Loncaric, Josip | Search NASA

DOE OSTI2019

Checkpoint-Restart and Platform Design Criteria

Long computations often use checkpoint/restart to survive the inevitable failures. Optimal checkpoint/restart strategy can be derived in terms of special functions, and approximated within the regime of interest. As a further step under nominal assumptions, platforms can be optimized for maximum progress rate, computed in terms of non-dimensional parameter JMTTI/Delta, where JMTTI is the Job Mean Time To Interrupt and Delta is the time it takes to write (or read) a checkpoint. Scaling to a larger platform of the same technology requires scaling checkpoint bandwidth quadratically to maintain the same progress rate. This was observed during the design of LANL’s Roadrunner in 2007, led to burst buffers proposed in 2009 and introduced on Trinity in 2015. The need to compute reliably continues to impact the design of future exascale platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

An Early Quantum Computing Proposal

The D-Wave 2X is the third generation of quantum processing created by D-Wave. NASA (with Google and USRA) and Lockheed Martin (with USC), both own D-Wave systems. Los Alamos National Laboratory (LANL) purchased a D-Wave 2X in November 2015. The D-Wave 2X processor contains (nominally) 1152 quantum bits (or qubits) and is designed to specifically perform quantum annealing, which is a well-known method for finding a global minimum of an optimization problem. This methodology is based on direct execution of a quantum evolution in experimental quantum hardware. While this can be a powerful method for solving particular kinds of problems, it also means that the D-Wave 2X processor is not a general computing processor and cannot be programmed to perform a wide variety of tasks. It is a highly specialized processor, well beyond what NNSA currently thinks of as an “advanced architecture.”A D-Wave is best described as a quantum optimizer. That is, it uses quantum superposition to find the lowest energy state of a system by repeated doses of power and settling stages. The D-Wave produces multiple solutions to any suitably formulated problem, one of which is the lowest energy state solution (global minimum). Mapping problems onto the D-Wave requires defining an objective function to be minimized and then encoding that function in the Hamiltonian of the D-Wave system. The quantum annealing method is then used to find the lowest energy configuration of the Hamiltonian using the current D-Wave Two, two-level, quantum processor. This is not always an easy thing to do, and the D-Wave Two has significant limitations that restrict problem sizes that can be run and algorithmic choices that can be made. Furthermore, as more people are exploring this technology, it has become clear that it is very difficult to come up with general approaches to optimization that can both utilize the D-Wave and that can do better than highly developed algorithms on conventional computers for specific applications. These are all fundamental challenges that must be overcome for the D-Wave, or similar, quantum computing technology to be broadly applicable.

97 MATHEMATICS AND COMPUTING↗