The 2016 Intel® HPC Developer Conference brought together developers from around the world to discuss code modernization in high-performance computing. For those who may have missed it or if you want to catch presentations that you may have missed, we have posted the Top Tech Sessions of 2016 to the HPC Developer’s Conference webpage. The sessions are split out by track, including Artificial Intelligence/Machine Learning, Systems, Software Visualization, Parallel Programming and others.
Artificial Intelligence/Machine Learning Track
- Accelerating Machine Learning Software on IA
- Data Analytics, Machine Learning and HPC in Today’s Changing Application Environment
- Scaling Deep Learning
- Optimizing Machine Learning workloads on Intel Platforms
- Performance Optimization of Deep Learning Frameworks Caffe* and Tensorflow* for Xeon Phi Cluster
- Using Machine Learning to Avoid the Unwanted
- Deep Neural Network Art
- Massively Parallel K-Nearest Neighbor Computation on Distributed Architectures
Systems Track
- Latest developments in OpenFabrics Interface (OFI): The new scalable fabric SW layer for Supercomputers
- Discover, extend and modernize your current development approach for hetergeneous compute with standards based OFI/MPI/OpenMP programming methods on Intel® Xeon Phi™ achitectures
- Intel® Omni-Path Architecture Software Architecture Overview
- Exploiting HPC Technologies to Accelerate Big Data Processing (Hadoop*, Spark*, and Memcached*)
- Best Practices and Performance Study of HPC Clusters
- Challenges of Deploying your HPC Application to the Cloud
- Parallel Performance: moving MPI Applications to the Next Level
- Simplify System Software Stack Development and Maintenance
High Productivity Languages Track
- Python Scalability Story in Production Environments
- The State of High Performance Computing in the Open Source R Ecosystem (University of Tennesse)
- Data Analytics and Simulation using the MATLAB Language (Mathworks)
- Julia in Parallel and High Performance Computing
- Jupyter: Python, Julia, C, and MKL HPC Batteries included
Software Visualization Track
- Introduction to SDVis and Update on Intel Efforts (James Jeffers, Intel)
- Update on OpenSWR (Jefferson Amstutz, Intel)
- OSPRay 1.0 and Beyond (Jefferson Amstutz, Intel)
- Large-scale Distributed Rendering with the OSPRay Ray Tracing Framework (Carson Brownley Intel)
- Realizing Multi-Hit Ray Tracing in Embree and OSPRay (Christiaan Gribble, Intel/SURVICE)
- Visualization w/Visit on Intel® Xeon Phi™ Processor (code name Knights Landing) KNL (Jian Huang & Hank Childs, Unv of Oregon / Unv of Tennessee)
- SDVis Research at the University of Utah Intel Parallel Compute Center (Aaron Knoll, Unv. Of Utah)
- OSPRay Integration into Pcon-Planner (Caglar Özgür & Frank Wicht, Eastern Graphics)
- Visualization and Analysis of Biomolecular Complexes on Upcoming KNL-based HPC Systems: TACC Stampede 2 and ANL Aurora
- Paraview and VTK w/OSPRay* and OpenSWR*
- SDVIs and In-Situ Visualization on TACC's Stampede
Parallel Programming Track
- Optimizations of Bspline-based Orbital Evaluations in Quantum Monte Carlo on Multi/Many-Core Shared Memory Processors
- Utilizating Roofline Analysis in the Intel® Advisor to Deliver Optimized Performance for Applications on Intel® Xeon Phi™ Processor (Code named Knights Landing)
- Many Cores for the Masses: Lessons Learned from Application Readiness Efforts at NERSC for the Knights Landing based Cori System
- Reshaping Core Genomics Software tools for the Many-Core era
- High-Performance and Scalable MPI +X Library for Emerging HPC Clusters
- All the things you need to know about Intel MPI Library (TACC)
- Using C++ and Intel Threading Building Blocks to program across processors and co-processors
- Improving Vectorization Efficiency using Intel SIMD Data Layout Template
- SWIFT: Using Task-Based Parallelism, Fully Asynchronous Communication and Vectorization to achieve maximal HPC performance
- Case Study: Optimization of Profrager, a protein structure and function prediction tool developed at the Brazilian National Laboratory for Scientific Computing (LNCC)
- Porting Industrial Application on Intel® Xeon Phi™: Altair RADIOSS Case Study, Developer feedbacks and Outlooks