From 46f7e55a04582d5f07c0a31b9f72fe3fe1e6a512 Mon Sep 17 00:00:00 2001 From: Boris <boris.baldassari@eclipse-foundation.org> Date: Wed, 6 Apr 2022 16:28:09 +0200 Subject: [PATCH] Update aice aura demonstrator following fza review. (cherry picked from commit a543666a54db115ec8d8133c39b6d3647958ca7b) --- content/articles/aice_aura_demonstrator/index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/articles/aice_aura_demonstrator/index.md b/content/articles/aice_aura_demonstrator/index.md index a447786..065980c 100644 --- a/content/articles/aice_aura_demonstrator/index.md +++ b/content/articles/aice_aura_demonstrator/index.md @@ -137,10 +137,10 @@ Also by being able to run the process everywhere, we could execute it on several  On three different machines: -* A middle-range laptop, HDD disks and i7 CPU. -* A high-range Station, SSD disks and (a better) i7 CPU. -* A high-range server (SDIA), HDD disks and 2 x Xeon (48 threads). -* With a single container for data preparation vs. multiple containers executed in parallel. +* A middle-range laptop (label: Laptop), HDD disks and i7 CPU. +* A high-range station (label: Station), SSD disks and (a better) i7 CPU. +* A high-range server (label: SDIA), HDD disks and 2 x Xeon (48 threads). +* With a single container for data preparation vs. multiple containers executed in parallel (label: Mono / Multi). We could identify different behaviours regarding performance. The data preparation step relies heavily on IOs, and improving the disk throughput (e.g. SSD + NVMe instead of a classic HDD) shows a 30% gain. The ML training on the other hand is very CPU- and memory- intensive, and running it on a node with a large number of threads (e.g. 48 in our case) brings a stunning 10x performance improvement compared to a laptop equipped with an Intel i7. @@ -148,7 +148,7 @@ We could identify different behaviours regarding performance. The data preparati AURA uses Grafana to display the ECG signals and the associated annotations, both for the creation of annotated data sets and for their exploitation. In order to build this workflow we need to import the rr-intervals files and their associated annotations in a PostgreSQL database, and configure Grafana to read and display the corresponding time series. -An example of rr-interval with the associated annotations (blue/red bottom line), is shown below: +An example of rr-interval plot with the associated annotations (blue/red bottom line) is shown below:  -- GitLab