Can far memory improve job throughput? Article Swipe

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Emmanuel Amaro , Christopher Branner-Augmon , Zhihong Luo , Amy Ousterhout , Marcos K. Aguilera , Aurojit Panda , Sylvia Ratnasamy , Scott Shenker ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1145/3342195.3387522 · OA: W3021725346

As memory requirements grow, and advances in memory technology slow, the availability of sufficient main memory is increasingly the bottleneck in large compute clusters. One solution to this is memory disaggregation, where jobs can remotely access memory on other servers, or far memory. This paper first presents faster swapping mechanisms and a far memory-aware cluster scheduler that make it possible to support far memory at rack scale. Then, it examines the conditions under which this use of far memory can increase job throughput. We find that while far memory is not a panacea, for memory-intensive workloads it can provide performance improvements on the order of 10% or more even without changing the total amount of memory available.

Can far memory improve job throughput? Article Swipe

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