The Linpack Benchmark is a measure of a computer’s floating-point rate of execution. It is determined by running a computer program that solves a dense system of linear equations. It is used by the TOP 500 as a tool to rank peak performance. The benchmark allows the user to scale the size of the problem and to optimize the software in order to achieve the best performance for a given machine. This performance does not reflect the overall performance of a given system, as no single number ever can. It does, however, reflect the performance of a dedicated system for solving a dense system of linear equations. Since the problem is very regular, the performance achieved is quite high, and the performance numbers give a good correction of peak performance.
- HPCG Benchmark
The High Performance Conjugate Gradients (HPCG) Benchmark project is an effort to create a new metric for ranking HPC systems. HPCG is intended as a complement to the High Performance LINPACK (HPL) benchmark, currently used to rank the TOP500 computing systems. The computational and data access patterns of HPL are still representative of some important scalable applications, but not all. HPCG is designed to exercise computational and data access patterns that more closely match a different and broad set of important applications, and to give incentive to computer system designers to invest in capabilities that will have impact on the collective performance of these applications.
- Tersoff Multi-Body Potential
For a second year in a row, students in the cluster competition will be asked to replicate the results of a publication from the previous year’s Supercomputing conference. The paper is "The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability" by Höhnerbach, Ismail, and Bientinesi. This paper was chosen from nine Supercomputing conference papers from 2016 that submitted an Artifact Description appendix.
The work from this paper adds a high performance implementation of the Tersoff potential to the widely used LAMMPS molecular dynamics (MD) code. Molecular dynamics simulations track the trajectory of up to billions of particles at a time. For most MD simulations, the interaction between particles is described by pairwise potentials such as the Coulomb potential. However, the Tersoff potential is described by a multi-body potential requiring much more computational work.
Cassava is the main staple food for 800 million people globally. Currently, about half of the world production of cassava is in Africa and many African families eat cassava for breakfast, lunch and dinner. Thus, cassava holds great promise for feeding Africa's growing population and reducing rural and urban poverty.
In east Africa, Cassava production is being affected by two devastating viruses—Cassava brown streak virus (CBSV) and Uganda cassava brown streak virus (UCBSV), with up to 100% yield loss for smallholder farmers in the region. These viruses are transmitted by 34 species of whiteflies called Bemisia tabaci. They have the same physical traits, making them impossible to identify without genomic sequencing. Using supercomputing, genomic and evolution history, Dr. Laura Boykin is leading this effort, funded by the Bill and Melinda Gates Foundation, to identify the species of whiteflies that transmit these viruses, to prevent this crop devastation. Her team is gathering big volume of useful data, now publicly available via WhiteFlyBase, which will help researchers breed new strains of cassava that resist the whitefly.
MrBayes is one of the main tools that Dr. Boykin used in identifying the species of whiteflies. It is a program for Bayesian phylogenetic inference and model choice across a wide range of phylogenetic and evolutionary models. MrBayes uses Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. Bayesian approaches were introduced into phylogenetics in the mid-1990s and have enjoyed enormous popularity since then, including among entomologists. For some background of the species identification process students can view the paper by Dr Laura Boykin and Dr Paul De Barro. 
MrBayes coupled with the Beagle library will give HPC administrators plenty of options to optimise the utilisation of their clusters. Students will be required to research and choose the most efficient installation type of MrBayes for their own cluster, design a run which will complete within the given timeframe and produce an accurate consensus tree with good posterior probability.
Practice datasets for this application: https://github.com/anders-savill/mrbayes-scc17/blob/master/README.md
 Bayesian Phylogenetics and Its Influence on Insect Systematics
Annual Review of Entomology. Fredrik Ronquist and Andrew R. Deans
 A practical guide to identifying members of the Bemisia tabaci species complex: and other
morphologically identical species. Laura M. Boykin and Paul J. De Barro.
 MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large
Model Space. Fredrik Ronquist, Maxim Teslenko, Paul van der Mark, Daniel L. Ayres,
Aaron Darling, Sebastian Höhna, Bret Larget, Liang Liu, Marc A. Suchard, and John P.
- Mystery Application
At the start of the competition, teams will be given an application and datasets for a mystery application. Students will be expected to build, optimize and run this mystery application all at the competition.
- Power Shutoff Activity
Some time during the general competition we will be shutting down the power. The exact timing of the shutdown is a secret. You and your team will need to know how to bring the hardware and software back from a full unscheduled power outage and how to resume any workload you were processing at that time. This exercise is designed to simulate real world events that system administrators must respond to. This activity will allow your team to demonstrate their systems skills by recovering the system.
This has happened before. During the first Student Cluster Competition, in 2007, the power to the Reno Convention Center suddenly failed. The entire show floor went dark. It turned out that the power coming to the convention center was inadequate for Supercomputing's high-performance machines. Power was out for an hour or so, followed by what the press described as "the world's largest reboot". After the conference, crews were seen laying additional power cables across Virginia Street.
Our competition clusters, of course, went down. When the power was restored, some teams, who had been checkpointing their systems, resumed their computations quickly. Other teams, who had not been saving data, lost many hours of work and had to start over. The experience prompted discussions about checkpointing in the real world--the tradeoff between protecting against possible disasters at a cost of reducing computations.
Since power and other failures are the realities of modern computing systems, we would like to encourage cluster teams to understand the tradeoffs, and to consider what is needed in real life. This year we will turn this thought-provoking accident into an activity to capture the think-on-your-feet spirit of the first competition.
The Overall SCC Winner will be the team with the highest score when combining their correctly completed workload of the four competition applications, mystery application, best benchmark run, application interviews, and HPC interview. The HPC interview will take into consideration the team's participation in the SC16 conference as well as their ability to wow the judges on their competition know-how.
Teams will be required to attend other aspects of the convention beyond the Student Cluster Competition, which will be included in their final score. Further details will be provided before the competition.