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https://www.researchgate.net/publication/220847193_Memory_energy_management_for_an_enterprise_decision_support_system
In these servers, memory may consume 40% of the total system power. Different memory configurations (sizes, numbers of ranks, speeds, etc.) can have significant impacts on the performance and energy consumption of enterprise workloads. Many of these workloads, such as deci- sion support systems (DSS), require large amounts of memory.
https://engineering.purdue.edu/HELPS/Publications/conference.html/papers/KumarDoshiDimitrovLu2011ISLPED.pdf
memory, performance, and energy consumption of a server running a decision support system (DSS). We use a Nehalem-EP 2-socket server (3 channels in each socket) with 16 Intel Xeon 5500 processors,48 GB of DDR3 SDRAM memory,and Solid State Drives (SSDs) for storage. The 48GB of memory is organized in 12 DIMMS of 4GB each. Each DIMM contains
https://dl.acm.org/citation.cfm?id=2016864
Different memory configurations (sizes, numbers of ranks, speeds, etc.) can have significant impacts on the performance and energy consumption of enterprise workloads. Many of these workloads, such as decision support systems (DSS), require large amounts of memory. This paper investigates the potential to save energy by making the memory configuration adaptive to workload behavior.Cited by: 8
https://www.academia.edu/10508279/Memory_energy_management_for_an_enterprise_decision_support_system
Energy efficiency is an important factor in designing and configuring enterprise servers. In these servers, memory may consume 40% of the total system power. Different memory configurations (sizes, numbers of ranks, speeds, etc.) can have significant
https://www.academia.edu/10333825/Memory_energy_management_for_an_enterprise_decision_support_system
Memory energy management for an enterprise decision support system
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000005993649
In these servers, memory may consume 40% of the total system power. Different memory configurations (sizes, numbers of ranks, speeds, etc.) can have significant impacts on the performance and energy consumption of enterprise workloads. Many of these workloads, such as decision support systems (DSS), require large amounts of memory.
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