Co-Design: Deploying Leading-Edge Computing Capabilities



Kripke is a simple, scalable, 3D Sn deterministic particle transport code. Its primary purpose is to research how data layout, programming paradigms and architectures effect the implementation and performance of Sn transport. A main goal of Kripke is investigating how different data-layouts effect instruction, thread and task level parallelism, and what the implications are on overall solver performance.

Kripkie supports storage of angular fluxes (Psi) using all six striding orders (or “nestings”) of Directions (D), Groups (G), and Zones (Z), and provides computational kernels specifically written for each of these nestings. Most Sn transport codes are designed around one of these nestings, which is an inflexibility that leads to software engineering compromises when porting to new architectures and programming paradigms. Early research has found that the problem dimensions 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 effects Sn performance. An asynchronous MPI-based parallel sweep algorithm is provided, which employs the concepts of Group Sets (GS) and Zone Sets (ZS), Direction Sets (DS), borrowed from the Texas A&M code PDT.

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 (ie. easily refactored and modified), and it gets us closer to the real question we want answered: “What is the best way to layout and implement 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?”.


Kripke on GitHub