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Jacob Merson

Publications and source records attributed to Jacob Merson.

4 recordsLinked to original sources

A Multiscale Workflow for Thermal Analysis of 3DI Chip Stacks

Thermally aware design of 2.5D and 3D advanced packaging systems will require fast, accurate, and powerful thermal analysis of chiplets, stacks, and packages. These systems contain multiple materials with non-linear heat transfer properties and geometric feature sizes that span many orders of magnitude. The smallest heterostructures in the front and back ends of the line present significant thermal modeling and analysis challenges in isolation. Replicated millions or billions of times in a chiplet stack, these structures present a near insurmountable hurdle to meeting the speed and accuracy needed of analysis in the design process. Additionally, establishing precise parameter values for the materials in these systems, when size and temperature dependencies create significant deviations from bulk properties, further complicates the problem. To address these issues, we have developed a multiscale methodology that advances the current state of the field by enabling die-scale simulations that capture phenomena arising from the structural details of the BEOL metallization stack. Taking advantage of the large length-scale separation between the BEOL features and the die-level structures, we employ a hierarchical, multiscale, finite-element approach. This hierarchical method uses a standard finite element method (FEM) formulation on a die or package scale, using computational homogenization to obtain effective thermal conductivities in the BEOL. Referring to industry-standard layout and design files, we construct and solve a locally appropriate subscale FEM problem in a representative volume element (RVE) at every quadrature point in the macroscale FEM problem. To accomplish this, RVE models are automatically constructed, meshed, and used to compute homogenized, anisotropic, thermal conductivities from the relevant GDSII or OASIS.

cs.OH

A new open source framework for multiscale modeling of fibrous materials on heterogeneous supercomputers

This article presents MuMFiM, an open source application for multiscale modeling of fibrous materials on massively parallel computers. MuMFiM uses two scales to represent fibrous materials such as biological network materials (extracellular matrix, connective tissue, etc.). It is designed to make use of multiple levels of parallelism, including distributed parallelism of the macro and microscales as well as GPU accelerated data-parallelism of the microscale. Scaling results of the GPU accelerated microscale show that solving microscale problems concurrently on the GPU can lead to a 1000x speedup over the solution of a single RVE on the GPU. In addition, we show nearly optimal strong and weak scaling results of MuMFiM on up to 128 nodes of AiMOS (Rensselaer Polytechnic Institute) which is composed of IBM AC922 nodes with 6 Volta V100 GPU and 2 20 core Power 9 CPUs each. We also show how MuMFiM can be used to solve problems of interest to the broader engineering community, in particular providing an example of the facet capsule ligament (FCL) of the human spine undergoing uniaxial extension.

cs.DC

Using Hierarchical Parallelism to Accelerate the Solution of Many Small Partial Differential Equations

This paper presents efforts to improve the hierarchical parallelism of a two scale simulation code. Two methods to improve the GPU parallel performance were developed and compared. The first used the NVIDIA Multi-Process Service and the second moved the entire sub-problem loop into a single kernel using Kokkos hierarchical parallelism and a PackedView data structure. Both approaches improved parallel performance with the second method providing the greatest improvements.

cs.DC

A performance portable, fully implicit Landau collision operator with batched linear solvers

Modern accelerators use hierarchical parallel programming models that enable massive multithreading within a processing element (PE), with multiple PEs per device driven by traditional processes. Batching is a technique for exposing PE-level parallelism in algorithms that have traditionally run on MPI processes or multiple threads within a single process. Opportunities for batching arise in, for example, kinetic discretizations of magnetized plasmas where collisions are advanced in velocity space at each spatial point independently. This paper builds on previous work on a high-performance, fully nonlinear, Landau collision operator by batching the linear solver, as well as batching the spatial point problems and adding new support for multiple grids for multiscale, multi-species problems. An anisotropic relaxation verification test that agrees well with previous published results and analytical models is presented. The performance results from NVIDIA A100 and AMD MI250X nodes are presented with hardware utilization analysis for each architecture. The entire implicit Landau operator time advance is implemented in Kokkos for performance portability, running entirely on the device and is available in the PETSc numerical library.

physics.plasm-ph