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.
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.
The IO500 benchmark is a benchmark suite for High-Performance IO. It harnesses existing and trusted open-source benchmarks such as IOR and MDTest and bundles execution rules and multiple workloads with the purpose to evaluate and analyze the storage devices for various IO patterns. The IO500 benchmark is designed to provide performance boundaries of the storage for HPC applications regarding data and metadata operations under what are commonly observed to be both easy and difficult IO patterns from multiple concurrent clients. Moreover, there is a phase that scans for previously-created files that match certain conditions using a (possibly file system-specific) parallel find utility to evaluate the speed of namespace traversal and file attribute retrieval. The final score that is used to rank submissions in the list is a combined score across all the executed benchmarks.
The VSCC results will be included in a dedicated list that will be released during SC20.
Parallel Computing with Climate Models
The Community Earth System Model (CESM) is a state-of-the-art climate model developed by the National Center for Atmospheric Research. It discretizes and parameterizes Earth system motion and processes (atmosphere, ocean, land, etc.) over tens of thousands of grid boxes, often using many parallel processors. Climate models like this one are used for understanding scenarios of how the Earth system might respond to change. Competition entrants will design and build a cluster that can run several self-contained test cases using CESM. The benchmark for this challenge will be the speed at which the test case can be completed, which includes reading input files, actual compute time, and writing output from the model.
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles. It is primarily designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non-biological systems, e.g. polymers. This year we are looking into the possibility of simulating problems related to the COVID-19 pandemic.
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.
The SC20 Reproducibility Committee has selected the paper "MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization" by Mert Hidayetoğlu, Tekin Biçer, Simon Garcia de Gonzalo, Bin Ren, Doğa Gürsoy, Rajkumar Kettimuthu, Ian T. Foster, and Wen-mei W. Hwu to serve as the Virtual Student Cluster Competition (VSCC) benchmark for the Reproducibility Challenge this year. A team of reviewers selected the paper from 45 finalists based on the paper’s Artifact Descriptor (AD) and its suitability to the VSCC. The authors and the Reproducibility Committee have been working to create a reproducible benchmark that builds on the paper’s results. During the VSCC, the 16 VSCC teams will be asked to run the benchmark, replicating the findings from the original paper under different settings and with different datasets.