OpenPOWER Foundation Members to Present at SC17 Conference
Published on Tuesday 7 November 2017
SC17 takes place November 12-17, 2017. Dedicated to showcasing work in high performance computing, networking, storage and analysis by the international HPC community, this year’s event is bound to be memorable.
OpenPOWER Foundation members including IBM, NVIDIA, Mellanox, Oak Ridge National Laboratory, Lawrence Livermore National Laboratory and Red Hat will be presenting a variety of talks, panels, research papers, tutorials, workshops, posters and Birds of a Feather sessions. Be sure to evaluate the sessions below and attend as many as possible at SC17!
IBM
- Application Porting and Optimization on GPU-Accelerated POWER Architectures
- Charting the PMIxRoadmap
- DOME Hot-Water Cooled MicroDataCenter
- Eighth Annual Workshop for the Energy Efficient HPC Working Group (EE HPC WG)
- Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS)
- Making HPC Consumable: Helping Wet-Lab Chemists Access the Power of Computational Methods
- OpenCAPI: High Performance, Host-Agnostic, Coherent Accelerator Interface
- P08: Performance Optimization of Matrix-free Finite-Element Algorithms within deal.II
- P58: Wharf: Sharing Docker Images across Hosts from a Distributed Filesystem
- P79: Porting the Opacity Client Library to a CPU-GPU Cluster Using OpenMP 4.5
- PowerAPI, GEOPM and Redfish: Open Interfaces for Power/Energy Measurement and Control
- Topology-Aware GPU Scheduling for Learning Workloads in Cloud Environments
- Towards a Composable Computer System
- Workshop on Education for High Performance Computing (EduHPC)
NVIDIA
- Application Porting and Optimization on GPU-Accelerated POWER Architectures
- How Serious Are We About the Convergence Between HPC and Big Data?
- Interactivity in Supercomputing
- OpenACC API User Experience, Vendor Reaction, Relevance, and Roadmap
- Scalable Parallel Programming Using OpenACC for Multicore, GPUs, and Manycore
- Toward Standardized Near-Data Processing with Unrestricted Data Placement for GPUs
- Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications
Mellanox
- Accelerating Big Data Processing and Machine/Deep Learning Middleware on Modern HPC Clusters
- Charting the PMIx Roadmap
- Interconnect Your Future with Mellanox “Smart” Interconnect
- Why Is MPI So Slow? Analyzing the Fundamental Limits in Implementing MPI-3.1
Oak Ridge National Laboratory
- 2nd International Workshop on Post Moore’s Era Supercomputing (PMES)
- 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems
- Application Porting and Optimization on GPU-Accelerated POWER Architectures
- Best Practices for Architecting Performance and Capacity in the Burst Buffer Era
- Eighth Annual Workshop for the Energy Efficient HPC Working Group (EE HPC WG)
- Exascale Challenges and Opportunities
- Fourth SC Workshop on Best Practices for HPC Training
- HPC Education: Meeting of the SIGHPC Education Chapter
- Interactivity in Supercomputing
- Machine Learning in HPC Environments
- OpenACC API User Experience, Vendor Reaction, Relevance, and Roadmap
- Post Moore Supercomputing
- Regression Testing and Monitoring Tools
- Scalable HPC Visualization and Data Analysis Using VisIt
- Scientific User Behavior and Data-Sharing Trends in a Petascale File System
- Software Engineering and Reuse in Computational Science and Engineering
- Software Engineers: Careers in Research
- The 2nd International Workshop on Data Reduction for Big Scientific Data (DRBSD-2)
- TOP500 - Past, Present, Future
- Total Cost of Ownership and HPC System Procurement
Lawrence Livermore National Laboratory
- 4th International Workshop on HPC User Support Tools (HUST-17)
- Eighth Annual Workshop for the Energy Efficient HPC Working Group (EE HPC WG)
- Machine Learning in HPC Environments
- Modeling and Simulation of Communication in HPC Systems
- P79: Porting the Opacity Client Library to a CPU-GPU Cluster Using OpenMP 4.5
- P82: Performance Evaluation of the NVIDIA Tesla P100: Our Directive-Based Partitioning and Pipelining vs. NVIDIA’s Unified Memory
- P94: Fully Hierarchical Scheduling: Paving the Way to Exascale Workloads
- Performance Modeling under Resource Constraints Using Deep Transfer Learning
- Power-Aware High Performance Computing: Challenges and Opportunities for Application and System Developers
- The Green500: Trends in Energy-Efficient Supercomputing
- Using HPC to Impact US Manufacturing through the HPC4Mfg Program