Berkeley Lab

Co-Design

Berkeley Lab supports two of the five co-design centers under the Department of Energy’s Exascale Computing Project (ECP). It leads the Block-Structured Adaptive Mesh Refinement Co-Design Center and partners with two other national labs and a university in the ExaGraph Co-Design Center.

Block-Structured Adaptive Mesh Refinement Co-Design Center

Berkeley Lab leads the Block-Structured Adaptive Mesh Refinement (AMReX) Co-Design Center.
photo of John B. Bell, Lawrence Berkeley National Laboratory

John Bell directs the AMRex Co-Design Center at Berkeley Lab

John Bell heads the center with support from Argonne National Laboratory and the National Renewable Energy Laboratory. The Co-Design Center is developing a new framework, AMReX, to support the development of block-structured AMR algorithms for solving systems of partial differential equations (PDE’s) with complex boundary conditions on exascale architectures. Block-structured AMR provides a natural framework in which to focus computing power on the most critical parts of the problem in the most computationally efficient way possible.

Block-structured AMR is already widely used to solve many problems relevant to DOE. Berkeley Lab’s Center for Computational Sciences and Engineering, which Bell previously led, and the lab’s Applied Numerical Algorithms Group have respectively developed BoxLib and Chombo, two of the leading AMR frameworks used by the U.S. research community. And at least 22 exascale application projects (in the areas of accelerators, astrophysics, combustion, cosmology and multiphase flow) rely on the technology.

ExaGraph Co-Design Center

Berkeley Lab is also a partner in the ECP Co-Design Center that focuses on Graph Analytics — combinatorial (graph) kernels that play a crucial enabling role in many data analytic computing (DAC) application areas as well as several ECP applications.

The ExaGraph Co-Design Center is led by Pacific Northwest National Laboratory (PNNL) in partnership with Berkeley Lab (Aydın Buluç heads the effort), Sandia National Laboratories and Purdue University. It targets a number of key data analytic computational motifs that are currently not being addressed in existing ECP Co-Design Centers, such as graph traversals, graph matching, graph coloring and graph clustering (including clique enumeration, parallel branch-and-bound, and graph partitioning).