Publications

Howard, Micah A.; Bradley, Andrew M.; Bova, S.W.; Overfelt, James R.; Wagnild, Ross M.; Dinzl, Derek J.; Hoemmen, Mark F.; Klinvex, Alicia M.

Abstract not provided.

Hu, Jonathan J.; Devine, Karen D.; Hoemmen, Mark F.; Lin, Paul L.; Rajamanickam, Sivasankaran R.; Roberts, Nathan V.; Siefert, Christopher S.; Trott, Christian R.; Prokopenko, Andrey P.

Abstract not provided.

Brunini, Victor B.; Clausen, Jonathan C.; Noble, David R.; Forster, Christopher J.; Trott, Christian R.; Hammond, Simon D.; Hoemmen, Mark F.; Lin, Paul L.

Abstract not provided.

23rd AIAA Computational Fluid Dynamics Conference, 2017

Howard, Micah A.; Bradley, Andrew M.; Bova, S.W.; Overfelt, James R.; Wagnild, Ross M.; Dinzl, Derek J.; Hoemmen, Mark F.; Klinvex, Alicia M.

High performance computing (HPC) is undergoing a dramatic change in computing architectures. Nextgeneration HPC systems are being based primarily on many-core processing units and general purpose graphics processing units (GPUs). A computing node on a next-generation system can be, and in practice is, heterogeneous in nature, involving multiple memory spaces and multiple execution spaces. This presents a challenge for the development of application codes that wish to compute at the extreme scales afforded by these next-generation HPC technologies and systems - the best parallel programming model for one system is not necessarily the best parallel programming model for another. This inevitably raises the following question: how does an application code achieve high performance on disparate computing architectures without having entirely different, or at least significantly different, code paths, one for each architecture? This question has given rise to the term ‘performance portability’, a notion concerned with porting application code performance from architecture to architecture using a single code base. In this paper, we present the work being done at Sandia National Labs to develop a performance portable compressible CFD code that is targeting the ‘leadership’ class supercomputers the National Nuclear Security Administration (NNSA) is acquiring over the course of the next decade.

Rajamanickam, Sivasankaran R.; Devine, Karen D.; Hu, Jonathan J.; Hoemmen, Mark F.

Abstract not provided.

Deveci, Mehmet D.; Rajamanickam, Sivasankaran R.; Kim, Kyungjoo K.; Bradley, Andrew M.; Trott, Christian R.; Hoemmen, Mark F.; Boman, Erik G.

Abstract not provided.

Edwards, Harold C.; Sunderland, Daniel S.; Hoemmen, Mark F.; Ellingwood, Nathan D.; Trott, Christian R.; Mackey, Greg

Abstract not provided.

Rothganger, Fredrick R.; Hoemmen, Mark F.; Phipps, Eric T.; Warrender, Christina E.

Abstract not provided.

Eberhardt, Ryan E.; Hoemmen, Mark F.

Abstract not provided.

Prokopenko, Andrey V.; Siefert, Christopher S.; Hu, Jonathan J.; Hoemmen, Mark F.; Klinvex, Alicia M.

This is the definitive user manual for the I FPACK 2 package in the Trilinos project. I FPACK 2 pro- vides implementations of iterative algorithms (e.g., Jacobi, SOR, additive Schwarz) and processor- based incomplete factorizations. I FPACK 2 is part of the Trilinos T PETRA solver stack, is templated on index, scalar, and node types, and leverages node-level parallelism indirectly through its use of T PETRA kernels. I FPACK 2 can be used to solve to matrix systems with greater than 2 billion rows (using 64-bit indices). Any options not documented in this manual should be considered strictly experimental .

Elliott, James J.; Hoemmen, Mark F.; Mueller, Frank M.

Abstract not provided.

Phipps, Eric T.; D'Elia, Marta D.; Edwards, Harold C.; Hoemmen, Mark F.; Hu, Jonathan J.; Rajamanickam, Sivasankaran R.

Abstract not provided.

Trott, Christian R.; Edwards, Harold C.; Ellingwood, Nathan D.; Hammond, Simon D.; Deveci, Mehmet D.; Boman, Erik G.; Bradley, Andrew M.; Hoemmen, Mark F.; Rajamanickam, Sivasankaran R.

Abstract not provided.

Rajamanickam, Sivasankaran R.; Yamazaki, Ichitaro Y.; Boman, Erik G.; Hoemmen, Mark F.; Heroux, Michael A.; Tomov, Stan T.; Dongarra, Jack D.

Abstract not provided.

Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2015

DeBenedictis, Erik; Cook, Jeanine C.; Hoemmen, Mark F.; Metodi, Tzvetan S.

We discuss a new approach to computing that retains the possibility of exponential growth while making substantial use of the existing technology. The exponential improvement path of Moore's Law has been the driver behind the computing approach of Turing, von Neumann, and FORTRAN-like languages. Performance growth is slowing at the system level, even though further exponential growth should be possible. We propose two technology shifts as a remedy, the first being the formulation of a scaling rule for scaling into the third dimension. This involves use of circuit-level energy efficiency increases using adiabatic circuits to avoid overheating. However, this scaling rule is incompatible with the von Neumann architecture. The second technology shift is a computer architecture and programming change to an extremely aggressive form of Processor-In-Memory (PIM) architecture, which we call Processor-In-Memory-and-Storage (PIMS). Theoretical analysis shows that the PIMS architecture is compatible with the 3D scaling rule, suggesting both immediate benefit and a long-term improvement path.

DeBenedictis, Erik; Cook, Jeanine C.; Metodi, Tzvetan S.; Hoemmen, Mark F.; Marinella, Matthew J.; Schiek, Richard S.; Zima, Hans Z.

Abstract not provided.

HPDC 2015 - Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing

Elliott, James J.; Hoemmen, Mark F.; Mueller, Frank

We present a fault model designed to bring out the \worst" in iterative solvers based on mathematical properties. Our model introduces substantially higher overhead, but smaller variance, than a fault model based on random bit ips. We also relate the statistics from our experiments back to the solvers' conffguration, and briey address the computational efiort that each model requires. Our approach requires signi ficantly fewer resources, while punishing our solvers with undetectable errors that require notable overhead for recovery. This work also illustrates the robustness of our resilient algorithms: Not only do we make forward progress in the presence of pathological faults, we always obtain the correct answer.

Rajamanickam, Sivasankaran R.; Yamazaki, Ichitaro Y.; Boman, Erik G.; Hoemmen, Mark F.; Heroux, Michael A.; Tomov, Stanimire T.; Dongarra, Jack D.

Abstract not provided.

Elliott, James J.; Hoemmen, Mark F.; Mueller, Frank M.

Abstract not provided.

Journal of Computational Science

Elliott, James J.; Hoemmen, Mark F.; Mueller, Frank M.

Incorrect computer hardware behavior may corrupt intermediate computations in numerical algorithms, possibly resulting in incorrect answers. Prior work models misbehaving hardware by randomly flipping bits in memory. We start by accepting this premise, and present an analytic model for the error introduced by a bit flip in an IEEE 754 floating-point number. We then relate this finding to the linear algebra concepts of normalization and matrix equilibration. In particular, we present a case study illustrating that normalizing both vector inputs of a dot product minimizes the probability of a single bit flip causing a large error in the dot product's result. Moreover, the absolute error is either less than one or very large, which allows detection of large errors. Then, we apply this to the GMRES iterative solver. We count all possible errors that can be introduced through faults in arithmetic in the computationally intensive orthogonalization phase of GMRES, and show that when the matrix is equilibrated, the absolute error is bounded above by one.

Parallel Processing Letters

Lin, Paul L.; Bettencourt, Matthew T.; Domino, Stefan P.; Fisher, Travis C.; Hoemmen, Mark F.; Hu, Jonathan J.; Phipps, Eric T.; Prokopenko, Andrey V.; Rajamanickam, Sivasankaran R.; Siefert, Christopher S.; Kennon, Stephen

Trilinos is an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. While Trilinos was originally designed for scalable solutions of large problems, the fidelity needed by many simulations is significantly greater than what one could have envisioned two decades ago. When problem sizes exceed a billion elements even scalable applications and solver stacks require a complete revision. The second-generation Trilinos employs C++ templates in order to solve arbitrarily large problems. We present a case study of the integration of Trilinos with a low Mach fluids engineering application (SIERRA low Mach module/Nalu). Through the use of improved algorithms and better software engineering practices, we demonstrate good weak scaling for up to a nine billion element large eddy simulation (LES) problem on unstructured meshes with a 27 billion row matrix on 524,288 cores of an IBM Blue Gene/Q platform.

Elliott, James J.; Hoemmen, Mark F.; Mueller, Frank M.

Abstract not provided.

Fisher, Travis C.; Siefert, Christopher S.; Hoemmen, Mark F.; Prokopenko, Andrey P.

Abstract not provided.

Hoemmen, Mark F.; Edwards, Harold C.

Abstract not provided.

Hoemmen, Mark F.; Elliott, James J.; Mueller, Frank M.

Abstract not provided.