteknik

Madhavan Manivannan

Chalmers, Data- och informationsteknik, Datorteknik

Per Stenström

Chalmers, Data- och informationsteknik, Datorteknik

Miquel Pericas

Chalmers, Data- och informationsteknik, Datorteknik

Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium, IPDPS 2020

578-589 9139842
978-172816876-0 (ISBN)

34th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2020
New Orleans, USA,

Meeting Challenges in Computer Architecture (MECCA)

Europeiska kommissionen (EU) (EC/FP7/340328), 2014-02-01 -- 2019-01-31.

Ämneskategorier

Datorteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/IPDPS47924.2020.00066

Mer information

Senast uppdaterat

2024-01-03