Intel® HPC Developer Conference 2017
Learn about 3D XPoint™ memory and open source libraries that help facilitate the adoption of persistent memory.
Usha Upadhyayula and Eduardo Berrocal, Intel
Intel® Cluster Checker is a system diagnostics tool capable of identifying issues such as those that lead to application errors or underperformance. In this presentation, see how application developers may leverage the API within Intel Cluster Checker to proactively identify and treat those issues.
Rebecca Paren, Intel
This session introduces Julia* and its tools and libraries for making parallel, high-performance computing easy on a massive scale.
Keno Fischer, Viral Shah, and Ranjan Anantharaman, Julia Computing Inc.
The two major trends in computing systems are the growth in high-performance computing (HPC) and the machine learning for big data phenomenon with an accompanying cloud infrastructure. This tutorial weaves these trends together using some key technologies of HPC and big data convergence in a scalable machine learning platform.
Langshi Chen, Mihai Avram, Supun Kamburugamuve, and Judy Qiu, Indiana University
PyCOMPSs is an approach to support a Python task-based parallel programming model where the tasks’ data-dependences are inferred at runtime. The session presents a tutorial on how to parallelize Python applications with PyCOMPSs, including results on hybrid SSF clusters with KNL nodes.
Rosa M. Badia, Barcelona Supercomputing Center (BSC)
Understand tuning opportunities using Intel® Parallel Studio XE 2018 and how it relates to code optimization. Focus on new build flags in Intel® compilers, optimizations for Intel® Performance Libraries with hands-on exercises exploiting latest features in Intel® VTune™ Amplifier and Intel® Advisor.
Carlos Rosales and Dmitry Prohorov, Intel
Zakhar Matveev, Aleksandar Ilic, INESC-ID/IST, University of Lisbon
The TAU Performance System is a mature, portable, performance evaluation tool available on HPC platforms. It supports profiling as well as tracing for programs written in C++, C, Fortran, Python*, and Java* using MPI, OpenMP, Apache Spark*, pthread, and OpenCL™ code. This tutorial introduces TAU and related tools.
Sameer Shende, University of Oregon
The Google Cloud Platform* Service is the first cloud service provider to offer Intel® Xeon® processors when creating virtual machines.
Emma Haruka Iwao, Google Cloud
This session discusses the application of convolutional generative adversarial networks to simulate particle energy showers in electromagnetic calorimeters. We present the development of our model in the neon™ framework, with detailed explanation of implementation and optimization steps to reproduce three-dimensional images of energy showers.
Sofia Vallecorsa, CERN
This tutorial demonstrates Devito, a finite difference framework that allows users to solve partial differential equations from only a few lines of Python. Explore how to use automated code generation to execute highly optimized stencil code for a range of scientific problems.
Michael Lange, Navjot Kukreja, and Fabio Luporini, Imperial College London
FPGA technologies can be leveraged as an ideal custom coprocessor to boost the performance of your algorithms. In this session, explore how to accelerate algorithms on FPGAs using the OpenCL™ platform.
Karl Qi, Intel Corporation
Singularity containers have been gaining widespread adoption in both enterprise and scientific computing due to their ability to facilitate extreme portability and reproducible software stacks. Learn about the new features that make it even more applicable for science.
Gregory Kurtzer and David Godlove, SyLabs Inc.