The Pittsburgh Supercomputing Center is pleased to present a workshop on GPU programming using OpenACC. OpenACC is the accepted standard using compiler directives to allow quick development of GPU capable codes using standard languages and compilers. It has been used with great success to accelerate real applications within very short development periods. This workshop assumes knowledge of either C or Fortran programming. Hands-on exercises using the Bridges-2 computing platform at the Pittsburgh Supercomputing Center are included to give attendees practice with the concepts presented.
The Pittsburgh Supercomputing Center is pleased to present a workshop on GPU programming using OpenACC. OpenACC is the accepted standard using compiler directives to allow quick development of GPU capable codes using standard languages and compilers. It has been used with great success to accelerate real applications within very short development periods. This workshop assumes knowledge of either C or Fortran programming. Hands-on exercises using the Bridges-2 computing platform at the Pittsburgh Supercomputing Center are included to give attendees practice with the concepts presented.
This workshop will focus on topics including big data analytics and machine learning with Spark, and deep learning using Tensorflow. Hands-on exercises are included to give attendees practice with the concepts presented.
This two day workshop is intended to give C and Fortran programmers a hands-on introduction to MPI programming. Both days are compact, to accommodate multiple time zones, but packed with useful information and lab exercises. Attendees will leave with a working knowledge of how to write scalable codes using MPI – the standard programming tool of scalable parallel computing.
This workshop is intended to give C and Fortran programmers a hands-on introduction to OpenMP programming. Attendees will leave with a working knowledge of how to write scalable codes using OpenMP. Hands-on exercises are included to give attendees practice with the concepts presented.
Bokeh is a software package in the Python ecosystem for data science that provides support for interactive visualization of data. As such, it is complementary to other visualization packages that focus more on enabling the generation of static figures for inclusion in publications and reports. Bokeh renders data visualizations within web browsers and Jupyter notebooks, providing capabilities for inspecting different aspects of plotted data, selecting and highlighting subsets of data, setting parameters through graphical user interfaces and direct interactions with plots, and the construction of dashboards that integrate different data streams and analyses based on user interactions.
This case study details how to use various profiling tools to assess computational performance and possible optimization strategies that can improve performance. This walk-through focuses on the Stampede2 KNL nodes at TACC, but the tools and approaches are generally applicable to a variety of advanced computational architectures. The complex internal structure of modern processors makes such profiling and analysis tools especially important, as one is often not able to predict computational performance a priori in the face of so many contributing factors. Analyses discussed here include: Identification of code hotspots Scaling of computational runtimes with number of nodes Identification of performance limitations due to inefficient communication with memory Profiling of vectorization performance Optimizations emphasized here primarily involve the use of various compiler options and small compiler directives that can be added to code, as opposed to large-scale restructuring of algorithms or data structures to improve performance. The material is based on a webcast presentation given by Shiquan Su (NCAR) and Chad Burdyshaw (NICS) on October 17, 2017, as part of the ECSS Symposium Series. This tutorial augments that presentation by integrating video clips from the webcast with a textual summary of key points, along with further explanations and remarks. The latter part of the joint presentation by Burdyshaw, which leverages the use of the Intel performance analysis tools (especially VTune Amplifier XE or "VTune"), is emphasized here, as it should be pertinent to a wide range of application codes. The first part of the presentation by Su focuses more on the scientific and numerical background of the particular code being analyzed. The key take-home messages from Su's presentation are summarized in the Application Context topic (and expanded upon in the Appendix), which serves as a prelude to the more generally applicable topics, Performance Analysis and Vectorization & Parallelization.
When running a job for any substantial length of time, you hope that nothing interrupts it so that you won't have to start over. But no matter the environment, interruption is always a possibility. Checkpoint/Restart (C/R) solutions allow you to resume a job from approximately where it left off. Checkpointing strategies also have additional benefits beyond fault tolerance, so learning about these strategies is likely to benefit your own work. This roadmap provides an overview of the checkpointing/restart strategy and a survey of different types of C/R solutions.
The C programming language is heavily used in the scientific and high performance computing community, and also happens to be the same language in which many operating systems are written. Thus, it is important for scientists and engineers to have a good understanding of how the language can assist them with their computing needs. This document is provided for the beginning programmer who has an interest in learning to effectively use the C language. If you have never programmed before, you can also use this document to learn the basic concepts of programming. However, you may also want to refer to other references as well.
