Junwhan Ahn
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An Imitation Learning Approach for Cache Replacement
Evan Z. Liu
International Conference on Machine Learning (2020)
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Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses. To increase cache hits, we focus on the problem of cache replacement: choosing which cache line to evict upon inserting a new line. This is challenging because it requires planning far ahead and currently there is no known practical solution. As a result, current replacement policies typically resort to heuristics designed for specific common access patterns, which fail on more diverse and complex access patterns. In contrast, we propose an imitation learning approach to automatically learn cache access patterns by leveraging Belady’s, an oracle policy that computes the optimal eviction decision given the future cache accesses. While directly applying Belady’s is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady’s even on diverse and complex access patterns, and call this approach PARROT. When evaluated on 13 of the most memory-intensive SPEC applications, PARROT increases cache miss rates by 20% over the current state of the art. In addition, on a large-scale web search benchmark, PARROT increases cache hit rates by 61% over a conventional LRU policy. We release a Gym environment to facilitate research in this area, as data is plentiful, and further advancements can have significant real-world impact.
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Software-defined far memory in warehouse-scale computers
Andres Lagar-Cavilla
Suleiman Souhlal
Neha Agarwal
Radoslaw Burny
Shakeel Butt
Junaid Shahid
Greg Thelen
Kamil Adam Yurtsever
Yu Zhao
International Conference on Architectural Support for Programming Languages and Operating Systems (2019)
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Increasing memory demand and slowdown in technology scaling pose important challenges to total cost of ownership (TCO) of warehouse-scale computers (WSCs). One promising idea to reduce the memory TCO is to add a cheaper, but slower, "far memory" tier and use it to store infrequently accessed (or cold) data. However, introducing a far memory tier brings new challenges around dynamically responding to workload diversity and churn, minimizing stranding of capacity, and addressing brownfield (legacy) deployments.
We present a novel software-defined approach to far memory that proactively compresses cold memory pages to effectively create a far memory tier in software. Our end-to-end system design encompasses new methods to define performance service-level objectives (SLOs), a mechanism to identify cold memory pages while meeting the SLO, and our implementation in the OS kernel and node agent. Additionally, we design learning-based autotuning to periodically adapt our design to fleet-wide changes without a human in the loop. Our system has been successfully deployed across Google's WSC since 2016, serving thousands of production services. Our software-defined far memory is significantly cheaper (67% or higher memory cost reduction) at relatively good access speeds (6 us) and allows us to store a significant fraction of infrequently accessed data (on average, 20%), translating to significant TCO savings at warehouse scale.
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