From 0f93cc1e17b8b2e6a257fb28b67863fa2c654e4d Mon Sep 17 00:00:00 2001 From: Keavon Chambers Date: Mon, 6 Jun 2022 16:09:42 -0700 Subject: [PATCH] Update banner for the lastest blog post --- ...022-05-12-distributed-computing-in-the-graphene-runtime.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/website/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime.md b/website/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime.md index 490e7a6e..a0648000 100644 --- a/website/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime.md +++ b/website/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime.md @@ -3,7 +3,7 @@ title = "Distributed computing in the Graphene runtime" date = 2022-05-12 [extra] -banner = "https://static.graphite.rs/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime.png" +banner = "https://static.graphite.rs/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime__2.png" author = "Keavon Chambers" reddit = "https://www.reddit.com/r/graphite/comments/unw45k/blog_post_distributed_computing_in_the_graphene/" twitter = "https://twitter.com/GraphiteEditor/status/1524664083554791424" @@ -43,6 +43,8 @@ For security and portability, user-authored nodes are compiled into WebAssembly In the product architecture, Graphene is a distributed runtime environment for quickly processing data in the node graph by utilizing a pool of CPU and GPU compute resources available on local and networked machines. Jobs are run where latency, speed, and bandwidth availability will be most likely to provide a responsive user experience. +![Graphene in a local and cloud environment](https://static.graphite.rs/content/blog/2022-05-12-distributed-computing-in-the-graphene-runtime/local-and-cloud.png) + ## Scheduler If users are running offline, their CPU threads and GPU (or multiple GPUs) are assigned jobs by the local Graphene scheduler. If running online, some jobs are performed locally while others are run in the cloud, an on-prem compute cluster, or just a spare computer on the same network. The schedulers generally prioritize keeping quicker, latency-sensitive jobs local to the client machine or LAN while allowing slower, compute-intensive jobs to usually run on the cloud.