There are some machine learning workloads that could be done in this
context. Renting smartphones may be more challenging, but desktop or
server idle computing power could be used in distributed offline
training algorithms.
The physics community does make heavy use of grid computing
(https://home.cern/science/computing/worldwide-lhc-computing-grid),
however am not sure of the economics and of many other commercial
workloads with similar requirements.
On 11/28/19 10:58 PM, Lux, Jim (US 337K) via Beowulf wrote:
Don't forget background loads like the cooling fan, losses in the power supply,
etc. Most power supplies (even switchers, unless specifically designed for
this) have lower efficiency at lower loads.
On 11/28/19, 12:39 AM, "Beowulf on behalf of Janne Blomqvist"
<beowulf-boun...@beowulf.org on behalf of janne.blomqv...@aalto.fi> wrote:
On 27/11/2019 22.56, Lux, Jim (US 337K) via Beowulf wrote:
> With respect to "free cycles" in desktop computers - back in the day, 10-15 years
ago, a bunch of folks made measurements on cluster nodes of one sort or another. As I recall, there
*is* a power consumption change between full load and not, but there's a significant "background
load" that is more than 50% of the total power consumption.
With current hardware, there is a significant difference (at least,
assuming the "ipmi-dcmi --get-system-power-statistics" output is
correct). On our skylake nodes ("standard 2-socket CPU nodes") idle
power is about 50W, when running flat out about 400W.
Somewhat older hardware is less good at saving power when idle, Westmere
nodes consume about 100W idling. IIRC our old Istanbul Opterons (which
we have already thrown away, so can't double-check) consumed about 170W
when idle.
--
Janne Blomqvist
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