AIFM: High-Performance, Application-Integrated Far Memory
Reading group: Jana Toljaga presented "AIFM: High-Performance, Application-Integrated Far Memory" (OSDI'20) at 1D19 the 14/10/2022 at 10h30.
Abstract
Memory is the most contended and least elastic resource in datacenter servers today. Applications can use only local memory—which may be scarce—even though memory might be readily available on another server. This leads to unnecessary killings of workloads under memory pressure and reduces effective server utilization.
We present application-integrated far memory (AIFM), which makes remote, “far” memory available to applications through a simple API and with high performance. AIFM achieves the same common-case access latency for far memory as for local RAM; it avoids read and write amplification that paging-based approaches suffer; it allows data structure engineers to build remoteable, hybrid near/far memory data structures; and it makes far memory transparent and easy to use for application developers.
Our key insight is that exposing application-level semantics to a high-performance runtime makes efficient remoteable memory possible. Developers use AIFM’s APIs to make allocations remoteable, and AIFM’s runtime handles swapping objects in and out, prefetching, and memory evacuation.
We evaluate AIFM with a prototypical web application frontend, a NYC taxi data analytics workload, a memcached-like key-value cache, and Snappy compression. Adding AIFM remoteable memory to these applications increases their available memory without performance penalty. AIFM outperforms Fastswap, a state-of-the-art kernel-integrated, paging-based far memory system by up to 61×.
