From bb63af28421b7989ac970c209ab7635942b8a1d4 Mon Sep 17 00:00:00 2001 From: pearce8 Date: Thu, 2 Apr 2026 18:03:08 -0500 Subject: [PATCH 1/2] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b98e64..4ec2e38 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ of Energy (DOE) National Nuclear Security Administration (NNSA). ### Sphinx Sample Sphinx documentation for consideration is provided within the -`doc/sphinx` folder. More information can be read [here](doc/sphinx/README.md). +`doc/sphinx` folder. More information can be read [here](docs/sphinx/README.md). ## License From fc8950e7e11b0f04de7362b4f21c9fd4aad4db93 Mon Sep 17 00:00:00 2001 From: pearce8 Date: Thu, 2 Apr 2026 18:04:10 -0500 Subject: [PATCH 2/2] Update kripke.rst --- docs/11_kripke/kripke.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/11_kripke/kripke.rst b/docs/11_kripke/kripke.rst index 3a74cf3..c8dfe4d 100644 --- a/docs/11_kripke/kripke.rst +++ b/docs/11_kripke/kripke.rst @@ -18,7 +18,7 @@ Most Sn transport codes are designed around one of these nestings, which is an i Early research has found that the problem dimensions (zones, groups, directions, scattering order) and the scaling (number of threads and MPI tasks), can make a profound difference in the performance of each of these nestings. To our knowledge, this is a capability unique to Kripke, and should provide key insight into how data-layout affects Sn solver performance. -An asynchronous MPI-based parallel sweep algorithm is provided, which employs the concepts of Group Sets (GS), Zone Sets (ZS), and Direction Sets (DS), borrowed from the [Texas A&M code PDT](https://parasol.tamu.edu/asci/). +An asynchronous MPI-based parallel sweep algorithm is provided, which employs the concepts of Group Sets (GS), Zone Sets (ZS), and Direction Sets (DS), borrowed from the [Texas A&M code PDT](https://multiphysics.engr.tamu.edu/research-topics/parallel-deterministic-transport/). As we explore new architectures and programming paradigms with Kripke, we will be able to incorporate these findings and ideas into our larger codes. The main advantages of using Kripke for this exploration is that it's light-weight (i.e. easily refactored and modified), and it gets us closer to the real question we want answered: "What is the best way to layout and traverse data in parallel in an Sn code on a given architecture+programming-model?" instead of the more commonly asked question "What is the best way to map my existing Sn code to a given architecture+programming-model?".