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Large-memory nodes for energy efficient high-performance computing

機(jī)譯：用于節(jié)能高性能計(jì)算的大內(nèi)存節(jié)點(diǎn)

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Energy consumption is by far the most important contributor to HPC cluster operational costs, and it accounts for a significant share of the total cost of ownership. Advanced energy-saving techniques in HPC components have received significant research and development effort, but a simple measure that can dramatically reduce energy consumption is often overlooked. We show that, in capacity computing, where many small to medium-sized jobs have to be solved at the lowest cost, a practical energy-saving approach is to scale-in the application on large-memory nodes. We evaluate scaling-in; i.e. decreasing the number of application processes and compute nodes (servers) to solve a fixed-sized problem, using a set of HPC applications running in a production system. Using standard-memory nodes, we obtain average energy savings of 36%, already a huge figure. We show that the main source of these energy savings is a decrease in the node-hours (node_hours = #nodes x exe_time), which is a consequence of the more efficient use of hardware resources.\udScaling-in is limited by the per-node memory capacity. We therefore consider using large-memory nodes to enable a greater degree of scaling-in. We show that the additional energy savings, of up to 52%, mean that in many cases the investment in upgrading the hardware would be recovered in a typical system lifetime of less than five years.
機(jī)譯：迄今為止，能源消耗是HPC集群運(yùn)營(yíng)成本的最重要因素，并且占總擁有成本的很大一部分。 HPC組件中的先進(jìn)節(jié)能技術(shù)已經(jīng)獲得了巨大的研究和開(kāi)發(fā)成果，但是通常會(huì)忽略一種可以顯著降低能耗的簡(jiǎn)單措施。我們表明，在容量計(jì)算中，必須以最低的成本解決許多中小型工作，一種實(shí)用的節(jié)能方法是在大型內(nèi)存節(jié)點(diǎn)上擴(kuò)展應(yīng)用程序。我們?cè)u(píng)估放大；即使用在生產(chǎn)系統(tǒng)中運(yùn)行的一組HPC應(yīng)用程序，減少應(yīng)用程序進(jìn)程和計(jì)算節(jié)點(diǎn)（服務(wù)器）的數(shù)量以解決固定大小的問(wèn)題。使用標(biāo)準(zhǔn)內(nèi)存節(jié)點(diǎn)，我們可以平均節(jié)省36％的能源，這已經(jīng)是一個(gè)巨大的數(shù)字了。我們表明，這些節(jié)能的主要來(lái)源是節(jié)點(diǎn)小時(shí)數(shù)的減少（node_hours = #nodes x exe_time），這是更有效地利用硬件資源的結(jié)果。\ udScaling-in受限于節(jié)點(diǎn)內(nèi)存容量。因此，我們考慮使用大內(nèi)存節(jié)點(diǎn)來(lái)實(shí)現(xiàn)更大程度的擴(kuò)展。我們表明，最多可節(jié)省52％的能源，這意味著在許多情況下，通常在不到五年的系統(tǒng)使用壽命內(nèi)就可以收回對(duì)硬件升級(jí)的投資。

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