Far-memory techniques that enable applications to use remote memory are increasingly appealing in modern datacenters, supporting applications’ large memory footprint and improving machines’ resource utilization. Unfortunately, most far-memory techniques focus on OS-level optimizations and are agnostic to managed runtimes and garbage collections (GC) underneath applications written in high-level languages. With different object-access patterns from applications, GC can severely interfere with existing far-memory techniques, breaking prefetching algorithms and causing severe local-memory misses.
We developed MemLiner, a runtime technique that improves the performance of far-memory systems by “lining up” memory accesses from the application and the GC so that they follow similar memory access paths, thereby (1)reducing the local-memory working set and (2) improving remote-memory prefetching through simplified memory access patterns. We implemented MemLiner in two widely-used GCs in OpenJDK: G1 and Shenandoah. Our evaluation with a range of widely-deployed cloud systems shows MemLiner improves applications’ end-to-end performance by up to 2.5x.
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FAM-Graph: Graph Analytics on Disaggregated Memory
Disaggregated memory is being proposed as a way to provide efficient memory scaling for data intensive applications. High performance interconnect technologies, such as CXL, make disaggregated, fabric-attached-memory (FAM) a viable secondary tier of memory. Previous work on remote memory relies on extending kernel level paging to utilize FAM as an additional storage tier after local memory. These approaches have the advantage of exposing remote memory in application transparent ways that do not require code changes, but they incur large overheads due to the mismatch between the abstraction of a flat virtual address space and the reality of the tiered nature of FAM. In this paper, we present an alternative approach to remote memory based on application-specific objects. We design FAM-Graph - a semi-external graph processing system that leverages application-level properties, such as read only edge data, to efficiently tier data between local and remote memory, and prefetch remote data for local computation. Using several graph algorithms and datasets, we demonstrate that FAM-Graph achieves end-to-end performance within factors of 1–6× of Galois, the state of the art shared memory graph processing system, while using up to 20× less local memory. When Galois is used in conjunction with an OS-level FAM solution, we show that FAM-Graph achieves better end-to-end performance by up to 9× when both systems are configured with the same amount of local memory.
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- Award ID(s):
- 2016701
- NSF-PAR ID:
- 10469910
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-6654-8106-9
- Page Range / eLocation ID:
- 81 to 92
- Format(s):
- Medium: X
- Location:
- Lyon, France
- Sponsoring Org:
- National Science Foundation
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