3 ms·

I was curious too so I took a look. I think this is the relevant bit: Recent work [94] shows that “lazy promotion” and “quick demotion” are two important prope

I was curious too so I took a look. I think this is the relevant bit: Recent work [94] shows that “lazy promotion” and “quick demotion” are two important properties of efficient cache eviction algorithms. [...] Quick demotion removes most objects quickly after they are inserted. Many previous works have discussed this idea in the context of evicting pages from a scan [16,60,67,70,75,77]. Recent work also shows that not only storage workloads but web cache workloads also benefit from quick demotion [94] because object popularity follows a power-law distribution, and many objects are unpopular. [...] [94] Juncheng Yang, Ziyue Qiu, Yazhuo Zhang, Yao Yue, and K. V. Rashmi. FIFO can be better than LRU: the power of lazy promotion and quick demotion. In The 19th Workshop on Hot Topics in Operating Systems (HotOS 23), 2023.

kazinator 3y ago
I read that before. Caching is predicated on locality. We put things into a cache based on the idea that something accessed once is likely to be used again, due to locality. Lazy promotion is useful in a situation in which we don't trust that the access pattern exhibits locality, nor not fully. Perhaps it exhibits locality for some data, but is widely scattered for others. For instance, an algorithm is working with numerous local variables in a loop (good locality) but the loop is also stepping through a very large set of data in memory, much larger than the cache. Moreover, items in this set are visited only once (poor/nonexistent locality). If we regard accesses with a bit of suspicion ("this might not have good locality") we can avoid those latter items from overstaying in the cache. Items in the cache that are not going to be touched again effectively shrink the cache. Like if at any given time about half our cache has such items, the cache is effectively halved. With a deterministic, fixed distance between the Hand pointer and the Insert point, which we can tune, we could empirically adjust for this. For instance we could have it so that, consistently, every new item that is not visited within 4 subsequent cache misses will be evicted. If we had a magic oracle, we would not use LRU or any of these other approaches. We would ask the oracle questions "Great Oracle, given these items in the cache, and this new one which is not in the cache, which one(s) will not be seen again, or else which one(s) will be seen again farthest into the future?" That's really what we want to know, but since oracles don't exist, we use heuristics, like the idea that the longer an item goes without being accessed, the more likely it is that it's a never-again-accessed item.
- 1a1a11a 3y ago
  Great point! We tried the oracle (Belady MIN) and found that the oracle often does not admit new objects (<20% chance admitting new objects) in most traces. That's why we need to evict new objects quickly

kazinator 3y ago

I read that before. Caching is predicated on locality. We put things into a cache based on the idea that something accessed once is likely to be used again, due to locality. Lazy promotion is useful in a situation in which we don't trust that the access pattern exhibits locality, nor not fully. Perhaps it exhibits locality for some data, but is widely scattered for others. For instance, an algorithm is working with numerous local variables in a loop (good locality) but the loop is also stepping through a very large set of data in memory, much larger than the cache. Moreover, items in this set are visited only once (poor/nonexistent locality). If we regard accesses with a bit of suspicion ("this might not have good locality") we can avoid those latter items from overstaying in the cache. Items in the cache that are not going to be touched again effectively shrink the cache. Like if at any given time about half our cache has such items, the cache is effectively halved. With a deterministic, fixed distance between the Hand pointer and the Insert point, which we can tune, we could empirically adjust for this. For instance we could have it so that, consistently, every new item that is not visited within 4 subsequent cache misses will be evicted. If we had a magic oracle, we would not use LRU or any of these other approaches. We would ask the oracle questions "Great Oracle, given these items in the cache, and this new one which is not in the cache, which one(s) will not be seen again, or else which one(s) will be seen again farthest into the future?" That's really what we want to know, but since oracles don't exist, we use heuristics, like the idea that the longer an item goes without being accessed, the more likely it is that it's a never-again-accessed item.

1a1a11a 3y ago
Great point! We tried the oracle (Belady MIN) and found that the oracle often does not admit new objects (<20% chance admitting new objects) in most traces. That's why we need to evict new objects quickly