NDLI: Amoeba-Cache: Adaptive Blocks for Eliminating Waste in the Memory Hierarchy

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Amoeba-Cache: Adaptive Blocks for Eliminating Waste in the Memory Hierarchy

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Amoeba-Cache: Adaptive Blocks for Eliminating Waste in the Memory Hierarchy

Content Provider	ACM Digital Library
Author	Shannon, Lesley Dwarkadas, Sandhya Shriraman, Arrvindh Zhao, Hongzhou Kumar, Snehasish Matthews, Eric
Abstract	The fixed geometries of current cache designs do not adapt to the working set requirements of modern applications, causing significant inefficiency. The short block lifetimes and moderate spatial locality exhibited by many applications result in only a few words in the block being touched prior to eviction. Unused words occupy between 17 -- 80% of a 64K L1 cache and between 1% -- 79% of a 1MB private LLC. This effectively shrinks the cache size, increases miss rate, and wastes on-chip bandwidth. Scaling limitations of wires mean that unused-word transfers comprise a large fraction (11%) of on-chip cache hierarchy energy consumption. We propose Amoeba-Cache, a design that supports a variable number of cache blocks, each of a different granularity. Amoeba-Cache employs a novel organization that completely eliminates the tag array, treating the storage array as uniform and morph able between tags and data. This enables the cache to harvest space from unused words in blocks for additional tag storage, thereby supporting a variable number of tags (and correspondingly, blocks). Amoeba-Cache adjusts individual cache line granularities according to the spatial locality in the application. It adapts to the appropriate granularity both for different data objects in an application as well as for different phases of access to the same data. Overall, compared to a fixed granularity cache, the Amoeba-Cache reduces miss rate on average (geometric mean) by 18% at the L1 level and by 18% at the L2 level and reduces L1 -- L2 miss bandwidth by ?46%. Correspondingly, Amoeba-Cache reduces on-chip memory hierarchy energy by as much as 36% (mcf) and improves performance by as much as 50% (art).
Starting Page	376
Ending Page	388
Page Count	13
File Format	PDF
ISBN	9780769549248
ISSN	10724451
DOI	10.1109/MICRO.2012.42
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-12-01
Access Restriction	Subscribed
Subject Keyword	Cache architecture energy efficiency adaptive granularity memory system amoeba-cache
Content Type	Text
Resource Type	Article

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