D-Wave Systems: An inTroduction to Programming a quantum computer
- Andy Mason, Sales Director, D-Wave Europe
Sheir Yarkoni, Technical Analysist, D-Wave Europe
- Quantum computing has progressed from ideas and research to implementation and product development. There are multiple physical devices capable of providing controllable evolution of a quantum wavefunction which could form the
basis for a quantum computer. The 2000-qubit D-Wave System uses an annealing/adiabatic architecture which natively solves discrete optimization, and probabilistic machine learning problems.
Our goal is to expand attendees’
understanding of quantum computing as implemented on the D-Wave System and where it fits in a computing environment (or maybe work flow).
- Important Notes:
- Participants will have access to 128 qubit simulators (using their own laptops) and the opportunity to implement a number of programming examples as well as view live demos on a 2000 qubit D-Wave System.
It is recommended attendees
have knowledge comparable to an M.Sc. or higher in computer science, mathematics or physics or equivalent, or otherwise have sufficient familiarity with algorithms and data structures and experience implementing algorithms
in C/C++, Matlab or Python.
High Performance I/O and in situ data processing using the ADIOS framework
- Scott A. Klasky, group leader for Scientific Data in the Computer Science and Mathematics Division, Oak Ridge National Laboratory
Norbert Podhorszki, team leader in the Scientific Data Group in the Computer Science and Mathematics Division, Oak Ridge National Laboratory
- As concurrency and complexity continue to increase on high-end machines, I/O performance is rapidly becoming a fundamental challenge to achieving exascale computing. Wider adoption of higher-level I/O abstractions will be critically important to address this challenge. Modern I/O libraries provide data models, portable APIs, storage abstractions, and self-describing data containers. They achieve high performance and scalability, allow data to be managed more effectively throughout the data life-cycle, and enable reproducible science.
Part I of this tutorial will provide an overview of parallel I/O systems and summarize the key techniques for obtaining high performance I/O on high-performance computing (HPC) resources at scale. Part II introduces the ADIOS library, delving through their usage models and examples, showing how to achieve high performance scalable I/O. Part III explains data compression. Part IV covers techniques for creating in situ analytics and teaches how to generate visualization services. Over one half of this tutorial will be hands-on sessions, where we provide access to the software and go through live examples using a Virtualbox VM. We will have users work with real examples including the heat equation, a brusselator example, and the Gray Scott Model of Reaction Diffusion.